Unrelated Parallel Machine Scheduling with Sequence-Dependent Setup Times in Multi-Factory Production Network: Modeling and Algorithm

Today, due to some challenges and competition, such as external pressures, factories are forced to reduce production time, traditional centralized production scheduling is not flexible enough to respond to rapid market changes. In such an environment, factories decide to merge and form a multi-factory production network to work more closely together.  In this research, the multi-factory scheduling problem is considered, which factories belong to a company. The problem is assigning the jobs to appropriate factory and scheduling jobs on machines in each factory. In this paper, it is assumed machines in each factory are unrelated parallel machines. For scheduling jobs on machines sequence-dependent setup times are considered. After proposing a novel mixed integer linear programming model for the problem which is a combination of two types of modeling based on sequence and assignment, we developed an evolutionary metaheuristic namely imperialist competitive algorithm (ICA) to minimize the maximum completion time or makespan among the factories. We compare the obtained solutions using the proposed ICA with those using an adopted genetic algorithm to show the efficiency of the proposed algorithm. Finally, the results are reported. Numerical results show that the proposed algorithm has good performance.