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A truncated column generation algorithm for the parallel batch scheduling problem to minimize total flow time

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  • Ozturk, Onur

Abstract

In this paper, we present a column generation based decomposition method for the parallel batch scheduling of jobs on identical parallel machines. Jobs have different release dates, processing times and sizes while machines have limited capacity. The objective is the minimization of total flow time, i.e., sum of completion times of all processed jobs. The straightforward mixed integer linear programming model of the problem is efficient for small size instances. To deal with larger instances, we develop a time indexed column generation model in which each column represents the set of jobs processed together in the same batch and the time instant for the beginning of batch processing. We obtain approximate solutions with a rounding technique followed by another mathematical model to schedule batches on machines. Numerical test results show that the proposed model is able to return high quality solutions within short computation times.

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  • Ozturk, Onur, 2020. "A truncated column generation algorithm for the parallel batch scheduling problem to minimize total flow time," European Journal of Operational Research, Elsevier, vol. 286(2), pages 432-443.
    • Handle: RePEc:eee:ejores:v:286:y:2020:i:2:p:432-443
    DOI: 10.1016/j.ejor.2020.03.044
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    3. Xu, Jun & Wang, Jun-Qiang & Liu, Zhixin, 2022. "Parallel batch scheduling: Impact of increasing machine capacity," Omega, Elsevier, vol. 108(C).
    4. Zhang, Han & Li, Kai & Jia, Zhao-hong & Chu, Chengbin, 2023. "Minimizing total completion time on non-identical parallel batch machines with arbitrary release times using ant colony optimization," European Journal of Operational Research, Elsevier, vol. 309(3), pages 1024-1046.
    5. Liu, Baoli & Li, Zhi-Chun & Wang, Yadong, 2023. "A branch-and-price heuristic algorithm for the bunkering operation problem of a liquefied natural gas bunkering station in the inland waterways," Transportation Research Part B: Methodological, Elsevier, vol. 167(C), pages 145-170.
    6. He, Xuan & Pan, Quan-Ke & Gao, Liang & Neufeld, Janis S. & Gupta, Jatinder N.D., 2024. "Historical information based iterated greedy algorithm for distributed flowshop group scheduling problem with sequence-dependent setup times," Omega, Elsevier, vol. 123(C).
    7. Wang, Zehao & Zeng, Qingcheng & Li, Xingchun & Qu, Chenrui, 2024. "A branch-and-price heuristic algorithm for the ART and external truck scheduling problem in an automated container terminal with a parallel layout," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 184(C).
    8. 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.
    9. Alessandro Druetto & Erica Pastore & Elena Rener, 2023. "Parallel batching with multi-size jobs and incompatible job families," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 31(2), pages 440-458, July.
    10. A. Alfieri & A. Druetto & A. Grosso & F. Salassa, 2021. "Column generation for minimizing total completion time in a parallel-batching environment," Journal of Scheduling, Springer, vol. 24(6), pages 569-588, December.
    11. 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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