Solving job shop scheduling with setup times through constraint-based iterative sampling: an experimental analysis

• Published in: Annals of mathematics and artificial intelligence Vol. 62; no. 3-4; pp. 371 - 402
• Main Authors: Oddi, Angelo, Rasconi, Riccardo, Cesta, Amedeo, Smith, Stephen F.
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01.07.2011; Springer Nature B.V
• Subjects: Algorithms; Artificial Intelligence; Benchmarks; Complex Systems; Computer Science; Conflict resolution; Heuristic job shop scheduling; Heuristic methods; Job shop scheduling; Mathematics; Precedence constraints; Sampling methods; Searching; Setup times; Random-restart; 68W20; Constraint-based reasoning; 68T20; Setup times; 68M20; Job-shop scheduling; Generalized precedence constraints
• ISSN: 1012-2443; 1573-7470; 1573-7470
• DOI: 10.1007/s10472-011-9264-8

This paper presents a heuristic algorithm for solving a job-shop scheduling problem with sequence dependent setup times and min/max separation constraints among the activities (SDST-JSSP/ max ). The algorithm relies on a core constraint-based search procedure, which generates consistent orderings of activities that require the same resource by incrementally imposing precedence constraints on a temporally feasible solution. Key to the effectiveness of the search procedure is a conflict sampling method biased toward selection of most critical conflicts and coupled with a non-deterministic choice heuristic to guide the base conflict resolution process. This constraint-based search is then embedded within a larger iterative-sampling search framework to broaden search space coverage and promote solution optimization. The efficacy of the overall heuristic algorithm is demonstrated empirically both on a set of previously studied job-shop scheduling benchmark problems with sequence dependent setup times and by introducing a new benchmark with setups and generalized precedence constraints.