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Artificial intelligence for decision support systems in the field of operations research: review and future scope of research

  • S.I.: Artificial Intelligence in Operations Management
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Abstract

Operations research (OR) has been at the core of decision making since World War II, and today, business interactions on different platforms have changed business dynamics, introducing a high degree of uncertainty. To have a sustainable vision of their business, firms need to have a suitable decision-making process at each stage, including minute details. Our study reviews and investigates the existing research in the field of decision support systems (DSSs) and how artificial intelligence (AI) capabilities have been integrated into OR. The findings of our review show how AI has contributed to decision making in the operations research field. This review presents synergies, differences, and overlaps in AI, DSSs, and OR. Furthermore, a clarification of the literature based on the approaches adopted to develop the DSS is presented along with the underlying theories. The classification has been primarily divided into two categories, i.e. theory building and application-based approaches, along with taxonomies based on the AI, DSS, and OR areas. In this review, past studies were calibrated according to prognostic capability, exploitation of large data sets, number of factors considered, development of learning capability, and validation in the decision-making framework. This paper presents gaps and future research opportunities concerning prediction and learning, decision making and optimization in view of intelligent decision making in today’s era of uncertainty. The theoretical and managerial implications are set forth in the discussion section justifying the research questions.

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References

  • Aboytes-Ojeda, M., Castillo-Villar, K. K., & Eksioglu, S. D. (2019). Modeling and optimization of biomass quality variability for decision support systems in biomass supply chains. Annals of Operations Research. https://doi.org/10.1007/s10479-019-03477-8.

    Article  Google Scholar 

  • Adams, J. S. (1963). Towards an understanding of inequity. The Journal of Abnormal and Social Psychology, 67(5), 422.

    Google Scholar 

  • Agerri, R., & Rigau, G. (2019). Language independent sequence labelling for opinion target extraction. Artificial Intelligence, 268, 85–95.

    Google Scholar 

  • Agnihothri, S., Sivasubramaniam, N., & Simmons, D. (2002). Leveraging technology to improve field service. International Journal of Service Industry Management, 13(1), 47–68.

    Google Scholar 

  • Ahn, B. S., Cho, S. S., & Kim, C. Y. (2000). The integrated methodology of rough set theory and artificial neural network for business failure prediction. Expert Systems with Applications, 18(2), 65–74.

    Google Scholar 

  • Akay, B., & Karaboga, D. (2012). Artificial bee colony algorithm for large-scale problems and engineering design optimization. Journal of Intelligent Manufacturing, 23(4), 1001–1014.

    Google Scholar 

  • Akter, S., & Wamba, S. F. (2019). Big data and disaster management: A systematic review and agenda for future research. Annals of Operations Research, 283(1–2), 939–959.

    Google Scholar 

  • Akter, S., Wamba, S. F., Gunasekaran, A., Dubey, R., & Childe, S. J. (2016). How to improve firm performance using big data analytics capability and business strategy alignment? International Journal of Production Economics, 182, 113–131.

    Google Scholar 

  • Alfandari, L., Lemalade, J. L., Nagih, A., & Plateau, G. (2011). A MIP flow model for crop-rotation planning in a context of forest sustainable development. Annals of Operations Research, 190(1), 149–164.

    Google Scholar 

  • Altay, N., & Green, W. G., III. (2006). OR/MS research in disaster operations management. European Journal of Operational Research, 175(1), 475–493.

    Google Scholar 

  • Alter, S. L. (1980). Decision support systems: Current practices and continuing challenges. Boston: Addison-Wesley.

    Google Scholar 

  • Alvim, L. G. M., & Milidiú, R. L. (2013). Trading team composition for the intraday multistock market. Decision Support Systems, 54(2), 838–845. https://doi.org/10.1016/j.dss.2012.09.009.

    Article  Google Scholar 

  • Ambrosini, V., & Bowman, C. (2009). What are dynamic capabilities and are they a useful construct in strategic management? International Journal of Management Reviews, 11(1), 29–49.

    Google Scholar 

  • Aparicio-Ruiz, P., Barbadilla-Martín, E., Guadix, J., & Cortés, P. (2019). KNN and adaptive comfort applied in decision making for HVAC systems. Annals of Operations Research. https://doi.org/10.1007/s10479-019-03489-4.

    Article  Google Scholar 

  • Aranha, C., Azevedo, C. R. B., & Iba, H. (2012). Money in trees: How memes, trees, and isolation can optimize financial portfolios. Information Sciences, 182(1), 184–198. https://doi.org/10.1016/j.ins.2011.05.023.

    Article  Google Scholar 

  • Aringhieri, R., Carello, G., & Morale, D. (2016). Supporting decision making to improve the performance of an Italian Emergency Medical Service. Annals of Operations Research, 236(1), 131–148.

    Google Scholar 

  • Armstrong, C. S., Larcker, D. F., & Su, C.-L. (2010). Endogenous selection and moral hazard in compensation contracts. Operations Research, 58(4 PART 2), 1090–1106. https://doi.org/10.1287/opre.1100.0828.

    Article  Google Scholar 

  • Arnott, D., & Pervan, G. (2005). A critical analysis of decision support systems research. Journal of Information Technology, 20(2), 67–87.

    Google Scholar 

  • Askarzadeh, A., & Rezazadeh, A. (2013). Artificial bee swarm optimization algorithm for parameters identification of solar cell models. Applied Energy, 102, 943–949.

    Google Scholar 

  • Avanzi, B., Taylor, G., & Wong, B. (2016). Correlations between insurance lines of business: An illusion or a real phenomenon? Some methodological considerations. Astin Bulletin, 46(2), 225–263.

    Google Scholar 

  • Ayesta, U., Erausquin, M., Ferreira, E., & Jacko, P. (2016). Optimal dynamic resource allocation to prevent defaults. Operations Research Letters, 44(4), 451–456. https://doi.org/10.1016/j.orl.2016.04.008.

    Article  Google Scholar 

  • Azadivar, F., Truong, T., & Jiao, Y. (2009). A decision support system for fisheries management using operations research and systems science approach. Expert Systems with Applications, 36(2), 2971–2978.

    Google Scholar 

  • Babazadeh, A., Poorzahedy, H., & Nikoosokhan, S. (2011). Application of particle swarm optimization to transportation network design problem. Journal of King Saud University-Science, 23(3), 293–300.

    Google Scholar 

  • Baesens, B., Mues, C., Martens, D., & Vanthienen, J. (2009). 50 years of data mining and OR: Upcoming trends and challenges. Journal of the Operational Research Society, 60(SUPPL. 1), S16–S23. https://doi.org/10.1057/jors.2008.171.

    Article  Google Scholar 

  • Bakhrankova, K. (2010). Decision support system for continuous production. Industrial Management & Data Systems, 110(4), 591–610.

    Google Scholar 

  • Ball, M. O., & Datta, A. (1997). Managing operations research models for decision support systems applications in a database environment. Annals of Operations Research, 72, 151–182.

    Google Scholar 

  • Ballouki, I., Douimi, M., & Ouzizi, L. (2017). Decision support tool for supply chain configuration considering new product re-design: An agent-based approach. Journal of Advanced Manufacturing Systems, 16(4), 291–315. https://doi.org/10.1142/S0219686717500184.

    Article  Google Scholar 

  • Barney, J. B. (2001). Resource-based theories of competitive advantage: A ten-year retrospective on the resource-based view. Journal of Management, 27(6), 643–650.

    Google Scholar 

  • Baryannis, G., Validi, S., Dani, S., & Antoniou, G. (2019). Supply chain risk management and artificial intelligence: state of the art and future research directions. International Journal of Production Research, 57(7), 2179–2202.

    Google Scholar 

  • Bennett, C. C., & Hauser, K. (2013). Artificial intelligence framework for simulating clinical decision-making: A Markov decision process approach. Artificial Intelligence in Medicine, 57(1), 9–19.

    Google Scholar 

  • Bera, K., Schalper, K. A., Rimm, D. L., Velcheti, V., & Madabhushi, A. (2019). Artificial intelligence in digital pathology—New tools for diagnosis and precision oncology. Nature Reviews Clinical Oncology, 16(11), 703–715.

    Google Scholar 

  • Beşikçi, E. B., Arslan, O., Turan, O., & Ölçer, A. I. (2016). An artificial neural network based decision support system for energy efficient ship operations. Computers & Operations Research, 66, 393–401.

    Google Scholar 

  • Bhattacharya, S., Xu, D., & Kumar, K. (2011). An ANN-based auditor decision support system using Benford’s law. Decision Support Systems, 50(3), 576–584. https://doi.org/10.1016/j.dss.2010.08.011.

    Article  Google Scholar 

  • Bhimani, A., & Willcocks, L. (2014). Digitization, ‘Big Data’ and the transformation of accounting information. Accounting and Business Research, 44(4), 469–490.

    Google Scholar 

  • Bielli, M., & Reverberi, P. (1996). New operations research and artificial intelligence approaches to traffic engineering problems. European Journal of Operational Research, 92(3), 550–572.

    Google Scholar 

  • Binder, M., & Edwards, J. S. (2010). Using grounded theory method for theory building in operations management research. International Journal of Operations & Production Management, 30(3), 232–259.

    Google Scholar 

  • Bose, I., & Mahapatra, R. K. (2001). Business data mining—A machine learning perspective. Information & Management, 39(3), 211–225.

    Google Scholar 

  • Botta, A., De Donato, W., Persico, V., & Pescapé, A. (2016). Integration of cloud computing and internet of things: A survey. Future Generation Computer Systems, 56, 684–700.

    Google Scholar 

  • Boyd, D., & Crawford, K. (2012). Critical questions for big data: Provocations for a cultural, technological, and scholarly phenomenon. Information, Communication & Society, 15(5), 662–679.

    Google Scholar 

  • Boyer, K. K., Hallowell, R., & Roth, A. V. (2002). E-services: Operating strategy—A case study and a method for analyzing operational benefits. Journal of Operations Management, 20(2), 175–188.

    Google Scholar 

  • Brasileiro, R. C., Souza, V. L. F., & Oliveira, A. L. I. (2017). Automatic trading method based on piecewise aggregate approximation and multi-swarm of improved self-adaptive particle swarm optimization with validation. Decision Support Systems, 104, 79–91. https://doi.org/10.1016/j.dss.2017.10.005.

    Article  Google Scholar 

  • Brown, D. E., & White, C. C., III (Eds.). (2012). Operations research and artificial intelligence: The integration of problem-solving strategies. New York: Springer.

    Google Scholar 

  • Brynjolfsson, E., & Mcafee, A. (2017). The business of Artificial Intelligence: What it can-and cannot-do for your organization. Harvard Business Review, 3–11.

  • Brynjolfsson, E., & McElheran, K. (2016). The rapid adoption of data-driven decision-making. American Economic Review, 106(5), 133–139.

    Google Scholar 

  • Burgard, W., Cremers, A. B., Fox, D., Hähnel, D., Lakemeyer, G., Schulz, D., et al. (1999). Experiences with an interactive museum tour-guide robot. Artificial Intelligence, 114(1–2), 3–55.

    Google Scholar 

  • Can, B., & Heavey, C. (2012). A comparison of genetic programming and artificial neural networks in metamodeling of discrete-event simulation models. Computers & Operations Research, 39(2), 424–436. https://doi.org/10.1016/j.cor.2011.05.004.

