A hybrid discrete firefly algorithm for solving multi-objective flexible job shop scheduling problems

被引:77
作者
Karthikeyan, S. [1 ]
Asokan, P. [1 ]
Nickolas, S. [2 ]
Page, Tom [3 ]
机构
[1] Natl Inst Technol, Dept Prod Engn, Tiruchirappalli 620015, Tamil Nadu, India
[2] Natl Inst Technol, Dept Comp Applicat, Tiruchirappalli 620015, Tamil Nadu, India
[3] Univ Loughborough, Loughborough Design Sch, Loughborough LE11 3TU, Leics, England
关键词
firefly algorithm; hybrid discrete firefly algorithm; HDFA; flexible job shop scheduling; F[!text type='JS']JS[!/text]P; discrete firefly algorithm; DFA; multi-objective optimisation; SWARM OPTIMIZATION ALGORITHM; TABU SEARCH ALGORITHM; GENETIC ALGORITHM;
D O I
10.1504/IJBIC.2015.073165
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Firefly algorithm (FA) is a nature-inspired optimisation algorithm that can be successfully applied to continuous optimisation problems. However, lot of practical problems are formulated as discrete optimisation problems. In this paper a hybrid discrete firefly algorithm (HDFA) is proposed to solve the multi-objective flexible job shop scheduling problem (FJSP). FJSP is an extension of the classical job shop scheduling problem that allows an operation to be processed by any machine from a given set along different routes. Three minimisation objectives - the maximum completion time, the workload of the critical machine and the total workload of all machines are considered simultaneously. This paper also proposes firefly algorithm's discretisation which consists of constructing a suitable conversion of the continuous functions as attractiveness, distance and movement, into new discrete functions. In the proposed algorithm discrete firefly algorithm (DFA) is combined with local search (LS) method to enhance the searching accuracy and information sharing among fireflies. The experimental results on the well-known benchmark instances and comparison with other recently published algorithms shows that the proposed algorithm is feasible and an effective approach for the multi-objective flexible job shop scheduling problems.
引用
收藏
页码:386 / 401
页数:16
相关论文
共 45 条
  • [11] A Particle Swarm Optimization algorithm for Flexible Job shop scheduling problem
    Girish, B. S.
    Jawahar, N.
    [J]. 2009 IEEE INTERNATIONAL CONFERENCE ON AUTOMATION SCIENCE AND ENGINEERING, 2009, : 298 - +
  • [12] Pareto simulated annealing for fuzzy multi-objective combinatorial optimization
    Hapke, M
    Jaszkiewicz, A
    Slowinski, R
    [J]. JOURNAL OF HEURISTICS, 2000, 6 (03) : 329 - 345
  • [13] An effective architecture for learning and evolving flexible job-shop schedules
    Ho, Nhu Binh
    Tay, Joc Cing
    Lai, Edmund M. -K.
    [J]. EUROPEAN JOURNAL OF OPERATIONAL RESEARCH, 2007, 179 (02) : 316 - 333
  • [14] Hsu T., 2002, SYST MAN CYB IEEE IN, V5
  • [15] Deterministic job-shop scheduling: Past, present and future
    Jain, AS
    Meeran, S
    [J]. EUROPEAN JOURNAL OF OPERATIONAL RESEARCH, 1999, 113 (02) : 390 - 434
  • [16] Jati GK, 2011, LECT NOTES ARTIF INT, V6943, P393, DOI 10.1007/978-3-642-23857-4_38
  • [17] Pareto-optimality approach for flexible job-shop scheduling problems: hybridization of evolutionary algorithms and fuzzy logic
    Kacem, I
    Hammadi, S
    Borne, P
    [J]. MATHEMATICS AND COMPUTERS IN SIMULATION, 2002, 60 (3-5) : 245 - 276
  • [18] Approach by localization and multiobjective evolutionary optimization for flexible job-shop scheduling problems
    Kacem, I
    Hammadi, S
    Borne, P
    [J]. IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART C-APPLICATIONS AND REVIEWS, 2002, 32 (01): : 1 - 13
  • [19] Karthikeyan S., 2012, International Journal of Manufacturing Technology and Management, V26, P81
  • [20] Khadwilard A., 2012, J. Ind. Technol., V8, P49