Abstract
Almost all industries and businesses make purchases; they usually have more than one possible supplier; in most cases, they have many possible suppliers, since suppliers have different prices, policies, different levels of quality. Making wise decisions may save money, which has an impact on the final price of products, and therefore, on the company’s competitiveness. The problem can be solved using mathematical models to represent the cost of the inventory management, and finally, applying techniques to minimize the cost. Mathematical models of the inventory management problem may be complex and NP-hard, and as a result, evaluating all possible solutions to find the cheapest one may be unfeasible, even with a computer. When that happens, metaheuristic algorithms may be used to find a reasonable solution in a reasonable amount of time. This chapter deals with this topic. This chapter presents an example problem, in order to illustrate the complexity of the inventory management and how a large number of possible solutions may arise, then it solves the problem with traditional and metaheuristic algorithms to demonstrate the advantages of metaheuristic algorithms. Finally, other problems are discussed, and further information about algorithms is provided.
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References
Mendoza A, Ventura JA (2013) Modeling actual transportation costs in supplier selection and order quantity allocation decisions. Oper Res Int J 13(1):5–25
Alejo-Reyes A, Mendoza A, Olivares-Benitez E (2019) Inventory replenishment decisions model for the supplier selection problem facing low perfect rate situations. Optim Lett. https://doi.org/10.1007/s11590-019-01510-0
Alejo-Reyes A, Olivares-Benitez E, Mendoza A, odriguez A (2020) Inventory replenishment decision model for the supplier selection problem using metaheuristic algorithms. Math Biosci Eng 17:2016–2036. https://doi.org/10.3934/mbe.2020107
Bowersox DJ, Closs DJ (1996) Logistical management: the integrated supply chain process. McGraw-Hill, New York, NY
Yang X-S (2010) Engineering optimization. Wiley, Hoboken
Barr BS, Golden BL, Kelly JP, Resende MGC, Stewart WR (1995) Designing and reporting on computational experiments with heuristic methods. J Heuristics 46:9–32
Talbi E-G (2009) Metaheuristics: from design to implementation. Wiley, Hoboken, pp 54–67
Eberhart R, Kennedy J (1995) A new optimizer using particle swarm theory. In: Proceedings of the sixth international symposium on micro machine and human science, pp 39–43
Sampson JR (1975) Adaptation in natural and artificial systems (John H. Holland). The University of Michigan Press, Ann Arbor
Price KV, Storn RM, Lampinen JA (2005) The differential evolution algorithm. Differential evolution: a practical approach to global optimization, pp 37–134
Glover F, Laguna M (1998) Tabu search. In: Handbook of combinatorial optimization. Springer, Boston, pp 2093–2229
Gendreau M, Potvin JY (2019) Handbook of metaheuristics. Operations research and management science. Springer, Berlin
Kirkpatrick S, Gellat C, Vecchi P (1983) Optimization by simulated annealing. Science 220(4598):671–680
Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61. ISSN 0965-9978. https://doi.org/10.1016/j.advengsoft.2013.12.007
Meindl SCP (2016) Supply chain management: Strategy, planning, and operations. Tsinghua University Press, Beijing
Verma R, Pullman ME (1998) An analysis of the supplier selection process. Omega 26:739–50
Boer LD, Labro E, Morlacchi P (2001) A review of methods supporting supplier selection. Eur J Purch Supply Manag 7:75–89
Zhang D, Zhang J, Lai K, Lu Y (2009) An novel approach to supplier selection based on vague sets group decision. Expert Syst Appl 36:9557–9563
Harris FW (1913) How many parts to make at once. Mag Manag 10:135–152
Kundu A, Guchhait P, Pramanik P, Maiti MK, Maiti M (2016) A production inventory model with price discounted fuzzy demand using an interval compared hybrid algorithm. Swarm Evolut Comput. https://doi.org/10.1016/j.swevo.2016.11.004
Glock CH, Grosse EH, Ries JM (2014) The lot sizing problem: a tertiary study. Int J Prod Econ 155:39–51. ISSN 0925-5273. https://doi.org/10.1016/j.ijpe.2013.12.009
Nair A, Jayaram J, Das A (2015) Strategic purchasing participation, supplier selection, supplier evaluation and purchasing performance. Int J Prod Res 53(20):6263–6278. https://doi.org/10.1080/00207543.2015.1047983
Mafakheri F, Breton M, Ghoniem A (2011) Supplier selection-order allocation: a two-stage multiple criteria dynamic programming approach. Int J Prod Econ 132(1):52–57. ISSN 0925-5273. https://doi.org/10.1016/j.ijpe.2011.03.005