    Article  Google Scholar 

  • Cao, Q., & Parry, M. E. (2009). Neural network earnings per share forecasting models: A comparison of backward propagation and the genetic algorithm. Decision Support Systems, 47(1), 32–41. https://doi.org/10.1016/j.dss.2008.12.011.

    Article  Google Scholar 

  • Carbonneau, R. A., Kersten, G. E., & Vahidov, R. M. (2011). Pairwise issue modeling for negotiation counteroffer prediction using neural networks. Decision Support Systems, 50(2), 449–459. https://doi.org/10.1016/j.dss.2010.11.002.

    Article  Google Scholar 

  • Carton, F., Hynes, T., & Adam, F. (2016). A business value oriented approach to decision support systems. Journal of Decision Systems, 25(sup1), 85–95.

    Google Scholar 

  • Cebeci, U. (2009). Fuzzy AHP-based decision support system for selecting ERP systems in textile industry by using balanced scorecard. Expert Systems with Applications, 36(5), 8900–8909.

    Google Scholar 

  • Challen, R., Denny, J., Pitt, M., Gompels, L., Edwards, T., & Tsaneva-Atanasova, K. (2019). Artificial intelligence, bias and clinical safety. BMJ Quality & Safety, 28(3), 231–237.

    Google Scholar 

  • Chan, C. Y. (2017). Advancements, prospects, and impacts of automated driving systems. International Journal of Transportation Science and Technology, 6(3), 208–216.

    Google Scholar 

  • Chan, H. K., He, H., & Wang, W. Y. (2012). Green marketing and its impact on supply chain management in industrial markets. Industrial Marketing Management, 41(4), 557–562.

    Google Scholar 

  • Chase, R. B., & Apte, U. M. (2007). A history of research in service operations: What’s the big idea? Journal of Operations Management, 25(2), 375–386.

    Google Scholar 

  • Checkland, P. (1981). Systems thinking, systems practice. New York, NY: Wiley.

    Google Scholar 

  • Chen, Y., Argentinis, J. E., & Weber, G. (2016a). IBM Watson: How cognitive computing can be applied to big data challenges in life sciences research. Clinical Therapeutics, 38(4), 688–701.

    Google Scholar 

  • Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business intelligence and analytics: From big data to big impact. MIS Quarterly, 1165–1188.

  • Chen, L., Li, X., Yang, Y., Kurniawati, H., Sheng, Q. Z., Hu, H.-Y., et al. (2016b). Personal health indexing based on medical examinations: A data mining approach. Decision Support Systems, 81, 54–65. https://doi.org/10.1016/j.dss.2015.10.008.

    Article  Google Scholar 

  • Chen, Y., & Wang, X. (2014). A hybrid stock trading system using genetic network programming and mean conditional value-at-risk. European Journal of Operational Research, 240(3), 861–871. https://doi.org/10.1016/j.ejor.2014.07.034.

    Article  Google Scholar 

  • Cheng, T. E., & Janiak, A. (2000). A permutation flow-shop scheduling problem with convex models of operation processing times. Annals of Operations Research, 96(1–4), 39–60.

    Google Scholar 

  • Chi, H.-M., Moskowitz, H., Ersoy, O. K., Altinkemer, K., Gavin, P. F., Huff, B. E., et al. (2009). Machine learning and genetic algorithms in pharmaceutical development and manufacturing processes. Decision Support Systems, 48(1), 69–80. https://doi.org/10.1016/j.dss.2009.06.010.

    Article  Google Scholar 

  • Chien, C. F., Dauzère-Pérès, S., Huh, W. T., Jang, Y. J., & Morrison, J. R. (2020). Artificial intelligence in manufacturing and logistics systems: Algorithms, applications, and case studies. International Journal of Production Research, 58(9), 2730–2731.

    Google Scholar 

  • Choi, Y., Lee, H., & Irani, Z. (2018). Big data-driven fuzzy cognitive map for prioritizing IT service procurement in the public sector. Annals of Operations Research, 270(1–2), 75–104.

    Google Scholar 

  • Chou, Y. C., & Benjamin, C. O. (1992). An AI-based decision support system for naval ship design. Naval Engineers Journal, 104(3), 156–165.

    Google Scholar 

  • Chung, C. C., Lee, S. H., Beamish, P. W., Southam, C., & Nam, D. D. (2013). Pitting real options theory against risk diversification theory: International diversification and joint ownership control in economic crisis. Journal of World Business, 48(1), 122–136.

    Google Scholar 

  • Combes, C., & Rivat, C. (2008). A modelling environment based on data warehousing to manage and to optimize the running of international company. International Journal of Production Economics, 112(1), 294–308. https://doi.org/10.1016/j.ijpe.2006.12.065.

    Article  Google Scholar 

  • Conejo, A. J., Carrión, M., & Morales, J. M. (2010). Decision making under uncertainty in electricity markets (Vol. 1). New York: Springer.

    Google Scholar 

  • Cowling, P. (2003). A flexible decision support system for steel hot rolling mill scheduling. Computers & Industrial Engineering, 45(2), 307–321.

    Google Scholar 

  • Crainic, T. G. (2000). Service network design in freight transportation. European Journal of Operational Research, 122(2), 272–288.

    Google Scholar 

  • Crainic, T. G., Gendreau, M., & Potvin, J. Y. (2009). Intelligent freight-transportation systems: Assessment and the contribution of operations research. Transportation Research Part C: Emerging Technologies, 17(6), 541–557.

    Google Scholar 

  • Czajkowski, M., Czerwonka, M., & Kretowski, M. (2015). Cost-sensitive global model trees applied to loan charge-off forecasting. Decision Support Systems, 74, 57–66. https://doi.org/10.1016/j.dss.2015.03.009.

    Article  Google Scholar 

  • D’Urso, P., Massari, R., De Giovanni, L., & Cappelli, C. (2017). Exponential distance-based fuzzy clustering for interval-valued data. Fuzzy Optimization and Decision Making, 16(1), 51–70.

    Google Scholar 

  • Dahal, K., Almejalli, K., & Hossain, M. A. (2013). Decision support for coordinated road traffic control actions. Decision Support Systems, 54(2), 962–975. https://doi.org/10.1016/j.dss.2012.10.022.

    Article  Google Scholar 

  • Davis, J., Mengersen, K., Bennett, S., & Mazerolle, L. (2014). Viewing systematic reviews and meta-analysis in social research through different lenses. SpringerPlus, 3(1), 511.

    Google Scholar 

  • De Boer, L., Labro, E., & Morlacchi, P. (2001). A review of methods supporting supplier selection. European Journal of Purchasing & Supply Management, 7(2), 75–89.

    Google Scholar 

  • de Oliveira, M. J. F., & Toscano, L. N. P. (2018). An integrated emergency care delivery system for major events. Operations Research for Health Care, 17, 16–27.

    Google Scholar 

  • de Sousa Jabbour, A. B. L., Jabbour, C. J. C., Godinho Filho, M., & Roubaud, D. (2018). Industry 4.0 and the circular economy: a proposed research agenda and original roadmap for sustainable operations. Annals of Operations Research, 270(1–2), 273–286.

    Google Scholar 

  • Derntl, M. (2014). Basics of research paper writing and publishing. International Journal of Technology Enhanced Learning, 6(2), 105–123.

    Google Scholar 

  • Desanctis, G., & Gallupe, R. B. (1987). A foundation for the study of group decision support systems. Management Science, 33(5), 589–609.

    Google Scholar 

  • Dey, T., Phillips, D. J., & Steele, P. (2011). A graphical tool to visualize predicted minimum delay flights. Journal of Computational and Graphical Statistics, 20(2), 294–297. https://doi.org/10.1198/jcgs.2011.5de.

    Article  Google Scholar 

  • D’Haen, J., Van Den Poel, D., Thorleuchter, D., & Benoit, D. F. (2016). Integrating expert knowledge and multilingual web crawling data in a lead qualification system. Decision Support Systems, 82, 69–78. https://doi.org/10.1016/j.dss.2015.12.002.

    Article  Google Scholar 

  • Dixon, H. E., & Ginsberg, M. L. (2000). Combining satisfiability techniques from AI and OR. The Knowledge Engineering Review, 15(1), 31–45.

    Google Scholar 

  • Dixon, M. V., Karniouchina, E., van der Rhee, B., Verma, R., & Victorino, L. (2014). The role of coordinated marketing-operations strategy in services: Implications for managerial decisions and execution. Journal of Service Management, 25(2), 275–294.

    Google Scholar 

  • Doerner, K., Gutjahr, W. J., Hartl, R. F., Strauss, C., & Stummer, C. (2004). Pareto ant colony optimization: A metaheuristic approach to multi objective portfolio selection. Annals of Operations Research, 131(1–4), 79–99.

    Google Scholar 

  • Duan, Y., Edwards, J. S., & Dwivedi, Y. K. (2019). Artificial intelligence for decision making in the era of Big Data—Evolution, challenges and research agenda. International Journal of Information Management, 48, 63–71.

    Google Scholar 

  • Dubey, R., Gunasekaran, A., Childe, S. J., Bryde, D. J., Giannakis, M., Foropon, C., et al. (2019). Big data analytics and artificial intelligence pathway to operational performance under the effects of entrepreneurial orientation and environmental dynamism: A study of manufacturing organizations. International Journal of Production Economics. https://doi.org/10.1016/j.ijpe.2019.107599.

    Article  Google Scholar 

  • Dubey, R., Gunasekaran, A., Childe, S. J., Papadopoulos, T., & Fosso Wamba, S. (2017). World class sustainable supply chain management: Critical review and further research directions. The International Journal of Logistics Management, 28(2), 332–362.

    Google Scholar 

  • Dubois, D., Fargier, H., & Prade, H. (1996). Refinements of the maxi-min approach to decision-making in a fuzzy environment. Fuzzy Sets and Systems, 81(1), 103–122.

    Google Scholar 

  • Dutta, A., & Basu, A. (1984). An artificial intelligence approach to model management in decision support systems. Computer, 9, 89–97.

    Google Scholar 

  • Dyer, J. H., & Singh, H. (1998). The relational view: Cooperative strategy and sources of interorganizational competitive advantage. Academy of Management Review, 23(4), 660–679.

    Google Scholar 

  • Efendigil, T., Önüt, S., & Kahraman, C. (2009). A decision support system for demand forecasting with artificial neural networks and neuro-fuzzy models: A comparative analysis. Expert Systems with Applications, 36(3), 6697–6707.

    Google Scholar 

  • Eisenhardt, K. M. (1989a). Agency theory: An assessment and review. Academy of Management Review, 14(1), 57–74.

    Google Scholar 

  • Eisenhardt, K. M. (1989b). Making fast strategic decisions in high-velocity environments. Academy of Management Journal, 32(3), 543–576.

    Google Scholar 

  • Elstein, A. S., & Schwarz, A. (2002). Clinical problem solving and diagnostic decision making: Selective review of the cognitive literature. BMJ, 324(7339), 729–732.

    Google Scholar 

  • Eryarsoy, E., Koehler, G. J., & Aytug, H. (2009). Using domain-specific knowledge in generalization error bounds for support vector machine learning. Decision Support Systems, 46(2), 481–491. https://doi.org/10.1016/j.dss.2008.09.001.

    Article  Google Scholar 

  • Fan, W., Liu, J., Zhu, S., & Pardalos, P. M. (2018). Investigating the impacting factors for the healthcare professionals to adopt artificial intelligence-based medical diagnosis support system (AIMDSS). Annals of Operations Research. https://doi.org/10.1007/s10479-018-2818-y.

