Using evolution strategies and simulation to optimize a pull production system

✍ Scribed by John D. Hall; Royce O. Bowden; John M. Usher

• Publisher: Elsevier Science
• Year: 1996
• Tongue: English
• Weight: 541 KB
• Volume: 61
• Category: Article
• ISSN: 0924-0136
• DOI: 10.1016/0924-0136(96)02464-8

✦ Synopsis

Evolution strategies (ES) has proven to be a robust search technique for solving deterministic problems. An ES conducts its search by processing a population of solutions for an optimization problem based on principles from evolution. This paper describes the use of ES integrated with a simulation model, which includes stochastic processes of a manufacturing system, to solve the kanban sizing problem. The ES search heuristic determines the minimum number of kanbans and corresponding production trigger values required to meet demand. The procedure is illustrated with an applied problem from a leading appliance manufacturer consisting of 39 decision variables. Insights are provided on the effect of the population size (number of parents and offspring) on the fitness of the solutions. The solutions found by the ES search heuristic are compared to solutions obtained from using the Toyota kanban sizing equation. Results indicate that the ES search heuristic provides good solutions for large kanban sizing problems.