Simulation-based multi-objective optimization for reconfigurable manufacturing system

Carlos A. Barrera-Diaz, Tehseen Aslam, Amos H.C. Ng, Erik Flores-García & Magnus Wiktorsson (2020). Simulation-based multi-objective optimization for reconfigurable manufacturing system configurations analysis. Proceedings of the 2020 Winter Simulation Conference.


The purpose of this study is to analyze the use of Simulation-Based Multi-Objective Optimization (SMO) for Reconfigurable Manufacturing System Configuration Analysis (RMS-CA). In doing so, this study addresses the need for efficiently performing RMS-CA with respect to the limited time for decision-making in the industry, and investigates one of the salient problems of RMS-CA: determining the minimum number of machines necessary to satisfy demand. The study adopts an NSGA II optimization algorithm and presents two contributions to existing literature. Firstly, the study proposes a series of steps for the use of SMO for RMS-CA and shows how to simultaneously maximize production throughput, minimize lead time, and buffer size. Secondly, the study presents a comparison between prior work in RMS-CA and the proposed use of SMO. The study discusses the advantages and challenges of using SMO and provides critical insight for production engineers and managers responsible for production system configuration. Access publication.

5 views
Initiators

VF-KDO is financed by: