Particle swarm optimization in multi-stage operations for operation sequence and DT allocation

• Published in: Computers & industrial engineering Vol. 62; no. 2; pp. 442 - 450
• Main Authors: Jeung, Heun-Sik, Choi, Hoo-Gon
• Format: Journal Article
• Language: English
• Published: New York Elsevier Ltd 01.03.2012; Pergamon Press Inc
• Subjects: Allocations; Costs; Dimensional tolerance (DT) allocation; Economics; Learning theory; Manufacturing; Manufacturing costs; Mathematical models; Multi-stage operations; Neural network; Neural networks; Operation sequence generation; Optimization; Optimization algorithms; Particle swarm optimization; Production costs; Studies; Tolerances; Dimensional tolerance (DT) allocation; Neural network; Multi-stage operations; Operation sequence generation; Particle swarm optimization
• ISSN: 0360-8352; 1879-0550
• DOI: 10.1016/j.cie.2011.10.009

Procedure for optimal operation sequence and DT allocation. [Display omitted] ► Use both artificial neural network and particle swarm optimization algorithm. ► Simultaneous optimization for sequence and DT allocation in multi-stage operations. ► Meet the design specifications with the results and utilize them in routing sheet generation. Improved operation sequence and economic tolerance allocation directly influence product quality and manufacturing costs. The purpose of this study is to generate the optimal operation sequence and allocate economic tolerances to cutting surfaces to achieve the specified quality and minimize the manufacturing costs. Because this type of problem is a multi-objective optimization problem subject to various constraints, it is defined as an NP-hard problem. A three-step procedure is used to solve the problem. First, a mathematical model is developed to define the relationships between manufacturing costs and tolerances. Second, an artificial neural network (ANN) is applied to obtain the best fitting cost-tolerance function. Finally, the formulated mathematical models are solved by using particle swarm optimization (PSO) in order to determine the optimal operation sequence. In addition, both the effectiveness and efficiency of the proposed methodologies are tested and verified for a given workpiece that needs multi-stage operations. The key contributions of this study are the generation of the optimal operation sequence and the effective allocation of the optimal dimensional tolerance (DT) using an advanced computational intelligence algorithm with consideration for multi-stage operations.