Novel Pareto-based meta-heuristics for solving multi-objective multi-item capacitated lot-sizing problems
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Capacitated production lot-sizing problems (LSPs) are challenging problems to solve due to their combinatorial nature. We consider a multi-item capacitated LSP with setup times, safety stocks, and demand shortages plus lost sales and backorder considerations for various production methods (i.e., job shop, batch flow, or continuous flow among others). We use multi-objective mathematical programming to solve this problem with three conflicting objectives including: (i) minimizing the total production costs; (ii) leveling the production volume in different production periods; and (iii) producing a solution which is as close as possible to the just-in-time level. We also consider lost sales, backorders, safety stocks, storage space limitation, and capacity constraints. We propose two novel Pareto-based multi-objective meta-heuristic algorithms: multi-objective vibration damping optimization (MOVDO) and a multi-objective harmony search algorithm (MOHSA). We compare MOVDO and MOHSA with two well-known evolutionary algorithms called the non-dominated sorting genetic algorithm (NSGA-II) and multi-objective simulated annealing (MOSA) to demonstrate the efficiency and effectiveness of the proposed methods.
KeywordsLot-sizing Computational Intelligence Multi-objective optimization Harmony search Vibration damping optimization MOSA NSGA-II
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- 9.Silver E, Mael H (1973) A heuristic selecting lot size requirements for the case of deterministic time varying demand rate and discrete opportunities for replenishment. Prod Invent Manag 14:64–74Google Scholar
- 28.Mehdizadeh E, Tavakkoli-Moghaddam R (2008) Vibration damping optimization. Proceedings of the International Conference of Operations Research and Global Business, Germany, 3–5 SeptemberGoogle Scholar
- 29.Mehdizadeh E, Tavarroth MR, Hajipour V (2011) A new hybrid algorithm to optimize stochastic-fuzzy capacitated multi-facility location–allocation problem. J Optim Ind Eng 7:71–80Google Scholar
- 31.Geem ZW (2007) Harmony search algorithm for solving Sudoku. In: Apolloni B, Howlett RJ, Jain L (eds) KES 2007, part I. LNCS (LNAI), vol 4692. Springer, Heidelberg, pp 371–378Google Scholar
- 33.Geem ZW, Kim J-H, Loganathan GV (2002) Harmony search optimization: application to pipe network design. Int J Model Simul 22(125–133):2002Google Scholar
- 36.Yeniay O, Ankare B (2005) Penalty function methods for constrained optimization with genetic algorithms. Math Comput Appl 10:45–56Google Scholar
- 38.Haupt RL, Haupt SE (2011) Practical genetic algorithms, 2nd edn. John Wiley & SonsGoogle Scholar
- 42.Zitzler E, Thiele L (1998) Multi-objective optimization using evolutionary algorithms a comparative case study. In: Eiben A E, Back T, Schoenauer M Schwefel H P (eds) Fifth International Conference on Parallel Problem Solving from Nature (PPSN-V), pp. 292–301. Berlin, GermanyGoogle Scholar
- 43.MATLAB (2010) Version 22.214.171.1249 (R2010a). The MathWorks, Inc. Protected by US and International patentsGoogle Scholar