Chang C-T, Chen H-M, Zhuang Z-Y (2014) Integrated multi-choice goal programming and multi-segment goal programming for supplier selection considering imperfect-quality and price-quantity discounts in a multiple sourcing environment. Int J Syst Sci 45(5):1101–1111. https://doi.org/10.1080/00207721.2012.745024
Batuhan M, Huseyin A, Kilic S (2015) A two stage approach for supplier selection problem in multi-item/multi-supplier environment with quantity discounts. Comput Indus Eng 85:1–12. ISSN 0360-8352. https://doi.org/10.1016/j.cie.2015.02.026
Choudhary D, Shankar R (2011) Modeling and analysis of single item multi-period procurement lot-sizing problem considering rejections and late deliveries. Comput Indus Eng 61(4):1318–1323. ISSN 0360-8352. https://doi.org/10.1016/j.cie.2011.08.005
Mohammad Ebrahim R, Razmi J, Haleh H (2009) Scatter search algorithm for supplier selection and order lot sizing under multiple price discount environment. Adv Eng Soft 40(9):766–776. ISSN 0965-9978. https://doi.org/10.1016/j.advengsoft.2009.02.003
Brahimi N, Dauzere-Peres S, Najid NM, Nordli A (2006) Single item lot sizing problems. Eur J Oper Res 168(1):1–16. ISSN 0377-2217. https://doi.org/10.1016/j.ejor.2004.01.054
Chen S, Feng Y, Kumar A, Lin B (2008) An algorithm for single-item economic lot-sizing problem with general inventory cost, non-decreasing capacity, and non-increasing setup and production cost. Oper Res Lett 36(3):300–302. https://doi.org/10.1016/j.orl.2007.09.005
Massahian Tafti MP, Godichaud M, Amodeo L (2019) Models for the single product disassembly lot sizing problem with disposal 52(13):547–552. https://doi.org/10.1016/j.ifacol.2019.11.215
Ghaniabadi M, Mazinani A (2017) Dynamic lot sizing with multiple suppliers, backlogging and quantity discounts. Comput Ind Eng 110:67–74. https://doi.org/10.1016/j.cie.2017.05.031
Haksever C, Moussourakis J (2008) Determining order quantities in multi-product inventory systems subject to multiple constraints and incremental discounts. Eur J Oper Res 184(3):930–945. https://doi.org/10.1016/j.ejor.2006.12.019
Bohner C, Minner S (2017) Supplier selection under failure risk, quantity and business volume discounts. Comput Ind Eng 104:145–155. https://doi.org/10.1016/j.cie.2016.11.028
Mahdavi Mazdeh M, Emadikhiav M, Parsa I (2015) A heuristic to solve the dynamic lot sizing problem with supplier selection and quantity discounts. Comput Indus Eng 85:33–43. https://doi.org/10.1016/j.cie.2015.02.027
Fordyce JM, Webster FM (1984) The Wagner, Whitin algorithm made simple. Product Invent Manage 25(2):21–30
Lee AH, Kang H-Y, Lai C-M, Hong W-Y (2013) An integrated model for lot sizing with supplier selection and quantity discounts. Appl Math Model 37(7):4733–4746. https://doi.org/10.1016/j.apm.2012.09.056
Alfares H, Turnadi R (2018) Lot sizing and supplier selection with multiple items, multiple periods, quantity discounts, and backordering. Comput Ind Eng 116:59–71
Silver EA, Meal HC (1973) A heuristic for selecting lot size quantities for the case of a deterministic time-varying demand rate and discrete opportunities for replenishment. Product Invent Manage 14(2):64–74
Eydi A, Fazli L (2016) Asia-Pac J Oper Res 33(06)
Mousavi SM, Bahreininejad A, Musa SN, Yusof F (2017) A modified particle swarm optimization for solving the integrated location and inventory control problems in a two-echelon supply chain network. J Intell Manuf 28(1):191–206
Li Y, Ding K, Wang L, Zheng W, Peng Z, Guo S (2018) An optimizing model for solving outsourcing supplier selecting problem based on particle swarm algorithm. J Ind Prod Eng 35(8):526–534
Kang H, Lee AHI, Wu C, Lee C (2017) An efficient method for dynamic-demand joint replenishment problem with multiple suppliers and multiple vehicles. Int J Prod Res 55(4):1065–1084
Wang Y, Geng X, Zhang F, Ruan J (2018) An immune genetic algorithm for multi-Echelon inventory cost control of IOT based supply chains. IEEE Access 6:8547–8555
Xiong F, Gong P, Jin P, Fan JF (2018) Supply chain scheduling optimization based on genetic particle swarm optimization algorithm. Cluster Comput 1–9
Fallahpour A, Olugu EU, Musa SN, Khezrimotlagh D, Wong KY (2016) An integrated model for green supplier selection under fuzzy environment: application of data envelopment analysis and genetic programming approach. Neural Comput Appl 27:707–725
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Cuevas, E., Rodríguez, A., Alejo-Reyes, A., Del-Valle-Soto, C. (2021). Metaheuristic Algorithms Applied to the Inventory Problem. In: Recent Metaheuristic Computation Schemes in Engineering. Studies in Computational Intelligence, vol 948. Springer, Cham. https://doi.org/10.1007/978-3-030-66007-9_8
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