    Article  Google Scholar 

  • Fan, C., Zhang, C., Yahja, A., & Mostafavi, A. (2019). Disaster City Digital Twin: A vision for integrating artificial and human intelligence for disaster management. International Journal of Information Management. https://doi.org/10.1016/j.ijinfomgt.2019.102049.

    Article  Google Scholar 

  • Ferreira, K. J., Lee, B. H. A., & Simchi-Levi, D. (2016). Analytics for an online retailer: Demand forecasting and price optimization. Manufacturing and Service Operations Management, 18(1), 69–88. https://doi.org/10.1287/msom.2015.0561.

    Article  Google Scholar 

  • Fethi, M. D., & Pasiouras, F. (2010). Assessing bank efficiency and performance with operational research and artificial intelligence techniques: A survey. European Journal of Operational Research, 204(2), 189–198.

    Google Scholar 

  • Fortun, M., & Schweber, S. S. (1993). Scientists and the legacy of World War II: The case of operations research (OR). Social Studies of Science, 23(4), 595–642.

    Google Scholar 

  • Gayathri, R., & Uma, V. (2018). Ontology based knowledge representation technique, domain modeling languages and planners for robotic path planning: A survey. ICT Express, 4(2), 69–74.

    Google Scholar 

  • Geoffrion, A. M., & Krishnan, R. (2001). Prospects for operations research in the e-business era. Interfaces, 31(2), 6–36.

    Google Scholar 

  • Ghodsypour, S. H., & O’Brien, C. (1998). A decision support system for supplier selection using an integrated analytic hierarchy process and linear programming. International Journal of Production Economics, 56, 199–212.

    Google Scholar 

  • Giboney, J. S., Brown, S. A., Lowry, P. B., & Nunamaker, J. F., Jr. (2015). User acceptance of knowledge-based system recommendations: Explanations, arguments, and fit. Decision Support Systems, 72, 1–10.

    Google Scholar 

  • Glouberman, S., & Zimmerman, B. (2002). Complicated and complex systems: What would successful reform of Medicare look like? Romanow Papers, 2, 21–53.

    Google Scholar 

  • Gomes, C. P. (2000). Artificial intelligence and operations research: Challenges and opportunities in planning and scheduling. The Knowledge Engineering Review, 15(1), 1–10.

    Google Scholar 

  • Greco, S., Matarazzo, B., Slowinski, R., & Zanakis, S. (2011). Global investing risk: A case study of knowledge assessment via rough sets. Annals of Operations Research, 185(1), 105–138. https://doi.org/10.1007/s10479-009-0542-3.

    Article  Google Scholar 

  • Grzonka, D., Jakobik, A., Kołodziej, J., & Pllana, S. (2018). Using a multi-agent system and artificial intelligence for monitoring and improving the cloud performance and security. Future Generation Computer Systems, 86, 1106–1117.

    Google Scholar 

  • Guillaume, R., Marques, G., Thierry, C., & Dubois, D. (2014). Decision support with ill-known criteria in the collaborative supply chain context. Engineering Applications of Artificial Intelligence, 36, 1–11.

    Google Scholar 

  • Gunasekaran, A., & Kobu, B. (2002). Modelling and analysis of business process reengineering. International Journal of Production Research, 40(11), 2521–2546.

    Google Scholar 

  • Gunasekaran, A., Patel, C., & Tirtiroglu, E. (2001). Performance measures and metrics in a supply chain environment. International Journal of Operations & Production Management, 21(1/2), 71–87.

    Google Scholar 

  • Guner, H. U., Chinnam, R. B., & Murat, A. (2016). Simulation platform for anticipative plant-level maintenance decision support system. International Journal of Production Research, 54(6), 1785–1803.

    Google Scholar 

  • Gupta, S., Altay, N., & Luo, Z. (2017). Big data in humanitarian supply chain management: A review and further research directions. Annals of Operations Research. https://doi.org/10.1007/s10479-017-2671-4.

    Article  Google Scholar 

  • Gupta, S., Modgil, S., & Gunasekaran, A. (2020). Big data in lean six sigma: A review and further research directions. International Journal of Production Research. https://doi.org/10.1080/00207543.2019.1598599.

    Article  Google Scholar 

  • Hadavandi, E., Shavandi, H., & Ghanbari, A. (2011). An improved sales forecasting approach by the integration of genetic fuzzy systems and data clustering: Case study of printed circuit board. Expert Systems with Applications, 38(8), 9392–9399.

    Google Scholar 

  • Hamet, P., & Tremblay, J. (2017). Artificial intelligence in medicine. Metabolism, 69, S36–S40.

    Google Scholar 

  • Hammer, M. (2004). Deep change: How operational innovation can transform your company. Harvard Business Review, 82.

  • Hasan, M. S., Ebrahim, Z., Mahmood, W. H. W., & Ab Rahman, M. N. (2017). Decision support system classification and its application in manufacturing sector: A review. Journal Teknologi, 79(1), 149–163.

    Google Scholar 

  • Hashem, I. A. T., Yaqoob, I., Anuar, N. B., Mokhtar, S., Gani, A., & Khan, S. U. (2015). The rise of “big data” on cloud computing: Review and open research issues. Information Systems, 47, 98–115.

    Google Scholar 

  • Hassabis, D., Kumaran, D., Summerfield, C., & Botvinick, M. (2017). Neuroscience-inspired artificial intelligence. Neuron, 95(2), 245–258.

    Google Scholar 

  • Hayashi, Y. (2016). Application of a rule extraction algorithm family based on the Re-RX algorithm to financial credit risk assessment from a Pareto optimal perspective. Operations Research Perspectives, 3, 32–42. https://doi.org/10.1016/j.orp.2016.08.001.

    Article  Google Scholar 

  • Hermenegildo, M. V. (2012). Conferences versus journals in CS, what to do? Evolutionary ways forward and the ICLP/TPLP model, Position paper for Dagstuhl meeting 12452: Publication Culture in Computing Research.

  • Hervert-Escobar, L., & López-Pérez, J. F. (2018). Production planning and scheduling optimization model: A case of study for a glass container company. Annals of Operations Research. https://doi.org/10.1007/s10479-018-3099-1.

    Article  Google Scholar 

  • Holsapple, C. W. (2008). DSS architecture and types. In F. Burstein & C. W. Holsapple (Eds.), Handbook on decision support systems 1: Basic themes (pp. 163–189). New York: Springer, Berlin Heidelberg.

    Google Scholar 

  • Hosack, B., Hall, D., Paradice, D., & Courtney, J. F. (2012). A look toward the future: Decision support systems research is alive and well. Journal of the Association for Information Systems, 13(5), 315–340.

    Google Scholar 

  • Hu, Y.-C., & Ansell, J. (2009). Retail default prediction by using sequential minimal optimization technique. Journal of Forecasting, 28(8), 651–666. https://doi.org/10.1002/for.1110.

    Article  Google Scholar 

  • Hu, Z.-H., & Sheng, Z.-H. (2015). Disaster spread simulation and rescue time optimization in a resource network. Information Sciences, 298, 118–135. https://doi.org/10.1016/j.ins.2014.12.011.

    Article  Google Scholar 

  • Hu, X., Sun, L., & Liu, L. (2013). A PAM approach to handling disruptions in real-time vehicle routing problems. Decision Support Systems, 54(3), 1380–1393. https://doi.org/10.1016/j.dss.2012.12.014.

    Article  Google Scholar 

  • Humphreys, P., McIvor, R., & Huang, G. (2002). An expert system for evaluating the make or buy decision. Computers & Industrial Engineering, 42(2–4), 567–585.

    Google Scholar 

  • Iansiti, M., & Lakhani, K. R. (2020). Competing in the age of AI. Harvard Business Review, 98(1), 60–67.

    Google Scholar 

  • Ivanov, D., Dolgui, A., & Sokolov, B. (2019). The impact of digital technology and Industry 4.0 on the ripple effect and supply chain risk analytics. International Journal of Production Research, 57(3), 829–846.

    Google Scholar 

  • Jaffar, J., & Maher, M. J. (1994). Constraint logic programming: A survey. The Journal of Logic Programming, 19, 503–581.

    Google Scholar 

  • Jaramillo, P., Smith, R. A., & Andréu, J. (2005). Multi-decision-makers equalizer: A multi objective decision support system for multiple decision-makers. Annals of Operations Research, 138(1), 97–111.

    Google Scholar 

  • Jarrahi, M. H. (2018). Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making. Business Horizons, 61(4), 577–586.

    Google Scholar 

  • Javaid, N., Sher, A., Nasir, H., & Guizani, N. (2018). Intelligence in IoT-based 5G networks: Opportunities and challenges. IEEE Communications Magazine, 56(10), 94–100.

    Google Scholar 

  • Jeon, S. M., & Kim, G. (2016). A survey of simulation modeling techniques in production planning and control (PPC). Production Planning & Control, 27(5), 360–377.

    Google Scholar 

  • Jin, X.-H., & Zhang, G. (2011). Modelling optimal risk allocation in PPP projects using artificial neural networks. International Journal of Project Management, 29(5), 591–603. https://doi.org/10.1016/j.ijproman.2010.07.011.

    Article  Google Scholar 

  • Jordan, M. I., & Mitchell, T. M. (2015). Machine learning: Trends, perspectives, and prospects. Science, 349(6245), 255–260.

    Google Scholar 

  • Kaplan, A., & Haenlein, M. (2018). Siri, Siri, in my hand: Who’s the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence. Business Horizons. https://doi.org/10.1016/j.bushor.2018.08.004.

    Article  Google Scholar 

  • Karacapilidis, N. I., & Pappis, C. P. (1997). A framework for group decision support systems: Combining AI tools and OR techniques. European Journal of Operational Research, 103(2), 373–388.

    Google Scholar 

  • Kasap, N., Turan, H. H., Savran, H., Tektas-Sivrikaya, B., & Delen, D. (2018). Provider selection and task allocation in telecommunications with QoS degradation policy. Annals of Operations Research, 263(1–2), 311–337.

    Google Scholar 

  • Kasie, F. M., Bright, G., & Walker, A. (2017). An intelligent decision support system for on-demand fixture retrieval, adaptation and manufacture. Journal of Manufacturing Technology Management, 28(2), 189–211.

    Google Scholar 

  • Keith, A. J., & Ahner, D. K. (2019). A survey of decision making and optimization under uncertainty. Annals of Operations Research. https://doi.org/10.1007/s10479-019-03431-8.

    Article  Google Scholar 

  • Ketter, W., Collins, J., Gini, M., Gupta, A., & Schrater, P. (2009). Detecting and forecasting economic regimes in multi-agent automated exchanges. Decision Support Systems, 47(4), 307–318. https://doi.org/10.1016/j.dss.2009.05.012.

    Article  Google Scholar 

  • Khalafallah, A., & El-Rayes, K. (2008). Minimizing construction-related security risks during airport expansion projects. Journal of Construction Engineering and Management, 134(1), 40–48. https://doi.org/10.1061/(ASCE)0733-9364(2008)134:1(40).

    Article  Google Scholar 

  • Kim, G. H., An, S. H., & Kang, K. I. (2004). Comparison of construction cost estimating models based on regression analysis, neural networks, and case-based reasoning. Building and Environment, 39(10), 1235–1242.

    Google Scholar 

  • Kim, K. J., & Han, I. (2000). Genetic algorithms approach to feature discretization in artificial neural networks for the prediction of stock price index. Expert Systems with Applications, 19(2), 125–132.

