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

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 $1\left|r_j\right|\Sigma C_j$ , we provide a new metaheuristic which is an extension of the so‐called filtered beam search proposed by Ow and Morton . 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 $1\left|r_j\right|\Sigma C_j$ 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.