Simultaneous optimization of parts and operations sequences in SSMS: a chaos embedded Taguchi particle swarm optimization approach

Abstract

Simultaneous optimization of interrelated manufacturing processes viz. part sequencing and operation sequencing is required for the efficient allocation of production resources. Present paper addresses this problem with an integrated approach for Single Stage Multifunctional Machining System (SSMS), and identifies the best part sequence available in the part-mix. A mathematical model has been formulated to minimize the broad objectives of set-up cost and time simultaneously. The proposed approach has more realistic attributes as fixture related intricacies are also taken into account for model formulation. It has been solved by a new variant of particle swarm optimization (PSO) algorithm and named as Chaos embedded Taguchi particle swarm optimization (CE-TPSO) that draws its traits from chaotic systems, statistical design of experiments and time varying acceleration coefficients (TVAC). A simulated case study has been adopted from the literature and effectiveness of the proposed algorithm is proved. The results obtained with different variants of its own are compared along with the basic PSO and Genetic Algorithm (GA) to reveal the superiority of the proposed algorithm.