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Scheduling of flexible manufacturing plants with redesign options: A MILP-based decomposition algorithm and case studies
•Flexible manufacturing plants.•MILP-based decomposition procedure.•Efficient MILP model embedded within an iterative decomposition algorithm.•Integrated scheduling and redesign problem.•Effective solutions of two real world scheduling problems. In the last years, the operational research on schedul...
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Published in: | Computers & chemical engineering 2020-05, Vol.136, p.106777, Article 106777 |
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Main Authors: | , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
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Summary: | •Flexible manufacturing plants.•MILP-based decomposition procedure.•Efficient MILP model embedded within an iterative decomposition algorithm.•Integrated scheduling and redesign problem.•Effective solutions of two real world scheduling problems.
In the last years, the operational research on scheduling problems has been moving away from rigorous optimization approaches into solution strategies being capable of returning practical and fast solutions for large-scale industrial problems. Following this line, this paper proposes a novel MILP-based decomposition procedure for solving scheduling problems arising in flexible manufacturing environments, which generally involve multipurpose units and assembly operations. The solution strategy also considers redesign constraints with the goal of improving the efficiency of the production system, preventing bottlenecks and balancing the equipment utilization. The proposal is validated through the resolution of several instances derived from three real-world case-studies coming from different industrial sectors. The computational results show that the decomposition procedure is capable of generating high quality solutions, sometimes the optimal one, with minimum computational effort for all problem instances considered.
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ISSN: | 0098-1354 1873-4375 |
DOI: | 10.1016/j.compchemeng.2020.106777 |