    Google Scholar 

  • Kırlar, B. B., Ergün, S., Gök, S. Z. A., & Weber, G. W. (2018). A game-theoretical and cryptographical approach to crypto-cloud computing and its economical and financial aspects. Annals of Operations Research, 260(1–2), 217–231.

    Google Scholar 

  • Kisilevich, S., Keim, D., & Rokach, L. (2013). A GIS-based decision support system for hotel room rate estimation and temporal price prediction: The hotel brokers’ context. Decision Support Systems, 54(2), 1119–1133. https://doi.org/10.1016/j.dss.2012.10.038.

    Article  Google Scholar 

  • Kleindorfer, P. R., Singhal, K., & Van Wassenhove, L. N. (2005). Sustainable operations management. Production and Operations Management, 14(4), 482–492.

    Google Scholar 

  • Kloör, B., Monhof, M., Beverungen, D., & Braäer, S. (2018). Design and evaluation of a model-driven decision support system for repurposing electric vehicle batteries. European Journal of Information Systems, 27(2), 171–188.

    Google Scholar 

  • Kobbacy, Khairy A. H., & Vadera, S. (2011a). A survey of AI in operations management from 2005 to 2009. Journal of Manufacturing Technology Management, 22(6), 706–733.

    Google Scholar 

  • Kobbacy, K. A., & Vadera, S. (2011b). A survey of AI in operations management from 2005 to 2009. Journal of Manufacturing Technology Management, 22(6), 706–733.

    Google Scholar 

  • Kobbacy, K. A., Vadera, S., & Rasmy, M. H. (2007). AI and OR in management of operations: history and trends. Journal of the Operational Research Society, 58(1), 10–28.

    Google Scholar 

  • Kourou, K., Exarchos, T. P., Exarchos, K. P., Karamouzis, M. V., & Fotiadis, D. I. (2015). Machine learning applications in cancer prognosis and prediction. Computational and Structural Biotechnology Journal, 13, 8–17.

    Google Scholar 

  • Kouziokas, G. N., & Perakis, K. (2017). Decision support system based on artificial intelligence, GIS and remote sensing for sustainable public and judicial management. European Journal of Sustainable Development, 6(3), 397.

    Google Scholar 

  • Kumar, A., Sah, B., Singh, A. R., Deng, Y., He, X., Kumar, P., et al. (2017). A review of multi criteria decision making (MCDM) towards sustainable renewable energy development. Renewable and Sustainable Energy Reviews, 69, 596–609.

    Google Scholar 

  • Kuo, R. J., & Lin, L. M. (2010). Application of a hybrid of genetic algorithm and particle swarm optimization algorithm for order clustering. Decision Support Systems, 49(4), 451–462. https://doi.org/10.1016/j.dss.2010.05.006.

    Article  Google Scholar 

  • Laguna-Salvadó, L., Lauras, M., Okongwu, U., & Comes, T. (2019). A multicriteria master planning DSS for a sustainable humanitarian supply chain. Annals of Operations Research, 283(1), 1303–1343.

    Google Scholar 

  • Lancaster, J., & Cheng, K. (2008). A fitness differential adaptive parameter controlled evolutionary algorithm with application to the design structure matrix. International Journal of Production Research, 46(18), 5043–5057. https://doi.org/10.1080/00207540701324176.

    Article  Google Scholar 

  • Larson, R. C. (1987). OR forum—Perspectives on queues: Social justice and the psychology of queueing. Operations Research, 35(6), 895–905.

    Google Scholar 

  • Lau, H. C. W., Ho, G. T. S., & Zhao, Y. (2013). A demand forecast model using a combination of surrogate data analysis and optimal neural network approach. Decision Support Systems, 54(3), 1404–1416. https://doi.org/10.1016/j.dss.2012.12.008.

    Article  Google Scholar 

  • LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436.

    Google Scholar 

  • Lee, J., Bagheri, B., & Kao, H. A. (2015). A cyber-physical systems architecture for industry 4.0-based manufacturing systems. Manufacturing Letters, 3, 18–23.

    Google Scholar 

  • Lee, K. C., Lee, N., & Lee, H. (2012). Multi-agent knowledge integration mechanism using particle swarm optimization. Technological Forecasting and Social Change, 79(3), 469–484. https://doi.org/10.1016/j.techfore.2011.08.004.

    Article  Google Scholar 

  • Lee, E. K., Maheshwary, S., Mason, J., & Glisson, W. (2006). Decision support system for mass dispensing of medications for infectious disease outbreaks and bioterrorist attacks. Annals of Operations Research, 148(1), 25–53.

    Google Scholar 

  • Lemaignan, S., Warnier, M., Sisbot, E. A., Clodic, A., & Alami, R. (2017). Artificial cognition for social human–robot interaction: An implementation. Artificial Intelligence, 247, 45–69.

    Google Scholar 

  • Lenin, N., Kumar, M. S., Ravindran, D., & Islam, M. N. (2014). A tabu search for multi-objective single row facility layout problem. Journal of Advanced Manufacturing Systems, 13(1), 17–40. https://doi.org/10.1142/S0219686714500024.

    Article  Google Scholar 

  • Li, J. Q., Borenstein, D., & Mirchandani, P. B. (2007). A decision support system for the single-depot vehicle rescheduling problem. Computers & Operations Research, 34(4), 1008–1032.

    Google Scholar 

  • Lieckens, K. T., Colen, P. J., & Lambrecht, M. R. (2015). Network and contract optimization for maintenance services with remanufacturing. Computers & Operations Research, 54, 232–244. https://doi.org/10.1016/j.cor.2014.10.003.

    Article  Google Scholar 

  • Lin, F.-R., Kuo, H.-C., & Lin, S.-M. (2008). The enhancement of solving the distributed constraint satisfaction problem for cooperative supply chains using multi-agent systems. Decision Support Systems, 45(4), 795–810. https://doi.org/10.1016/j.dss.2008.02.001.

    Article  Google Scholar 

  • Lin, H. W., Nagalingam, S. V., Kuik, S. S., & Murata, T. (2012). Design of a global decision support system for a manufacturing SME: Towards participating in collaborative manufacturing. International Journal of Production Economics, 136(1), 1–12.

    Google Scholar 

  • Lipshitz, R., Klein, G., Orasanu, J., & Salas, E. (2001). Taking stock of naturalistic decision making. Journal of Behavioral Decision Making, 14(5), 331–352.

    Google Scholar 

  • Lisboa, P. J., & Taktak, A. F. (2006). The use of artificial neural networks in decision support in cancer: A systematic review. Neural Networks, 19(4), 408–415.

    Google Scholar 

  • Liu, Q., & Van Ryzin, G. (2008). On the choice-based linear programming model for network revenue management. Manufacturing & Service Operations Management, 10(2), 288–310.

    Google Scholar 

  • Liu, Y., Zhang, H., Li, C., & Jiao, R. J. (2012). Workflow simulation for operational decision support using event graph through process mining. Decision Support Systems, 52(3), 685–697. https://doi.org/10.1016/j.dss.2011.11.003.

    Article  Google Scholar 

  • Lui, A., & Lamb, G. W. (2018). Artificial intelligence and augmented intelligence collaboration: Regaining trust and confidence in the financial sector. Information & Communications Technology Law, 27(3), 267–283.

    Google Scholar 

  • Lwin, K. T., Qu, R., & MacCarthy, B. L. (2017). Mean-VaR portfolio optimization: A nonparametric approach. European Journal of Operational Research, 260(2), 751–766. https://doi.org/10.1016/j.ejor.2017.01.005.

    Article  Google Scholar 

  • Marinakos, G., Daskalaki, S., & Ntrinias, T. (2014). Defensive financial decisions support for retailers in Greek pharmaceutical industry. Central European Journal of Operations Research, 22(3), 525–551. https://doi.org/10.1007/s10100-013-0325-4.

    Article  Google Scholar 

  • Mar-Ortiz, J., Castillo-García, N., & Gracia, M. D. (2019). A decision support system for a capacity management problem at a container terminal. International Journal of Production Economics. https://doi.org/10.1016/j.ijpe.2019.09.023.

    Article  Google Scholar 

  • Martens, D., Van Gestel, T., De Backer, M., Haesen, R., Vanthienen, J., & Baesens, B. (2010). Credit rating prediction using ant colony optimization. Journal of the Operational Research Society, 61(4), 561–573. https://doi.org/10.1057/jors.2008.164.

    Article  Google Scholar 

  • Mata, J., De Miguel, I., Duran, R. J., Merayo, N., Singh, S. K., Jukan, A., et al. (2018). Artificial intelligence (AI) methods in optical networks: A comprehensive survey. Optical Switching and Networking, 28, 43–57.

    Google Scholar 

  • Matzler, K., Strobl, A., Thurner, N., & Füller, J. (2015). Switching experience, customer satisfaction, and switching costs in the ICT industry. Journal of Service Management, 26(1), 117–136.

    Google Scholar 

  • Mazhar, F., Khan, A. M., Chaudhry, I. A., & Ahsan, M. (2013). On using neural networks in UAV structural design for CFD data fitting and classification. Aerospace Science and Technology, 30(1), 210–225.

    Google Scholar 

  • Mehlawat, M. K., Kannan, D., Gupta, P., & Aggarwal, U. (2019). Sustainable transportation planning for a three-stage fixed charge multi-objective transportation problem. Annals of Operations Research. https://doi.org/10.1007/s10479-019-03451-4.

    Article  Google Scholar 

  • Meredith, J. (1998). Building operations management theory through case and field research. Journal of Operations Management, 16(4), 441–454.

    Google Scholar 

  • Mes, M., van der Heijden, M., & van Hillegersberg, J. (2008). Design choices for agent-based control of AGVs in the dough making process. Decision Support Systems, 44(4), 983–999. https://doi.org/10.1016/j.dss.2007.11.005.

    Article  Google Scholar 

  • Min, H. (2010). Artificial intelligence in supply chain management: Theory and applications. International Journal of Logistics Research and Applications, 13(1), 13–39.

    Google Scholar 

  • Min, J. H., & Lee, Y. C. (2005). Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters. Expert Systems with Applications, 28(4), 603–614.

    Google Scholar 

  • Miranda, S. M., & Kim, Y. M. (2006). Professional versus political contexts: institutional mitigation and the transaction cost heuristic in information systems outsourcing. Mis Quarterly, 725–753.

  • Mitchell, E. M., & Kovach, J. V. (2016). Improving supply chain information sharing using design for six sigma. European Research on Management and Business Economics, 22(3), 147–154.

    Google Scholar 

  • Moghaddam, M., & Nof, S. Y. (2015). Best-matching with interdependent preferences—Implications for capacitated cluster formation and evolution. Decision Support Systems, 79, 125–137. https://doi.org/10.1016/j.dss.2015.08.005.

    Article  Google Scholar 

  • Montani, S. (2008). Exploring new roles for case-based reasoning in heterogeneous AI systems for medical decision support. Applied Intelligence, 28(3), 275–285.

    Google Scholar 

  • Montes, G. C., Bastos, J. C. A., & de Oliveira, A. J. (2019). Fiscal transparency, government effectiveness and government spending efficiency: Some international evidence based on panel data approach. Economic Modelling, 79, 211–225.

    Google Scholar 

  • Monteserin, A., & Amandi, A. (2011). Argumentation-based negotiation planning for autonomous agents. Decision Support Systems, 51(3), 532–548.

