An enhanced simulation-based multi-objective optimization approach with knowledge discovery for reconfigurable manufacturing systems

TL;DR

This paper introduces an advanced simulation-based multi-objective optimization method with knowledge discovery to improve reconfigurable manufacturing systems, enabling better decision-making under uncertain production conditions.

Contribution

It presents a novel customized SMO approach combined with flexible pattern mining for optimizing RMS and supporting post-optimal analysis.

Findings

Optimized resource and buffer allocations under fluctuating volumes.

Enhanced decision support through knowledge discovery.

Improved throughput and reduced buffer capacity requirements.

Abstract

In today's uncertain and competitive market, where enterprises are subjected to increasingly shortened product life-cycles and frequent volume changes, reconfigurable manufacturing systems (RMS) applications play a significant role in the manufacturing industry's success. Despite the advantages offered by RMS, achieving a high-efficiency degree constitutes a challenging task for stakeholders and decision-makers when they face the trade-off decisions inherent in these complex systems. This study addresses work tasks and resource allocations to workstations together with buffer capacity allocation in RMS. The aim is to simultaneously maximize throughput and minimize total buffer capacity under fluctuating production volumes and capacity changes while considering the stochastic behavior of the system. An enhanced simulation-based multi-objective optimization (SMO) approach with customized simulation and optimization components is proposed to address the abovementioned challenges. Apart from presenting the optimal solutions subject to volume and capacity changes, the proposed approach support decision-makers with discovered knowledge to further understand the RMS design. In particular, this study presents a problem-specific customized SMO combined with a novel flexible pattern mining method for optimizing RMS and conducting post-optimal analyzes. To this extent, this study demonstrates the benefits of applying SMO and knowledge discovery methods for fast decision-support and production planning of RMS.