Exact and heuristic methods for the discrete parallel machine scheduling location problem

Scheduling and facility location represent two classes of well-studied combinatorial optimization problems. The main motivation for studying them relies on the broad range of applications (e.g., in public services, industry, logistics, project management, production planning, data processing, etc.), as well as on the challenge in providing efficient solutions, since many of these problems are classified as NPhard (see, e.g., Pinedo 2009, Pinedo 2016, Drezner and Hamacher 2002, and Laporte et al. 2015). Since the 1960s, many works on these topics have been published, but only a few of them has focused on studying these problems in an integrated fashion. Due to the limited capacity of the computers of two decades ago, it was usual to solve integrated combinatorial optimization problems using sequential approaches, i.e., solving each problem separately in such a way that the solution of one represents an input to the other. However, this strategy does not guarantee the optimality of the overall solution and, in addition, the input solutions may not be feasible for the successor problems. With the recent advances in technology, especially in the computational field, solving integrated combinatorial optimization problems using integrated approaches is becoming more accessible. In this context, the ScheLoc problem combines the job scheduling and facility location in a single and integrated problem.