A simheuristic approach for the flexible job shop scheduling problem with stochastic processing times

In this work, we address the flexible job shop scheduling problem (FJSSP), which is a classification of the well-known job shop scheduling problem. This problem can be encountered in real-life applications such as automobile assembly, aeronautical, textile, and semiconductor manufacturing industries. To represent inherent uncertainties in the production process, we consider stochastic flexible job shop scheduling problem (SFJSSP) with operation processing times represented by random variables following a known probability distribution. To solve this stochastic combinatorial optimization problem we propose a simulation-optimization approach to minimize the expected makespan. Our approach employs Monte Carlo simulation integrated into a Jaya algorithm framework. Due to the unavailability of standard benchmark instances in SFJSSP, our algorithm is evaluated on an extensive set of well-known FJSSP benchmark instances that are extended to SFJSSP instances. Computational results demonstrate the performance of the algorithm at different variability levels through the use of reliability-based methods.

Automated summary

This study addresses the flexible job shop scheduling problem with a focus on stochastic approaches. By integrating Monte Carlo simulation with the Jaya algorithm framework, a solution is proposed to minimize the expected makespan. Through evaluation on extended benchmark instances, the algorithm's performance at different variability levels is demonstrated using reliability-based methods.