A Computational Study of the Job-Shop Scheduling Problem

Applegate, David; Cook, W. J.

doi:10.1287/ijoc.3.2.149

Cited by 727 publications

(403 citation statements)

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“…The edge-finding algorithm 2 is an extension of classical disjunctive edge-finding bounding techniques (Carlier and Pinson, 1990), (Applegate and Cook, 1991), (Nuijten, 1994), (Baptiste and Le Pape, 1995), (Caseau and Laburthe, 1995). This extension supports both non-interruptible and interruptible activities.…”

Section: • From Resources To Activitiesmentioning

confidence: 99%

Constraint propagation and decomposition techniques for highly disjunctive and highly cumulative project scheduling problems

Baptiste

Papel

1997

Principles and Practice of Constraint Programming-Cp97

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Abstract. In recent years, constraint satisfaction techniques have been successfully applied to "disjunctive" scheduling problems, i.e., scheduling problems where each resource can execute at most one activity at a time. Less significant and less generally applicable results have been obtained in the area of "cumulative" scheduling. Multiple constraint propagation algorithms have been developed for cumulative resources but they tend to be less uniformly effective than their disjunctive counterparts. Different problems in the cumulative scheduling class seem to have different characteristics that make them either easy or hard to solve with a given technique. The aim of this paper is to investigate one particular dimension along which problems differ. Within the cumulative scheduling class, we distinguish between "highly disjunctive" and "highly cumulative" problems: a problem is highly disjunctive when many pairs of activities cannot execute in parallel, e.g., because many activities require more than half of the capacity of a resource; on the contrary, a problem is highly cumulative if many activities can effectively execute in parallel. New constraint propagation and problem decomposition techniques are introduced with this distinction in mind. This includes an O(n 2 ) "edge-finding" algorithm for cumulative resources (where n is the number of activities requiring the same resource) and a problem decomposition scheme which applies well to highly disjunctive project scheduling problems. Experimental results confirm that the impact of these techniques varies from highly disjunctive to highly cumulative problems. In the end, we also propose a refined version of the "edge-finding" algorithm for cumulative resources which, despite its worst case complexity in O(n 3 ), performs very well on highly cumulative instances.

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Section: • From Resources To Activitiesmentioning

confidence: 99%

Constraint propagation and decomposition techniques for highly disjunctive and highly cumulative project scheduling problems

Baptiste

Papel

1997

Principles and Practice of Constraint Programming-Cp97

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“…We now consider 12 benchmark problems for job shop: the well-known FT10 and FT20, and the set of 10 problems identified in [1] as hard to solve for classical JSP: La21, La24, La25, La27, La29, La38, La40, ABZ7, ABZ8, and ABZ9. Ten fuzzy versions of each benchmark are generated following [5] and [9], so task durations become symmetric TFNs where the modal value is the original duration, ensuring that the optimal solution to the crisp problem provides a lower bound for the fuzzified version.…”

Section: Resultsmentioning

confidence: 99%

Combining Neighbourhoods in Fuzzy Job Shop Problems

Puente

Vela

González-Rodríguez

2011

Advances in Artificial Intelligence

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Abstract. In the sequel, we propose a new neighbourhood structure for local search for the fuzzy job shop scheduling problem, which is a variant of the well-known job shop problem, where uncertain durations are modelled as fuzzy numbers and the objective is to minimise the expected makespan of the resulting schedule. The new neighbourhood structure is based on changing the position of a task in a critical block. We provide feasibility conditions and a makespan estimate which allows to select only feasible and promising neighbours. The experimental results illustrate the success of our proposal in reducing expected makespan within a memetic algorithm. The experiments also show that combining the new structure with an existing neighbourhood from the literature considering both neighborhoods at the same time, provides the best results.

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“…We now consider 12 benchmark problems for job shop: the well-known FT10 and FT20, and the set of 10 problems identified in [18] as hard to solve for classical JSP: La21, La24, La25, La27, La29, La38, La40, ABZ7, ABZ8, and ABZ9. Ten fuzzy versions of each benchmark are generated following [10] and [12], so task durations become symmetric TFNs where the modal value is the original duration, ensuring that the optimal solution to the crisp problem provides a lower bound for the fuzzified version.…”

Section: Resultsmentioning

confidence: 99%

Improving Local Search for the Fuzzy Job Shop Using a Lower Bound

Puente

Vela

Hernández-Arauzo

et al. 2010

Current Topics in Artificial Intelligence

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Abstract. We consider the fuzzy job shop problem, where uncertain durations are modelled as fuzzy numbers and the objective is to minimise the expected makespan. A recent local search method from the literature has proved to be very competitive when used in combination with a genetic algorithm, but at the expense of a high computational cost. Our aim is to improve its efficiency with an alternative rescheduling algorithm and a makespan lower bound to prune non-improving neighbours. The experimental results illustrate the success of our proposals in reducing both CPU time and number of evaluated neighbours.

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A Computational Study of the Job-Shop Scheduling Problem

Cited by 727 publications

References 0 publications

Constraint propagation and decomposition techniques for highly disjunctive and highly cumulative project scheduling problems

Constraint propagation and decomposition techniques for highly disjunctive and highly cumulative project scheduling problems

Combining Neighbourhoods in Fuzzy Job Shop Problems

Improving Local Search for the Fuzzy Job Shop Using a Lower Bound

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