Multi-Objective Optimization-Based Approach for Throughput Maximization in Reconfigurable Manufacturing Systems

Realizing a specific product at a very high rate and with short time is contemporary market demand. Reconfigurable manufacturing systems (RMSs) represent a modern manufacturing paradigm. RMSs are capable of modifying their behavior when needed in hardware and software components. This paper tackles a multi-objective optimization problem which is the optimal process planning for maximizing throughput of RMS. To deal with this problem, we propose a multi-objective optimization model for achieving high throughput of RMS based on three objectives: minimum completion time of all operations of a product, minimum lateness time of reconfigurable machine tools (RMTs) availability, and minimum RMTs failure correctness time. We adapt a recent multi-objective genetic algorithm (i.e., NSGA-III) for getting a set of optimal process plans (i.e., Pareto set) based on the proposed customized genetic operators (mutation and crossover) and a labeled precedence graph (LPG). Then a multi-objective decision-making approach known as "technique for order of preference by similarity to ideal solution" (TOPSIS) is used for helping the company in selecting the best process plan among the Pareto set. The proposed multi-objective optimization model is applied to a case study for illustrating its effectiveness.