    Google Scholar 

  • Moslemi, H., & Zandieh, M. (2011). Comparisons of some improving strategies on MOPSO for multi-objective (r, Q) inventory system. Expert Systems with Applications, 38(10), 12051–12057.

    Google Scholar 

  • Nagar, K. (2009). Evaluating the effect of consumer sales promotions on brand loyal and brand switching segments. Vision, 13(4), 35–48.

    Google Scholar 

  • Nazemi, A., Fatemi, P. F., Heidenreich, K., & Fabozzi, F. J. (2017). Fuzzy decision fusion approach for loss-given-default modeling. European Journal of Operational Research, 262(2), 780–791. https://doi.org/10.1016/j.ejor.2017.04.008.

    Article  Google Scholar 

  • Nedělková, Z., Lindroth, P., Patriksson, M., & Strömberg, A. B. (2018). Efficient solution of many instances of a simulation-based optimization problem utilizing a partition of the decision space. Annals of Operations Research, 265(1), 93–118.

    Google Scholar 

  • Nemati, H. R., Steiger, D. M., Iyer, L. S., & Herschel, R. T. (2002). Knowledge warehouse: An architectural integration of knowledge management, decision support, artificial intelligence and data warehousing. Decision Support Systems, 33(2), 143–161.

    Google Scholar 

  • Neshat, N., & Amin-Naseri, M. R. (2015). Cleaner power generation through market-driven generation expansion planning: An agent-based hybrid framework of game theory and particle swarm optimization. Journal of Cleaner Production, 105, 206–217. https://doi.org/10.1016/j.jclepro.2014.10.083.

    Article  Google Scholar 

  • Ngai, E. W. T., Peng, S., Alexander, P., & Moon, K. K. (2014). Decision support and intelligent systems in the textile and apparel supply chain: An academic review of research articles. Expert Systems with Applications, 41(1), 81–91.

    Google Scholar 

  • Nowakowski, P., Szwarc, K., & Boryczka, U. (2018). Vehicle route planning in e-waste mobile collection on demand supported by artificial intelligence algorithms. Transportation Research Part D: Transport and Environment, 63, 1–22.

    Google Scholar 

  • Omisore, M. O., Samuel, O. W., & Atajeromavwo, E. J. (2017). A genetic-neuro-fuzzy inferential model for diagnosis of tuberculosis. Applied Computing and Informatics, 13(1), 27–37.

    Google Scholar 

  • Otoiu, A., Titan, E., & Dumitrescu, R. (2014). Are the variables used in building composite indicators of well-being relevant? Validating composite indexes of well-being. Ecological Indicators, 46, 575–585. https://doi.org/10.1016/j.ecolind.2014.07.019.

    Article  Google Scholar 

  • Özdamar, L., Ekinci, E., & Küçükyazici, B. (2004). Emergency logistics planning in natural disasters. Annals of Operations Research, 129(1–4), 217–245.

    Google Scholar 

  • Palmatier, R. W., Houston, M. B., & Hulland, J. (2018). Review articles: Purpose, process, and structure. Journal of the Academy of Marketing Science, 46, 1–5.

    Google Scholar 

  • Paolanti, M., Liciotti, D., Pietrini, R., Mancini, A., & Frontoni, E. (2018). Modelling and forecasting customer navigation in intelligent retail environments. Journal of Intelligent and Robotic Systems, 91(2), 165–180.

    Google Scholar 

  • Peinado, J., Graeml, A. R., & Vianna, F. (2018). Operations management body of knowledge and its relevance to manufacturing and service organizations. Revista de Gestão, 25(4), 373–389.

    Google Scholar 

  • Perraju, T. (2013). Artificial intelligence and decision support systems. International Journal of Advanced Research in IT and Engineering, 2(4), 17–26.

    Google Scholar 

  • Phillips-Wren, G. (2012). AI tools in decision making support systems: A review. International Journal on Artificial Intelligence Tools, 21(02), 1240005-1-13.

    Google Scholar 

  • Phillips-Wren, G., & Ichalkaranje, N. (Eds.). (2008). Intelligent decision making: An AI-based approach (Vol. 97). New York: Springer.

    Google Scholar 

  • Phillips-Wren, G., Mora, M., Forgionne, G. A., & Gupta, J. N. (2009). An integrative evaluation framework for intelligent decision support systems. European Journal of Operational Research, 195(3), 642–652.

    Google Scholar 

  • Poria, S., Cambria, E., Howard, N., Huang, G. B., & Hussain, A. (2016). Fusing audio, visual and textual clues for sentiment analysis from multimodal content. Neurocomputing, 174, 50–59.

    Google Scholar 

  • Power, D. J. (2004). Specifying an expanded framework for classifying and describing decision support systems. Communications of the Association for Information Systems, 13, 158–166.

    Google Scholar 

  • Power, D. J., & Sharda, R. (2007). Model-driven decision support systems: Concepts and research directions. Decision Support Systems, 43(3), 1044–1061.

    Google Scholar 

  • Priem, R. L., & Butler, J. E. (2001). Is the resource-based “view” a useful perspective for strategic management research? Academy of Management Review, 26(1), 22–40.

    Google Scholar 

  • Przybyła-Kasperek, M., & Wakulicz-Deja, A. (2016). The strength of coalition in a dispersed decision support system with negotiations. European Journal of Operational Research, 252(3), 947–968.

    Google Scholar 

  • Pullan, T. T., Bhasi, M., & Madhu, G. (2013). Decision support tool for lean product and process development. Production Planning & Control, 24(6), 449–464.

    Google Scholar 

  • Rajaeian, M. M., Cater-Steel, A., & Lane, M. (2017). A systematic literature review and critical assessment of model-driven decision support for IT outsourcing. Decision Support Systems, 102, 42–56.

    Google Scholar 

  • Ramos, C., Augusto, J. C., & Shapiro, D. (2008). Ambient intelligence—The next step for artificial intelligence. IEEE Intelligent Systems, 23(2), 15–18.

    Google Scholar 

  • Reutterer, T., Hornik, K., March, N., & Gruber, K. (2017). A data mining framework for targeted category promotions. Journal of Business Economics, 87(3), 337–358. https://doi.org/10.1007/s11573-016-0823-7.

    Article  Google Scholar 

  • Román, S., Villegas, A. M., & Villegas, J. G. (2017). An evolutionary strategy for multi objective reinsurance optimization. Journal of the Operational Research Society. https://doi.org/10.1057/s41274-017-0210-y.

    Article  Google Scholar 

  • Romanowski, C., Raj, R., Schneider, J., Mishra, S., Shivshankar, V., Ayengar, S., et al. (2015). Regional response to large-scale emergency events: Building on historical data. International Journal of Critical Infrastructure Protection, 11, 12–21. https://doi.org/10.1016/j.ijcip.2015.07.003.

    Article  Google Scholar 

  • Roy, D., Krishnamurthy, A., Heragu, S., & Malmborg, C. (2015). Stochastic models for unit-load operations in warehouse systems with autonomous vehicles. Annals of Operations Research, 231(1), 129–155.

    Google Scholar 

  • Russell, S., & Norvig, P. (1995). Artificial intelligence: A modern approach. Upper Saddle River, NJ: Prentice-Hall.

    Google Scholar 

  • Rygielski, C., Wang, J. C., & Yen, D. C. (2002). Data mining techniques for customer relationship management. Technology in Society, 24(4), 483–502.

    Google Scholar 

  • Ryman-Tubb, N. F., Krause, P., & Garn, W. (2018). How artificial intelligence and machine learning research impacts payment card fraud detection: A survey and industry benchmark. Engineering Applications of Artificial Intelligence, 76, 130–157.

    Google Scholar 

  • Saaty, T. L. (2013). The modern science of multicriteria decision making and its practical applications: The AHP/ANP approach. Operations Research, 61(5), 1101–1118.

    Google Scholar 

  • Sahebjamnia, N., Torabi, S. A., & Mansouri, S. A. (2017). A hybrid decision support system for managing humanitarian relief chains. Decision Support Systems, 95, 12–26.

    Google Scholar 

  • Samsatli, S., & Samsatli, N. J. (2018). A general mixed integer linear programming model for the design and operation of integrated urban energy systems. Journal of Cleaner Production, 191, 458–479.

    Google Scholar 

  • Saranya, K., Jegaraj, J. J. R., Kumar, K. R., & Rao, G. V. (2018). Artificial intelligence based selection of optimal cutting tool and process parameters for effective turning and milling operations. Journal of the Institution of Engineers (India): Series C, 99(4), 381–392.

    Google Scholar 

  • Scholz, M., Franz, M., & Hinz, O. (2017). Effects of decision space information on MAUT-based systems that support purchase decision processes. Decision Support Systems, 97, 43–57.

    Google Scholar 

  • Scott, J., Ho, W., Dey, P. K., & Talluri, S. (2015). A decision support system for supplier selection and order allocation in stochastic, multi-stakeholder and multi-criteria environments. International Journal of Production Economics, 166, 226–237.

    Google Scholar 

  • Seng-cho, T. C., Hsu, H. J., Yang, C. C., & Lai, F. (1997). A stock selection DSS combining AI and technical analysis. Annals of Operations Research, 75, 335–353.

    Google Scholar 

  • Sforza, A., & Sterle, C. (Eds.). (2017). Optimization and decision science: Methodologies and applications: ODS, Sorrento, Italy (Vol. 217). New York: Springer.

    Google Scholar 

  • Shcherbina, O., & Shembeleva, E. (2014). Modeling recreational systems using optimization techniques and information technologies. Annals of Operations Research, 221(1), 309–329.

    Google Scholar 

  • Shenfield, A., Day, D., & Ayesh, A. (2018). Intelligent intrusion detection systems using artificial neural networks. ICT Express, 4(2), 95–99.

    Google Scholar 

  • Shim, J. P., Warkentin, M., Courtney, J. F., Power, D. J., Sharda, R., & Carlsson, C. (2002). Past, present, and future of decision support technology. Decision Support Systems, 33(2), 111–126.

    Google Scholar 

  • Shortliffe, E. H., & Sepúlveda, M. J. (2018). Clinical decision support in the era of artificial intelligence. JAMA, 320(21), 2199–2200.

    Google Scholar 

  • Silbermayr, L., & Minner, S. (2016). Dual sourcing under disruption risk and cost improvement through learning. European Journal of Operational Research, 250(1), 226–238. https://doi.org/10.1016/j.ejor.2015.09.017.

    Article  Google Scholar 

  • Simeunović, N., Kamenko, I., Bugarski, V., Jovanović, M., & Lalić, B. (2017). Improving workforce scheduling using artificial neural networks model. Advances in Production Engineering and Management, 12(4), 337–352. https://doi.org/10.14743/apem2017.4.262.

    Article  Google Scholar 

  • Simon, H. A. (1969). The sciences of the artificial. Cambridge, MA: Cambridge University Press.

    Google Scholar 

  • Simon, H. A. (1979). Rational decision making in business organizations. The American Economic Review, 69(4), 493–513.

    Google Scholar 

  • Simon, H. A. (1981). The sciences of the artificial. 1969. Massachusetts: Massachusetts Institute of Technology.

    Google Scholar 

  • Singh, A. K., Subramanian, N., Pawar, K. S., & Bai, R. (2018). Cold chain configuration design: Location-allocation decision-making using coordination, value deterioration, and big data approximation. Annals of Operations Research, 270(1–2), 433–457.

    Google Scholar 

  • Sivarajah, U., Kamal, M. M., Irani, Z., & Weerakkody, V. (2017). Critical analysis of big data challenges and analytical methods. Journal of Business Research, 70, 263–286.

