Coupling Genetic Local Search and Recovering Beam Search algorithms for minimizing the total completion time in the single machine scheduling problem subject to release dates

  • Authors:

  • Mohamed Ali Rakrouki;Talel Ladhari;Vincent T'kindt

  • Affiliations:

  • ROI-Combinatorial Optimization Research Group, Ecole Polytechnique de Tunisie, 2078 La Marsa, Université du 7 Novembre í Carthage, Tunisia and Ecole Supérieure des Sciences Economiq ...;Ecole Supérieure des Sciences Economiques et Commerciales de Tunis, Université de Tunis, Tunisia and Princess Fatimah Alnijris's Research Chair for AMT College of Engineering, King Saud ...;Université Francois Rabelais de Tours, Tours, France

  • Venue:
  • Computers and Operations Research
  • Year:
  • 2012

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Abstract

In this paper we consider the well-known single machine scheduling problem with release dates and minimization of the total job completion time. For solving this problem, denoted by 1|r"j|@?C"j, we provide a new metaheuristic which is an extension of the so-called filtered beam search proposed by Ow and Morton [30]. This metaheuristic, referred to as a Genetic Recovering Beam Search (GRBS), takes advantages of a Genetic Local Search (GLS) algorithm and a Recovering Beam Search (RBS) in order to efficiently explore the solution space. In this paper we present the GRBS framework and its application to the 1|r"j|@?C"j problem. Computational results show that it consistently yields optimal or near-optimal solutions and that it provides interesting results by comparison to GLS and RBS algorithms. Moreover, these results highlight that the proposed algorithm outperforms the state-of-the-art heuristics.