Unsupervised parallel machines scheduling with tool switches

This paper addresses the problem of scheduling jobs on identical parallel machines with tool switches in a high-mix, low-volume manufacturing environment. Inspired by the initiatives on “lights-out factory” at our industry partner, our problem setting involves several complex features. For example, we consider unsupervised production hours (e.g., night shifts where operators are not available) in which tool switches cannot occur. Moreover, motivated by current practice, tool switches in our problem setting cause costs instead of delays. Also, a subset of jobs is prioritized to be completed within a scheduling horizon, and a job may consist of ordered operations due to reentry to machines. The objective is to maximize the profit generated by the manufacturing system, which is composed of revenue generated by the finished operations minus tool switching costs and penalty costs of unfinished priority jobs. The decisions involve assigning operations to machines, sequencing these operations, and determining a tool-switching plan. A mix-integer linear programming model is first formulated. We then propose a genetic algorithm to solve industry-size problem instances, in which tailored crossover and mutation mechanisms are introduced. We illustrate the performance of the proposed GA with industry case studies using real-world data. We also make the anonymized data set publicly available. Computational experiments reveal that approximately 26% profit improvement can be achieved by using the proposed GA instead of the current way of scheduling at our industry partner. Moreover, we find that the proposed GA brings higher benefits when the duration of the unsupervised shifts gets longer, and there is high pressure on prioritizing jobs in the schedule.