    Google Scholar 

  • Skulimowski, A. M. (2011). Future trends of intelligent decision support systems and models. In Future information technology (pp. 11–20). Berlin: Springer.

  • Snyder, H. (2019). Literature review as a research methodology: An overview and guidelines. Journal of Business Research, 104, 333–339.

    Google Scholar 

  • Sousa, M. J., & Wilks, D. (2018). Sustainable skills for the world of work in the digital age. Systems Research and Behavioral Science, 35(4), 399–405.

    Google Scholar 

  • Steenken, D., Voß, S., & Stahlbock, R. (2004). Container terminal operation and operations research—A classification and literature review. OR Spectrum, 26(1), 3–49.

    Google Scholar 

  • Stevanovic, A., Stevanovic, J., & Kergaye, C. (2013). Optimization of traffic signal timings based on surrogate measures of safety. Transportation Research Part C: Emerging Technologies, 32, 159–178. https://doi.org/10.1016/j.trc.2013.02.009.

    Article  Google Scholar 

  • Stone, M., Aravopoulou, E., Gerardi, G., Todeva, E., Weinzierl, L., Laughlin, P., et al. (2017). How platforms are transforming customer information management. The Bottom Line, 30(3), 216–235.

    Google Scholar 

  • Suzuki, K., & Chen, Y. (Eds.). (2018). Artificial intelligence in decision support systems for diagnosis in medical imaging, (Vol. 140). New York: Springer.

    Google Scholar 

  • Svozil, D., Kvasnicka, V., & Pospichal, J. (1997). Introduction to multi-layer feed-forward neural networks. Chemometrics and Intelligent Laboratory Systems, 39(1), 43–62.

    Google Scholar 

  • Takeda, A., & Kanamori, T. (2009). A robust approach based on conditional value-at-risk measure to statistical learning problems. European Journal of Operational Research, 198(1), 287–296. https://doi.org/10.1016/j.ejor.2008.07.027.

    Article  Google Scholar 

  • Taleizadeh, A. A., Tavakoli, S., & San-José, L. A. (2018). A lot sizing model with advance payment and planned backordering. Annals of Operations Research, 271(2), 1001–1022.

    Google Scholar 

  • Tang, A., Tam, R., Cadrin-Chênevert, A., Guest, W., Chong, J., Barfett, J., et al. (2018). Canadian Association of Radiologists white paper on artificial intelligence in radiology. Canadian Association of Radiologists Journal, 69(2), 120–135.

    Google Scholar 

  • Tarter, C. J., & Hoy, W. K. (1998). Toward a contingency theory of decision making. Journal of Educational Administration, 36(3), 212–228.

    Google Scholar 

  • Taylor, C. R. (2019). Artificial intelligence, customized communications, privacy, and the General Data Protection Regulation (GDPR). International Journal of Advertising, 38(5), 649–650.

    Google Scholar 

  • Teece, D. J., Pisano, G., & Shuen, A. (1997). Dynamic capabilities and strategic management. Strategic Management Journal, 18(7), 509–533.

    Google Scholar 

  • Tenfold. (2019). How artificial intelligence will change decision-making for businesses. Accessed 23 August 2019. https://www.tenfold.com/business/artificial-intelligence-business-decisions.

  • Thompson, C., Aitken, L., Doran, D., & Dowding, D. (2013). An agenda for clinical decision making and judgement in nursing research and education. International Journal of Nursing Studies, 50(12), 1720–1726.

    Google Scholar 

  • Topol, E. J. (2019). High-performance medicine: The convergence of human and artificial intelligence. Nature Medicine, 25(1), 44–56.

    Google Scholar 

  • Tranfield, D., Denyer, D., & Smart, P. (2003). Towards a methodology for developing evidence-informed management knowledge by means of systematic review. British Journal of Management, 14(3), 207–222.

    Google Scholar 

  • Turing, A. M. (2009). Computing machinery and intelligence. In Parsing the turing test (pp. 23–65). Dordrecht: Springer.

  • Udías, A., Efremov, R., Galbiati, L., & Cañamón, I. (2014). Simulation and multicriteria optimization modeling approach for regional water restoration management. Annals of Operations Research, 219(1), 123–140. https://doi.org/10.1007/s10479-012-1101-x.

    Article  Google Scholar 

  • Uriarte, A. G., Zúñiga, E. R., Moris, M. U., & Ng, A. H. (2017). How can decision makers be supported in the improvement of an emergency department? A simulation, optimization and data mining approach. Operations Research for Health Care, 15, 102–122.

    Google Scholar 

  • Van Der Zee, D.-J., Holkenborg, B., & Robinson, S. (2012). Conceptual modeling for simulation-based serious gaming. Decision Support Systems, 54(1), 33–45. https://doi.org/10.1016/j.dss.2012.03.006.

    Article  Google Scholar 

  • Vandermerwe, S., & Rada, J. (1988). Servitization of business: Adding value by adding services. European Management Journal, 6(4), 314–324.

    Google Scholar 

  • Van Hee, K. M., & Lapinski, A. (1988). OR and AI approaches to decision support systems. Decision Support Systems, 4(4), 447–459.

    Google Scholar 

  • Vlachos, D., Georgiadis, P., & Iakovou, E. (2007). A system dynamics model for dynamic capacity planning of remanufacturing in closed-loop supply chains. Computers & Operations Research, 34(2), 367–394.

    Google Scholar 

  • Von Neumann, J., Morgenstern, O., & Kuhn, H. W. (1944). Theory of games and economic behavior. Princeton, NJ: Princeton University Press.

    Google Scholar 

  • Wamba, S. F. (2020). Humanitarian supply chain: A bibliometric analysis and future research directions. Annals of Operations Research. https://doi.org/10.1007/s10479-020-03594-9.

    Article  Google Scholar 

  • Wamba, S. F., Akter, S., Edwards, A., Chopin, G., & Gnanzou, D. (2015). How ‘big data’can make big impact: Findings from a systematic review and a longitudinal case study. International Journal of Production Economics, 165, 234–246.

    Google Scholar 

  • Wamba, S. F., Dubey, R., Gunasekaran, A., & Akter, S. (2019a). The performance effects of big data analytics and supply chain ambidexterity: The moderating effect of environmental dynamism. International Journal of Production Economics. https://doi.org/10.1016/j.ijpe.2019.09.019.

    Article  Google Scholar 

  • Wamba, S. F., Edwards, A., & Akter, S. (2019b). Social media adoption and use for improved emergency services operations: The case of the NSW SES. Annals of Operations Research, 283(1–2), 225–245.

    Google Scholar 

  • Wamba, S. F., Gunasekaran, A., Akter, S., Ren, S. J. F., Dubey, R., & Childe, S. J. (2017). Big data analytics and firm performance: Effects of dynamic capabilities. Journal of Business Research, 70, 356–365.

    Google Scholar 

  • Wamba, S. F., Gunasekaran, A., Dubey, R., & Ngai, E. W. (2018). Big data analytics in operations and supply chain management. Annals of Operations Research, 270(1–2), 1–4.

    Google Scholar 

  • Wang, H. (2005). Flexible flow shop scheduling: optimum, heuristics and artificial intelligence solutions. Expert Systems, 22(2), 78–85.

    Google Scholar 

  • Wang, Y., Andoh-Baidoo, F. K., & Sun, J. (2014). Security investment in aviation industry: A longitudinal analysis. Industrial Management and Data Systems, 114(2), 276–291. https://doi.org/10.1108/IMDS-04-2013-0176.

    Article  Google Scholar 

  • Wang, N., Liang, H., Jia, Y., Ge, S., Xue, Y., & Wang, Z. (2016). Cloud computing research in the IS discipline: A citation/co-citation analysis. Decision Support Systems, 86, 35–47.

    Google Scholar 

  • Warner, K. S., & Wäger, M. (2019). Building dynamic capabilities for digital transformation: An ongoing process of strategic renewal. Long Range Planning, 52(3), 326–349.

    Google Scholar 

  • Wassertheurer, S., Mayer, C., & Breitenecker, F. (2008). Modeling arterial and left ventricular coupling for non-invasive measurements. Simulation Modelling Practice and Theory, 16(8), 988–997. https://doi.org/10.1016/j.simpat.2008.04.016.

    Article  Google Scholar 

  • Will, M., Bertrand, J., & Fransoo, J. C. (2002). Operations management research methodologies using quantitative modeling. International Journal of Operations & Production Management, 22(2), 241–264.

    Google Scholar 

  • Williamson, O. E. (1979). Transaction-cost economics: the governance of contractual relations. The Journal of Law and Economics, 22(2), 233–261.

    Google Scholar 

  • Willis, K. O., & Jones, D. F. (2008). Multi-objective simulation optimization through search heuristics and relational database analysis. Decision Support Systems, 46(1), 277–286. https://doi.org/10.1016/j.dss.2008.06.012.

    Article  Google Scholar 

  • Wixom, B. H., & Watson, H. J. (2001). An empirical investigation of the factors affecting data warehousing success. MIS Quarterly, 17–41.

  • Wright, S. A., & Schultz, A. E. (2018). The rising tide of artificial intelligence and business automation: Developing an ethical framework. Business Horizons, 61(6), 823–832.

    Google Scholar 

  • Xidonas, P., Mavrotas, G., Zopounidis, C., & Psarras, J. (2011). IPSSIS: An integrated multicriteria decision support system for equity portfolio construction and selection. European Journal of Operational Research, 210(2), 398–409.

    Google Scholar 

  • Xu, Q., Liu, X., Jiang, C., & Yu, K. (2016). Nonparametric conditional autoregressive expectile model via neural network with applications to estimating financial risk. Applied Stochastic Models in Business and Industry, 32(6), 882–908. https://doi.org/10.1002/asmb.2212.

    Article  Google Scholar 

  • Xu, Y., Sahnoun, M. H., Ben Abdelaziz, F., & Baudry, D. (2020). A simulated multi-objective model for flexible job shop transportation scheduling. Annals of Operations Research. https://doi.org/10.1007/s10479-020-03600-0.

    Article  Google Scholar 

  • Yam, R. C. M., Tse, P. W., Li, L., & Tu, P. (2001). Intelligent predictive decision support system for condition-based maintenance. The International Journal of Advanced Manufacturing Technology, 17(5), 383–391.

    Google Scholar 

  • Yan, D., Zhou, Q., Wang, J., & Zhang, N. (2017). Bayesian regularization neural network based on artificial intelligence optimization. International Journal of Production Research, 55(8), 2266–2287.

    Google Scholar 

  • Yang, J.-G., Kim, J.-K., Kang, U.-G., & Lee, Y.-H. (2014). Coronary heart disease optimization system on adaptive-network-based fuzzy inference system and linear discriminant analysis (ANFIS-LDA). Personal and Ubiquitous Computing, 18(6), 1351–1362. https://doi.org/10.1007/s00779-013-0737-0.

    Article  Google Scholar 

  • Yang, D. H., Kim, S., Nam, C., & Min, J. W. (2007). Developing a decision model for business process outsourcing. Computers & Operations Research, 34(12), 3769–3778.

    Google Scholar 

  • Yang, R., Lee, C. Y., Liu, Q., & Zheng, S. (2019). A carrier–shipper contract under asymmetric information in the ocean transport industry. Annals of Operations Research, 273(1–2), 377–408.

