Job Sequencing Problem in a Semi-automated Production Process

In this work the authors addresses the problem of sequencing a set of jobs on a single machine using a genetic algorithm and simulation. The goal is to find the schedule that minimizes the total earliness and tardiness penalties of all jobs, under the assumptions that no pre-emption of jobs is allowed and all jobs are available at time zero. In order to accelerate the search process, the Authors also implemented a procedure for genetic algorithm initialization. Simulation has been used for the fitness evaluation of the population’s members: in this way, one of the most critical issues related to evolutionary computation has been successfully addressed. This hybrid approach led to an effective tool adopted for the scheduling in a real production plant, where three bottling lines are used and several kind of product are commercialized.