Job‐shop scheduling with cooperative transportation resources

Abstract We study a job‐shop scheduling problem with transportation resources, which involves the simultaneous scheduling of machines and robots. In contrast to the existing literature, we consider scenarios where a transportation operation may require multiple robots simultaneously, requiring synchronization of resources over time. We formulate this problem using a mixed integer linear programming (MILP) approach. We show how our problem can be represented by a disjunctive graph. We explain how to describe a feasible solution using an operation sequence vector and a robot assignment vector, and we provide a fast algorithm for evaluating a solution described in this way. We then propose a greedy randomized adaptive search procedure and evolutionary local search meta‐heuristic combined with a local search procedure. Finally, in a numerical study, we adapt instances from the literature to align with our problem framework and also create new large‐scale instances specifically designed for our problem. For small to medium instances, the meta‐heuristic demonstrates competitive performance against exact solutions. For large instances, the metaheuristic outperforms the MILP approach.