    Google Scholar 

  • Yang, C. C., Wei, C.-P., & Li, K. W. (2008). Cross-lingual thesaurus for multilingual knowledge management. Decision Support Systems, 45(3), 596–605. https://doi.org/10.1016/j.dss.2007.07.005.

    Article  Google Scholar 

  • Yang, C.-S., Wei, C.-P., Yuan, C.-C., & Schoung, J.-Y. (2010). Predicting the length of hospital stay of burn patients: Comparisons of prediction accuracy among different clinical stages. Decision Support Systems, 50(1), 325–335. https://doi.org/10.1016/j.dss.2010.09.001.

    Article  Google Scholar 

  • Yolcu, U., Egrioglu, E., & Aladag, C. H. (2013). A new linear & nonlinear artificial neural network model for time series forecasting. Decision Support Systems, 54(3), 1340–1347. https://doi.org/10.1016/j.dss.2012.12.006.

    Article  Google Scholar 

  • Yu, K. H., Beam, A. L., & Kohane, I. S. (2018). Artificial intelligence in healthcare. Nature Biomedical Engineering, 2(10), 719–731.

    Google Scholar 

  • Yu, L., Wang, S., & Lai, K. K. (2008). Neural network-based mean-variance-skewness model for portfolio selection. Computers & Operations Research, 35(1), 34–46. https://doi.org/10.1016/j.cor.2006.02.012.

    Article  Google Scholar 

  • Zeng, Z., Di Maio, F., Zio, E., & Kang, R. (2017). A hierarchical decision-making framework for the assessment of the prediction capability of prognostic methods. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, 231(1), 36–52.

    Google Scholar 

  • Zeng, Q., Sun, S. X., Duan, H., Liu, C., & Wang, H. (2013). Cross-organizational collaborative workflow mining from a multi-source log. Decision Support Systems, 54(3), 1280–1301. https://doi.org/10.1016/j.dss.2012.12.001.

    Article  Google Scholar 

  • Zhang, H. (2012). Analysis of a dynamic adverse selection model with asymptotic efficiency. Mathematics of Operations Research, 37(3), 450–474. https://doi.org/10.1287/moor.1120.0541.

    Article  Google Scholar 

  • Zhang, J., Avasarala, V., & Subbu, R. (2010). Evolutionary optimization of transition probability matrices for credit decision-making. European Journal of Operational Research, 200(2), 557–567. https://doi.org/10.1016/j.ejor.2009.01.020.

    Article  Google Scholar 

  • Zhang, Z., Gao, G., & Shi, Y. (2014). Credit risk evaluation using multi-criteria optimization classifier with kernel, fuzzification and penalty factors. European Journal of Operational Research, 237(1), 335–348. https://doi.org/10.1016/j.ejor.2014.01.044.

    Article  Google Scholar 

  • Zhang, K., Leng, S., Peng, X., Pan, L., Maharjan, S., & Zhang, Y. (2018a). Artificial intelligence inspired transmission scheduling in cognitive vehicular communications and networks. IEEE Internet of Things Journal, 6(2), 1987–1997.

    Google Scholar 

  • Zhang, X., Wang, Y., Liu, C., & Chen, Z. (2018b). A novel approach of battery pack state of health estimation using artificial intelligence optimization algorithm. Journal of Power Sources, 376, 191–199.

    Google Scholar 

  • Zigurs, I., & Buckland, B. K. (1998). A theory of task/technology fit and group support systems effectiveness. MIS Quarterly, 313–334.

  • Zou, Y., Kiviniemi, A., & Jones, S. W. (2016). Developing a tailored RBS linking to BIM for risk management of bridge projects. Engineering, Construction and Architectural Management, 23(6), 727–750. https://doi.org/10.1108/ECAM-01-2016-0009.

    Article  Google Scholar 

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Acknowledgement

This study was supported by the Category II research grant received from the Indian Institute of Management Calcutta with work order number 3557/RP:ATBOCOMSUDM.

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Appendices

Appendix A

See Table 5.

Table 5 Year- wise number of publications with regards to journal title.

Appendix B

Classification of literature. Source: Author(s) Compilation.

figure a

Appendix C

See Table 6.

Table 6 Top 10 institutions with regards to the number of journal papers

Appendix D: Five categories of approaches adopted

See Tables 7, 8, 9, 10 and 11.

Table 7 Network-based approaches to DSS
Table 8 Agent-based approaches to DSS
Table 9 Genetic algorithm-based approaches to DSS
Table 10 Hybrid and mathematical approaches to DSS
Table 11 Fuzzy logic-based approaches to DSS

Appendix E: Papers used in this study for review

  1. 1.

    Alvim L.G.M., Milidiú R.L. (2013). Trading team composition for the intraday multistock market. Decision Support Systems, 54(2), 838–845. https://doi.org/10.1016/j.dss.2012.09.009

  2. 2.

    Aranha C., Azevedo C.R.B., Iba H. (2012). Money in trees: How memes, trees, and isolation can optimize financial portfolios. Information Sciences, 182(1), 184–198. https://doi.org/10.1016/j.ins.2011.05.023

  3. 3.

    Armstrong C.S., Larcker D.F., Su C.-L. (2010). Endogenous selection and moral hazard in compensation contracts. Operations Research, 58(4 PART 2), 1090–1106. https://doi.org/10.1287/opre.1100.0828

  4. 4.

    Ayesta U., Erausquin M., Ferreira E., Jacko P. (2016). Optimal dynamic resource allocation to prevent defaults. Operations Research Letters, 44(4), 451–456. https://doi.org/10.1016/j.orl.2016.04.008

  5. 5.

    Baesens B., Mues C., Martens D., Vanthienen J. (2009). 50 years of data mining and OR: Upcoming trends and challenges. Journal of the Operational Research Society, 60(SUPPL. 1), S16–S23. https://doi.org/10.1057/jors.2008.171

  6. 6.

    Ballouki I., Douimi M., Ouzizi L. (2017). Decision support tool for supply chain configuration considering new product re-design: An agent-based approach. Journal of Advanced Manufacturing Systems, 16(4), 291–315. https://doi.org/10.1142/s0219686717500184

  7. 7.

    Bhattacharya S., Xu D., Kumar K. (2011). An ANN-based auditor decision support system using Benford’s law. Decision Support Systems, 50(3), 576–584. https://doi.org/10.1016/j.dss.2010.08.011

  8. 8.

    Brasileiro R.C., Souza V.L.F., Oliveira A.L.I. (2017). Automatic trading method based on piecewise aggregate approximation and multi-swarm of improved self-adaptive particle swarm optimization with validation. Decision Support Systems, 104, 79–91. https://doi.org/10.1016/j.dss.2017.10.005

  9. 9.

    Can B., Heavey C. (2012). A comparison of genetic programming and artificial neural networks in metamodeling of discrete-event simulation models. Computers and Operations Research, 39(2), 424–436. https://doi.org/10.1016/j.cor.2011.05.004

  10. 10.

    Cao Q., Parry M.E. (2009). Neural network earnings per share forecasting models: A comparison of backward propagation and the genetic algorithm. Decision Support Systems, 47(1), 32–41. https://doi.org/10.1016/j.dss.2008.12.011

  11. 11.

    Carbonneau R.A., Kersten G.E., Vahidov R.M. (2011). Pairwise issue modeling for negotiation counteroffer prediction using neural networks. Decision Support Systems, 50(2), 449–459. https://doi.org/10.1016/j.dss.2010.11.002

  12. 12.

    Chen L., Li X., Yang Y., Kurniawati H., Sheng Q.Z., Hu H.-Y., Huang N. (2016). Personal health indexing based on medical examinations: A data mining approach. Decision Support Systems, 81, 54–65. https://doi.org/10.1016/j.dss.2015.10.008

  13. 13.

    Chen Y., Wang X. (2014). A hybrid stock trading system using genetic network programming and mean conditional value-at-risk. European Journal of Operational Research, 240(3), 861–871. https://doi.org/10.1016/j.ejor.2014.07.034

  14. 14.

    Chi H.-M., Moskowitz H., Ersoy O.K., Altinkemer K., Gavin P.F., Huff B.E., Olsen B.A. (2009). Machine learning and genetic algorithms in pharmaceutical development and manufacturing processes. Decision Support Systems, 48(1), 69–80. https://doi.org/10.1016/j.dss.2009.06.010

  15. 15.

    Combes C., Rivat C. (2008). A modelling environment based on data warehousing to manage and to optimize the running of international company. International Journal of Production Economics, 112(1), 294–308. https://doi.org/10.1016/j.ijpe.2006.12.065

  16. 16.

    Czajkowski M., Czerwonka M., Kretowski M. (2015). Cost-sensitive Global Model Trees applied to loan charge-off forecasting. Decision Support Systems, 74, 57–66. https://doi.org/10.1016/j.dss.2015.03.009

  17. 17.

    Dahal K., Almejalli K., Hossain M.A. (2013). Decision support for coordinated road traffic control actions. Decision Support Systems, 54(2), 962–975. https://doi.org/10.1016/j.dss.2012.10.022

  18. 18.

    Dey T., Phillips D.J., Steele P. (2011). A graphical tool to Visualize predicted minimum delay Flights. Journal of Computational and Graphical Statistics, 20(2), 294–297. https://doi.org/10.1198/jcgs.2011.5de

  19. 19.

    D’Haen J., Van Den Poel D., Thorleuchter D., Benoit D.F. (2016). Integrating expert knowledge and multilingual web crawling data in a lead qualification system. Decision Support Systems, 82, 69–78. https://doi.org/10.1016/j.dss.2015.12.002

  20. 20.

    Eryarsoy E., Koehler G.J., Aytug H. (2009). Using domain-specific knowledge in generalization error bounds for support vector machine learning. Decision Support Systems, 46(2), 481–491. https://doi.org/10.1016/j.dss.2008.09.001

  21. 21.

    Ferreira K.J., Lee B.H.A., Simchi-Levi D. (2016). Analytics for an online retailer: Demand forecasting and price optimization. Manufacturing and Service Operations Management, 18(1), 69–88. https://doi.org/10.1287/msom.2015.0561

  22. 22.

    Greco S., Matarazzo B., Slowinski R., Zanakis S. (2011). Global investing risk: A case study of knowledge assessment via rough sets. Annals of Operations Research, 185(1), 105–138. https://doi.org/10.1007/s10479-009-0542-3

  23. 23.

    Hayashi Y. (2016). Application of a rule extraction algorithm family based on the Re-RX algorithm to financial credit risk assessment from a Pareto optimal perspective. Operations Research Perspectives, 3, 32–42. https://doi.org/10.1016/j.orp.2016.08.001

  24. 24.

    Hu X., Sun L., Liu L. (2013). A PAM approach to handling disruptions in real-time vehicle routing problems. Decision Support Systems, 54(3), 1380–1393. https://doi.org/10.1016/j.dss.2012.12.014

  25. 25.

    Hu Y.-C., Ansell J. (2009). Retail default prediction by using sequential minimal optimization technique. Journal of Forecasting, 28(8), 651–666. https://doi.org/10.1002/for.1110

  26. 26.

    Hu Z.-H., Sheng Z.-H. (2015). Disaster spread simulation and rescue time optimization in a resource network. Information Sciences, 298(), 118–135. https://doi.org/10.1016/j.ins.2014.12.011

  27. 27.

    Jin X.-H., Zhang G. (2011). Modelling optimal risk allocation in PPP projects using artificial neural networks. International Journal of Project Management, 29(5), 591–603. https://doi.org/10.1016/j.ijproman.2010.07.011

  28. 28.

