Abstract
This chapter presents an efficient evolutionary algorithm, called multi-objective differential evolution algorithm (MODE) to find a Pareto front for multi-objective flexible job shop scheduling problems. The objective is to simultaneously minimize makespan and total tardiness of jobs. The MODE framework adopts the idea of the Elite group to store solutions and utilizes those solutions as the guidance of the vectors. Five mutation strategies with different search behaviors are proposed in MODE algorithms in order to search for the good quality Pareto front. The performances of the MODE algorithms with different mutation strategies are evaluated on a set of benchmark problems and compared with results obtained from an existing algorithm. The experimental results demonstrated that the MODE algorithms are highly competitive approaches which are capable of providing a set of diverse and high-quality non-dominated solutions.
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References
Abbass HA, Sarker R, Newton C (2001) PDE: a Pareto-frontier differential evolution approach for multi-objective optimization problems. In Proceedings of the 2001 congress on evolutionary computation, vol 2, pp 971–978
Bean JC (1994) Genetic algorithms and random keys for sequencing and optimization. ORSA J Comput 6(2):154–160
Bierwirth C (1995) A generalized permutation approach to job shop scheduling with genetic algorithms. OR Spectr Spec Issue Appl Local Search 17(2–3):87–92
Brandimarte P (1993) Routing and scheduling in a flexible job-shop by tabu search. Ann Oper Res 41(3):157–183
Dauzère-Pérès S, Paulli J (1997) An integrated approach for modeling and solving the general multiprocessor job-shop scheduling problem using tabu search. Ann Oper Res 70:281–306
Gao J, Gen M, Sun L, Zhao X (2007). A hybrid of genetic algorithm and bottleneck shifting for multiobjective flexible job shop scheduling problems. Comput Ind Eng 53(1):149–162
He Z, Yang T, Deal DE (1993) A multiple-pass heuristic rule for job-shop scheduling with due dates. Int J Prod Res 31(11):187–199
Kacem I, Hammadi S, Borne P (2002a) Approach by localization and multi-objective evolutionary optimization for flexible job shop scheduling problems. IEEE Trans Syst Man Cybertics Part C 32(1):1–13
Kacem I, Hammadi S, Borne P (2002b) Pareto-optimality approach for flexible job-shop scheduling problems: hybridization of evolutionary algorithms and fuzzy logic. Math Comput Simul 60(3–5):245–276
Madavan NK (2002) Multiobjective optimization using a Pareto differential evolution approach. In: Proceedings of the evolutionary computation, pp 1145-1150, 12-17 May 2002
Moslehi G, Mahnam M (2011) A Pareto approach to multi-objective flexible job-shop scheduling problem using particle swarm optimization and local search. Int J Prod Econ 129(1):14–22
Nguyen S, Kachitvichyanukul V (2010) Movement strategies for multi-objective particle swarm optimization. Int J Appl Metaheuristic Comput 1(3):59–79
Qian B, Wang L, Huang DX, Wang X (2008) Scheduling multi-objective job shops using a memetic algorithm based on differential evolution. Int J Adv Manuf Technol 35(9–10):1014–1027
Storn R, Price K (1995) Differential evolution—a simple and efficient adaptive scheme for global optimization over continuous spaces. Technical Report TR-95-012. International Computer Science, Berkeley, CA
Wisittipanich W, Kachitvichyanukul V (2011) Differential evolution algorithm for makespan minimization in flexible job shop scheduling problem. In: Proceeding of the 12th Asia Pacific industrial engineering and management systems conference, Beijing, China
Wisittipanich W, Kachitvichyanukul V (2013) Total Tardiness minimization in flexible job shop scheduling problem by differential evolution algorithm. In: Proceeding of operations research network conference, Thailand
Wisittipanich W, Kachitvichyanukul V (2014) Mutation Strategies toward Pareto front for multi-objective differential evolution algorithm. Int J Oper Res 19(3):317–337
Wisittipanich W, Kachitvichyanukul V (2013) A Pareto-based differential evolution algorithm for multi-objective job shop scheduling problems. In: Proceeding of the institute of industrial engineers Asian conference, pp 1117–1125
Zhang H, Gen M (2005) Multistage-based genetic algorithm for flexible job-shop scheduling problem. J Complex Int 11:223–232
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Wisittipanich, W., Kachitvichyanukul, V. (2014). A Pareto-Archived Differential Evolution Algorithm for Multi-Objective Flexible Job Shop Scheduling Problems. In: Golinska, P. (eds) Logistics Operations, Supply Chain Management and Sustainability. EcoProduction. Springer, Cham. https://doi.org/10.1007/978-3-319-07287-6_23
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DOI: https://doi.org/10.1007/978-3-319-07287-6_23
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