  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.