    Ketter W., Collins J., Gini M., Gupta A., Schrater P. (2009). Detecting and forecasting economic regimes in multi-agent automated exchanges. Decision Support Systems, 47(4), 307–318. https://doi.org/10.1016/j.dss.2009.05.012

  29. 29.

    Khalafallah A., El-Rayes K. (2008). Minimizing construction-related security risks during airport expansion projects. Journal of Construction Engineering and Management, 134(1), 40–48. https://doi.org/10.1061/(asce)0733-9364(2008)134:1(40)

  30. 30.

    Kisilevich S., Keim D., Rokach L. (2013). A GIS-based decision support system for hotel room rate estimation and temporal price prediction: The hotel brokers’ context. Decision Support Systems, 54(2), 1119–1133. https://doi.org/10.1016/j.dss.2012.10.038

  31. 31.

    Kuo R.J., Lin L.M. (2010). Application of a hybrid of genetic algorithm and particle swarm optimization algorithm for order clustering. Decision Support Systems, 49(4), 451–462. https://doi.org/10.1016/j.dss.2010.05.006

  32. 32.

    Lancaster J., Cheng K. (2008). A fitness differential adaptive parameter controlled evolutionary algorithm with application to the design structure matrix. International Journal of Production Research, 46(18), 5043–5057. https://doi.org/10.1080/00207540701324176

  33. 33.

    Lau H.C.W., Ho G.T.S., Zhao Y. (2013). A demand forecast model using a combination of surrogate data analysis and optimal neural network approach. Decision Support Systems, 54(3), 1404–1416. https://doi.org/10.1016/j.dss.2012.12.008

  34. 34.

    Lee K.C., Lee N., Lee H. (2012). Multi-agent knowledge integration mechanism using particle swarm optimization. Technological Forecasting and Social Change, 79(3), 469–484. https://doi.org/10.1016/j.techfore.2011.08.004

  35. 35.

    Lenin N., Kumar M.S., Ravindran D., Islam M.N. (2014). A tabu search for multi-objective single row facility layout problem. Journal of Advanced Manufacturing Systems, 13(1), 17–40. https://doi.org/10.1142/s0219686714500024

  36. 36.

    Lieckens K.T., Colen P.J., Lambrecht M.R. (2015). Network and contract optimization for maintenance services with remanufacturing. Computers and Operations Research, 54, 232–244. https://doi.org/10.1016/j.cor.2014.10.003

  37. 37.

    Lin F.-r., Kuo H.-c., Lin S.-m. (2008). The enhancement of solving the distributed constraint satisfaction problem for cooperative supply chains using multi-agent systems. Decision Support Systems, 45(4), 795–810. https://doi.org/10.1016/j.dss.2008.02.001

  38. 38.

    Liu Y., Zhang H., Li C., Jiao R.J. (2012). Workflow simulation for operational decision support using event graph through process mining. Decision Support Systems, 52(3), 685–697. https://doi.org/10.1016/j.dss.2011.11.003

  39. 39.

    Lwin K.T., Qu R., MacCarthy B.L. (2017). Mean-VaR portfolio optimization: A nonparametric approach. European Journal of Operational Research, 260(2), 751–766. https://doi.org/10.1016/j.ejor.2017.01.005

  40. 40.

    Marinakos G., Daskalaki S., Ntrinias T. (2014). Defensive financial decisions support for retailers in Greek pharmaceutical industry. Central European Journal of Operations Research, 22(3), 525–551. https://doi.org/10.1007/s10100-013-0325-4

  41. 41.

    Martens D., Van Gestel T., De Backer M., Haesen R., Vanthienen J., Baesens B. (2010). Credit rating prediction using Ant Colony Optimization. Journal of the Operational Research Society, 61(4), 561–573. https://doi.org/10.1057/jors.2008.164

  42. 42.

    Mes M., van der Heijden M., van Hillegersberg J. (2008). Design choices for agent-based control of AGVs in the dough making process. Decision Support Systems, 44(4), 983–999. https://doi.org/10.1016/j.dss.2007.11.005

  43. 43.

    Moghaddam M., Nof S.Y. (2015). Best-matching with interdependent preferences–implications for capacitated cluster formation and evolution. Decision Support Systems, 79, 125–137. https://doi.org/10.1016/j.dss.2015.08.005

  44. 44.

    Nazemi A., Fatemi Pour F., Heidenreich K., Fabozzi F.J. (2017). Fuzzy decision fusion approach for loss-given-default modeling. European Journal of Operational Research, 262(2), 780–791. https://doi.org/10.1016/j.ejor.2017.04.008

  45. 45.

    Neshat N., Amin-Naseri M.R. (2015). Cleaner power generation through market-driven generation expansion planning: An agent-based hybrid framework of game theory and Particle Swarm Optimization. Journal of Cleaner Production, 105, 206–217. https://doi.org/10.1016/j.jclepro.2014.10.083

  46. 46.

    Otoiu A., Titan E., Dumitrescu R. (2014). Are the variables used in building composite indicators of well-being relevant? Validating composite indexes of well-being. Ecological Indicators, 46, 575–585. https://doi.org/10.1016/j.ecolind.2014.07.019

  47. 47.

    Reutterer T., Hornik K., March N., Gruber K. (2017). A data mining framework for targeted category promotions. Journal of Business Economics, 87(3), 337–358. https://doi.org/10.1007/s11573-016-0823-7

  48. 48.

    Román S., Villegas A.M., Villegas J.G. (2017). An evolutionary strategy for multiobjective reinsurance optimization. Journal of the Operational Research Society. https://doi.org/10.1057/s41274-017-0210-y

  49. 49.

    Romanowski C., Raj R., Schneider J., Mishra S., Shivshankar V., Ayengar S., Cueva F. (2015). Regional response to large-scale emergency events: Building on historical data. International Journal of Critical Infrastructure Protection, 11, 12–21. https://doi.org/10.1016/j.ijcip.2015.07.003

  50. 50.

    Silbermayr L., Minner S. (2016). Dual sourcing under disruption risk and cost improvement through learning. European Journal of Operational Research, 250(1), 226–238. https://doi.org/10.1016/j.ejor.2015.09.017

  51. 51.

    Simeunović N., Kamenko I., Bugarski V., Jovanović M., Lalić B. (2017). Improving workforce scheduling using artificial neural networks model. Advances in Production Engineering and Management, 12(4), 337–352. https://doi.org/10.14743/apem2017.4.262

  52. 52.

    Stevanovic A., Stevanovic J., Kergaye C. (2013). Optimization of traffic signal timings based on surrogate measures of safety. Transportation Research Part C: Emerging Technologies, 32, 159–178. https://doi.org/10.1016/j.trc.2013.02.009

  53. 53.

    Takeda A., Kanamori T. (2009). A robust approach based on conditional value-at-risk measure to statistical learning problems. European Journal of Operational Research, 198(1), 287–296. https://doi.org/10.1016/j.ejor.2008.07.027

  54. 54.

    Udías A., Efremov R., Galbiati L., Cañamón I. (2014). Simulation and multicriteria optimization modeling approach for regional water restoration management. Annals of Operations Research, 219(1), 123–140. https://doi.org/10.1007/s10479-012-1101-x

  55. 55.

    Van Der Zee D.-J., Holkenborg B., Robinson S. (2012). Conceptual modeling for simulation-based serious gaming. Decision Support Systems, 54(1), 33–45. https://doi.org/10.1016/j.dss.2012.03.006

  56. 56.

    Wang Y., Andoh-Baidoo F.K., Sun J. (2014). Security investment in aviation industry: A longitudinal analysis. Industrial Management and Data Systems, 114(2), 276–291. https://doi.org/10.1108/imds-04-2013-0176

  57. 57.

    Wassertheurer S., Mayer C., Breitenecker F. (2008). Modeling arterial and left ventricular coupling for non-invasive measurements. Simulation Modelling Practice and Theory, 16(8), 988–997. https://doi.org/10.1016/j.simpat.2008.04.016

  58. 58.

    Willis K.O., Jones D.F. (2008). Multi-objective simulation optimization through search heuristics and relational database analysis. Decision Support Systems, 46(1), 277–286. https://doi.org/10.1016/j.dss.2008.06.012

  59. 59.

    Xu Q., Liu X., Jiang C., Yu K. (2016). Nonparametric conditional autoregressive expectile model via neural network with applications to estimating financial risk. Applied Stochastic Models in Business and Industry, 32(6), 882–908. https://doi.org/10.1002/asmb.2212

  60. 60.

    Yang C.C., Wei C.-P., Li K.W. (2008). Cross-lingual thesaurus for multilingual knowledge management. Decision Support Systems, 45(3), 596–605. https://doi.org/10.1016/j.dss.2007.07.005

  61. 61.

    Yang C.-S., Wei C.-P., Yuan C.-C., Schoung J.-Y. (2010). Predicting the length of hospital stay of burn patients: Comparisons of prediction accuracy among different clinical stages. Decision Support Systems, 50(1), 325–335. https://doi.org/10.1016/j.dss.2010.09.001

  62. 62.

    Yang J.-G., Kim J.-K., Kang U.-G., Lee Y.-H. (2014). Coronary heart disease optimization system on adaptive-network-based fuzzy inference system and linear discriminant analysis (ANFIS-LDA). Personal and Ubiquitous Computing, 18(6), 1351–1362. https://doi.org/10.1007/s00779-013-0737-0

  63. 63.

    Yolcu U., Egrioglu E., Aladag C.H. (2013). A new linear & nonlinear artificial neural network model for time series forecasting. Decision Support Systems, 54(3), 1340–1347. https://doi.org/10.1016/j.dss.2012.12.006

  64. 64.

    Yu L., Wang S., Lai K.K. (2008). Neural network-based mean–variance-skewness model for portfolio selection. Computers and Operations Research, 35(1), 34–46. https://doi.org/10.1016/j.cor.2006.02.012

  65. 65.

    Zeng Q., Sun S.X., Duan H., Liu C., Wang H. (2013). Cross-organizational collaborative workflow mining from a multi-source log. Decision Support Systems, 54(3), 1280–1301. https://doi.org/10.1016/j.dss.2012.12.001

  66. 66.

    Zhang H. (2012). Analysis of a dynamic adverse selection model with asymptotic efficiency. Mathematics of Operations Research, 37(3), 450–474. https://doi.org/10.1287/moor.1120.0541

  67. 67.

    Zhang J., Avasarala V., Subbu R. (2010). Evolutionary optimization of transition probability matrices for credit decision-making. European Journal of Operational Research, 200(2), 557–567. https://doi.org/10.1016/j.ejor.2009.01.020

  68. 68.

    Zhang Z., Gao G., Shi Y. (2014). Credit risk evaluation using multi-criteria optimization classifier with kernel, fuzzification and penalty factors. European Journal of Operational Research, 237(1), 335–348. https://doi.org/10.1016/j.ejor.2014.01.044

  69. 69.

    Zou Y., Kiviniemi A., Jones S.W. (2016). Developing a tailored RBS linking to BIM for risk management of bridge projects. Engineering, Construction and Architectural Management, 23(6), 727–750. https://doi.org/10.1108/ecam-01-2016-0009

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Gupta, S., Modgil, S., Bhattacharyya, S. et al. Artificial intelligence for decision support systems in the field of operations research: review and future scope of research. Ann Oper Res 308, 215–274 (2022). https://doi.org/10.1007/s10479-020-03856-6

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