On Solving the Job Sequencing and Tool Switching Problem With Non-Identical Parallel Machines and Stochastic Processing Times

This paper investigates the job sequencing and tool switching problem with non-identical parallel machines and stochastic processing times, referred to as SSP-NPM-SPT. It simultaneously determines the job processing sequence and the tool switching schedule while minimizing the makespan. Processing time uncertainty reflects the real-world conditions of flexible manufacturing systems where processing time variability can be attributed to many factors such as machines, humans, and supply chains. We propose a Monte Carlo Simulation approach to generate various scenarios and multiple levels of variability for stochastic processing times to determine optimal parameter settings. Extensive computational experiments were performed using benchmark instances from the literature and considering a representative set of scenarios. The results show that integrating stochastic modeling with Monte Carlo Simulation provides a robust framework to effectively address the complexities of SSP-NPM-SPT.