Joint Allocation of Buffer Sizes and Service Times in Unreliable Assembly-Disassembly Systems

This paper introduces a modeling methodology for Assembly/Disassembly (AD) systems, proposing an effective simulation-based optimization approach. The objective is to simultaneously determine optimal allocations of service times and buffer sizes within unreliable AD systems, ultimately aiming to maximize the production rate. Our method integrates a global search approach, Genetic Algorithm (GA), with a local search method, Finite Perturbation Analysis (FPA). Through this integration, we extract production rate gradient information based on system parameters, which is then incorporated into a stochastic optimization algorithm to simultaneously identify the best allocation of service times and buffer sizes. The proposed approach is applied to various instances of AD systems, and experiments are conducted to assess its performance. The results demonstrate the superior performance of the integrated GA-FPA method compared to the standalone GA and FPA methods, both in terms of convergence behavior and solution quality. The identified allocation patterns and the potential to expand our approach to other complex manufacturing systems can provide valuable insights for managers seeking to establish more sustainable and efficient assembly and disassembly systems.