Volume 55, 2021Regular articles published in advance of the transition of the journal to Subscribe to Open (S2O). Free supplement sponsored by the Fonds National pour la Science Ouverte
|Page(s)||S1447 - S1467|
|Published online||02 March 2021|
Multi-objective multi-factory scheduling
Department of Industrial Engineering, Faculty of Engineering, Bu-Ali Sina University, Hamedan, Iran
2 Department of Industrial Engineering, Amirkabir University of Technology, 424 Hafez Avenue, Tehran 15916-34311, Iran
* Corresponding author: email@example.com
Accepted: 28 April 2020
This paper introduces a multi-factory scheduling problem with heterogeneous factories and parallel machines. This problem, as a major part of supply chain planning, includes the finding of a suitable factory for each job and the scheduling of the assigned jobs at each factory, simultaneously. For the first time, this paper studies multi-objective scheduling in the production network in which each factory has its customers and demands can be satisfied by itself or other factories. In other words, this paper assumes that jobs can transfer from the overloaded machine in the origin factory to the factory, which has fewer workloads by imposing some transportation times. For simultaneous minimization of the sum of the earliness and tardiness of jobs and total completion time, after modeling the scheduling problem as a mixed-integer linear program, the existing multi-objective techniques are analyzed and a new one is applied to our problem. Since this problem is NP-hard, a heuristic algorithm is also proposed to generate a set of Pareto optimal solutions. Also, the algorithms are proposed to improve and cover the Pareto front. Computational experiences of the heuristic algorithm and the output of the model implemented by CPLEX over a set of randomly generated test problems are reported.
Mathematics Subject Classification: 90B35 / 68M14 / 90C29 / 90C59
Key words: Scheduling / distributed system / multi-objective optimization / heuristic / elastic constraints method / Pareto front improving
© EDP Sciences, ROADEF, SMAI 2021
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