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Makespan minimization in flowshop batch processing problem with different batch compositions on machines

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  • Matin, Hossein N.Z.
  • Salmasi, Nasser
  • Shahvari, Omid

Abstract

In this research, we consider the flowshop batch processing problem (FBPP) with minimization of makespan, in which the composition of batches can change on different machines. A batch capacity of a machine restricts not only the maximum number of jobs, but also the total attribute size of jobs assigned to the batch processed on the machine. This is the first time that the FBPP is considered for different batch compositions on machines with respect to both the total size and the number of jobs assigned to batches. We propose a mixed-integer linear programming model for the research problem. Since this problem is shown to be NP-hard, several meta-heuristic algorithms based on particle swarm optimization (PSO), enhanced with local search structures, are proposed to solve the research problem heuristically. To have more diversity, different rules are implemented to generate the initial population of the PSO algorithms. Two lower bounding mechanisms are also proposed to generate good quality lower bounds for special cases of the research problem and, consequently, evaluate the performance of the proposed PSO algorithms. A data generation mechanism has been developed in a way that it fairly reflects the real industry requirements. The proposed PSO algorithms are examined by different numerical experiments and the results affirm the efficiency of the proposed algorithms.

Suggested Citation

  • Matin, Hossein N.Z. & Salmasi, Nasser & Shahvari, Omid, 2017. "Makespan minimization in flowshop batch processing problem with different batch compositions on machines," International Journal of Production Economics, Elsevier, vol. 193(C), pages 832-844.
  • Handle: RePEc:eee:proeco:v:193:y:2017:i:c:p:832-844
    DOI: 10.1016/j.ijpe.2017.09.015
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    References listed on IDEAS

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    1. Damodaran, Purushothaman & Kumar Manjeshwar, Praveen & Srihari, Krishnaswami, 2006. "Minimizing makespan on a batch-processing machine with non-identical job sizes using genetic algorithms," International Journal of Production Economics, Elsevier, vol. 103(2), pages 882-891, October.
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    3. Shahvari, Omid & Logendran, Rasaratnam, 2016. "Hybrid flow shop batching and scheduling with a bi-criteria objective," International Journal of Production Economics, Elsevier, vol. 179(C), pages 239-258.
    4. Sung, Chang Sup & Kim, Young Hwan & Yoon, Sang Hum, 2000. "A problem reduction and decomposition approach for scheduling for a flowshop of batch processing machines," European Journal of Operational Research, Elsevier, vol. 121(1), pages 179-192, February.
    5. Nawaz, Muhammad & Enscore Jr, E Emory & Ham, Inyong, 1983. "A heuristic algorithm for the m-machine, n-job flow-shop sequencing problem," Omega, Elsevier, vol. 11(1), pages 91-95.
    6. Potts, Chris N. & Kovalyov, Mikhail Y., 2000. "Scheduling with batching: A review," European Journal of Operational Research, Elsevier, vol. 120(2), pages 228-249, January.
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    Cited by:

    1. Ivan Kristianto Singgih & Onyu Yu & Byung-In Kim & Jeongin Koo & Seungdoe Lee, 2020. "Production scheduling problem in a factory of automobile component primer painting," Journal of Intelligent Manufacturing, Springer, vol. 31(6), pages 1483-1496, August.
    2. Omid Shahvari & Rasaratnam Logendran & Madjid Tavana, 2022. "An efficient model-based branch-and-price algorithm for unrelated-parallel machine batching and scheduling problems," Journal of Scheduling, Springer, vol. 25(5), pages 589-621, October.
    3. Husseinzadeh Kashan, Ali & Ozturk, Onur, 2022. "Improved MILP formulation equipped with valid inequalities for scheduling a batch processing machine with non-identical job sizes," Omega, Elsevier, vol. 112(C).

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