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Issue title: 19th RCRA International Workshop on “Experimental evaluation of algorithms for solving problems with combinatorial explosion”
Article type: Research Article
Authors: Mencía, Carlos | Sierra, María R. | Salido, Miguel A. | Escamilla, Joan | Varela, Ramiro;
Affiliations: Departamento de Informática, Universidad de Oviedo, Gijón, Spain. E-mails: [email protected], [email protected], [email protected] | Instituto Universitario de Automática e Informática Industrial, Universidad Politécnica de Valencia, Valencia, Spain. E-mails: [email protected], [email protected]
Note: [] Corresponding author: Ramiro Varela, Departamento de Informática, Universidad de Oviedo, Campus de Viesques s/n, 33271, Gijón, Spain. E-mail: [email protected]
Abstract: The job shop scheduling problem with an additional resource type has been recently proposed to model the situation where each operation in a job shop has to be assisted by one of a limited set of human operators. We confront this problem with the objective of minimizing the total flow time, which makes the problem more interesting from a practical point of view and harder to solve than the version with makespan minimization. To solve this problem we propose an enhanced dept-first search algorithm. This algorithm exploits a schedule generation schema termed OG&T, two admissible heuristics and some powerful pruning rules. In order to diversify the search, we also consider a variant of this algorithm with restarts. We have conducted an experimental study across several benchmarks. The results of this study show that the global pruning rules are really effective and that the proposed algorithms are quite competent for solving this problem.
Keywords: Depth first search, pruning by dominance, job shop scheduling problem, operators, restarts
DOI: 10.3233/AIC-140630
Journal: AI Communications, vol. 28, no. 2, pp. 365-381, 2015
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