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

Angelo Oddi, Riccardo Rasconi, Amedeo Cesta and Stephen F. Smith

From the issue entitled "Special Issue: Experimental evaluation of algorithms for solving problems with combinatorial explosion"

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Abstract

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.

Keywords  Random-restart – Constraint-based reasoning – Job-shop scheduling – Setup times – Generalized precedence constraints

Mathematics Subject Classifications (2010)  68T20 – 68M20 – 68W20

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