Type-2 integrated process-planning and scheduling problem: Reformulation and solution algorithms

We study the type-2 integrated process-planning and scheduling (IPPS) problem where each job is represented by a directed network graph. To the best of our knowledge, there is only one mathematical model in the literature implementing the type-2 IPPS partially, and the solution methods available for...

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Veröffentlicht in:Computers & operations research Jg. 142; S. 105728
Hauptverfasser: Naderi, Bahman, Begen, Mehmet A., Zaric, Gregory S.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Elsevier Ltd 01.06.2022
Pergamon Press Inc
Schlagworte:
Algorithms
Benchmarks
Benders decomposition
Combinatorial analysis
Constraint programming
Graphical representations
Heuristic methods
Mathematical analysis
Mathematical models
Mathematical programming
Operations research
Optimality cut
Scheduling
Type-2 integrated process planning and scheduling problem
Optimality cut
Benders decomposition
Type-2 integrated process planning and scheduling problem
Constraint programming
ISSN:0305-0548, 1873-765X, 0305-0548
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Zusammenfassung:We study the type-2 integrated process-planning and scheduling (IPPS) problem where each job is represented by a directed network graph. To the best of our knowledge, there is only one mathematical model in the literature implementing the type-2 IPPS partially, and the solution methods available for this problem are all based on heuristics and metaheuristics. We introduce three properties that enable us to fully formulate all aspects of the type-2 IPPS problem with a mathematical programming model for the first time. To solve our model, we develop a logic-based Benders decomposition method hybridized with constraint programming. We decompose the problem into two smaller ones such that we can use the best solution technique for each one, master problem and subproblem. To enhance our solution approach, we incorporate a combinatorial relaxation of subproblem into the master problem. We evaluate our method using a well-known benchmark including 24 instances and compare its performance with six existing solution methods solving the same benchmark. We solve all the 24 instances of this benchmark to optimality where seven of these 24 instances are solved to optimality for the first time. We also generate a new set of 144 larger instances to further evaluate our solution methods and provide insights on when each method performs better. •A full mathematical model for the type-2 IPPS problem.•Logic-based Benders decomposition method with constraint programming.•7 of 24 existing benchmark instances are solved to optimality for the first time.•A new set of 144 larger instances to further evaluate our solution methods.•Insights based on numerical experiments on problem size and solution methods.
Bibliographie:ObjectType-Article-1
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ISSN:0305-0548
1873-765X
0305-0548
DOI:10.1016/j.cor.2022.105728