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

Abstract

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.

A Fuzzy Simheuristic for the Permutation Flow Shop Problem under Stochastic and Fuzzy Uncertainty

“…Applications of simheuristics are very wide, especially in the field of logistics and transportation [24]. Recent works on simheuristics have focused on solving different optimization problems with stochastic uncertainty such as waste collection management [25], stochastic permutation flow shop problems and job shop problems [7], financial problems [26], etc. On the other hand, fuzzy logic-based systems have been utilized in optimization problems with uncertain non-probabilistic variables.…”

“…The metaheuristic uses this feedback to improve the search and construct new solutions in subsequent iterations. Examples of simheuristic applications to solve stochastic problems can be found in Juan et al [6] and Caldeira and Gnanavelbabu [7]. The simheuristic framework described in Rabe et al [8] handles systems with moderate stochastic uncertainty.…”

A Lean Robotics Approach to the Scheduling of Robotic Adhesive Dispensing Process

“…Mohsin et al [6] proposed a solution in which path planning was combined with simultaneous force control in a robotic grinding process. It was also observed that robot path planning problems were solved by multi-criteria analysis, wherein the solutions consist of determining relationships between path shortening, cost minimization, collision elimination and failure rate reduction [27,28].…”

Fuzzy Simheuristics for Optimizing Transportation Systems: Dealing with Stochastic and Fuzzy Uncertainty

“…Simheuristics have been successfully employed to solve problems related to different application fields, such as flow shop scheduling [32,33], job shop scheduling [34], waste collection [35][36][37], hazardous waste management [38], facility location [39], military applications [40], healthcare [41], finance [42], telecommunication networks [43], or disaster management [44]. Nevertheless, simheuristics have been mainly applied to the optimization of transportation systems.…”