• 2026-05-08
• M.O. Idris, B.S. Adeboye, A.A Adegoke, A.G Abioye, S.T. Oyewo, H.O. Lawal, A.A. Aweda
• Volume 2, Issue 1

Abstract

This study optimised job scheduling in job shop environments using simulation modelling to address challenges of high work-in-process, long flow times, and job lateness. A discrete-event simulation model was developed in SimPy to represent a five-machine job shop with stochastic job arrivals, variable processing times, routing complexity, and machine reliability. Four dispatching rules—first come first serve, shortest processing time, longest processing time, and earliest due date—were evaluated under low, medium, and high workload scenarios. Key performance indicators included average flow time, work-in-process, throughput, and late job ratio, with results validated using replication and ANOVA analysis. The results show that the shortest processing time rule consistently achieved the best performance, delivering flow times of about 1.22–1.24 days, minimizing lateness to 0.037–0.042, and increasing throughput to up to 900 jobs under high load. In contrast, FCFS and EDD recorded high lateness (0.80–0.94) and longer flow times (6–12 days). Overall, SPT improved system efficiency by over 80%, demonstrating that simulation-based scheduling is an effective decision-support tool for enhancing operational performance in job shop systems.