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Applying multi-start simulated annealing to schedule a flowline manufacturing cell with sequence dependent family setup times

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  • Lin, Shih-Wei
  • Ying, Kuo-Ching
  • Lu, Chung-Cheng
  • Gupta, Jatinder N.D.

Abstract

Meta-heuristics that attempt to obtain (near) global optimal solutions of NP-hard combinatorial optimization problems generally require diversification to escape from local optimality. One way to achieve diversification is to utilize the multi-start hill climbing strategy. By combining the respective advantages of the multi-start hill climbing strategy and simulated annealing (SA), an effective multi-start simulated annealing (MSA) heuristic is proposed to minimize the makespan for a flowline manufacturing cell scheduling problem with sequence dependent family setup times. The heuristic performance is evaluated by comparing the results achieved by the proposed heuristic with those achieved by the existing meta-heuristics. The computational results show that following multi-start refinement the proposed MSA heuristic is more effective compared to the state-of the-art meta-heuristics on the same benchmark instances.

Suggested Citation

  • Lin, Shih-Wei & Ying, Kuo-Ching & Lu, Chung-Cheng & Gupta, Jatinder N.D., 2011. "Applying multi-start simulated annealing to schedule a flowline manufacturing cell with sequence dependent family setup times," International Journal of Production Economics, Elsevier, vol. 130(2), pages 246-254, April.
    • Handle: RePEc:eee:proeco:v:130:y:2011:i:2:p:246-254
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    References listed on IDEAS

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    Cited by:

    1. Allahverdi, Ali, 2015. "The third comprehensive survey on scheduling problems with setup times/costs," European Journal of Operational Research, Elsevier, vol. 246(2), pages 345-378.
    2. Solimanpur, Maghsud & Elmi, Atabak, 2013. "A tabu search approach for cell scheduling problem with makespan criterion," International Journal of Production Economics, Elsevier, vol. 141(2), pages 639-645.
    3. Diaz, Rafael & Bailey, Mike, 2011. "Building knowledge to improve enterprise performance from inventory simulation models," International Journal of Production Economics, Elsevier, vol. 134(1), pages 108-113, November.

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