A new method for mathematical and simulation modelling interactivity: A case study in flexible job shop scheduling

Abstract

The present study has investigated mathematical and simulation model interactivity for production system scheduling. A mathematical model of a Flexible Job Shop Scheduling Production optimisation problem (FJSSP) was used to evaluate a new evolutionary computation method of multi-objective heuristic Kalman algorithm (MOHKA). Ten Brandimarte and five Kacem benchmarks were applied for evaluation and comparison of MOHKA optimisation results with the Multi-Objective Particle Swarm Optimization algorithm (MOPSO) and Bare-Bones Multi-Objective Particle Swarm Optimization algorithm (BBMOPSO). Benchmark data sets were divided into three groups, regarding their complexity, from low, middle to high dimensional optimisation problems. The optimisation results of MOHKA show high capability to solve complex multiobjective optimisation problems, especially with real world production systems data. A new robust method is presented of optimisation data interactivity between a mathematical optimisation algorithm and a simulation model. The results show that the presented method can overcome the integrated decision logic of commercial simulation software and transfer the optimisation results into the simulation model. Our interactive method can be used in a variety of production and service companies to ensure an optimised and sustainable cost-time profile.