A hybrid particle swarm optimization and tabu search algorithm for order planning problems of steel factories based on the Make-To-Stock and Make-To-Order management architecture

Tao Zhang; Yue-Jie Zhang; Qipeng P. Zheng; P. M. Pardalos

doi:10.3934/jimo.2011.7.31

Article Contents

2011, Volume 7, Issue 1: 31-51. Doi: 10.3934/jimo.2011.7.31

This issue Previous Article A queueing analysis of multi-purpose production facility's operations Next Article Smoothing Newton methods for symmetric cone linear complementarity problem with the Cartesian $P$/$P_0$-property

A hybrid particle swarm optimization and tabu search algorithm for order planning problems of steel factories based on the Make-To-Stock and Make-To-Order management architecture

1.
School of Information Management and Engineering, Shanghai University of Finance and Economics, Shanghai 200433
2.
School of Computer Science, Key Laboratory of Intelligent Information Processing, Fudan University, Shanghai 200433
3.
Department of Industrial and Management Systems Engineering, PO Box 6070, West Virginia University, Morgantown, WV 26505
4.
Center for Applied Optimization, Department of Industrial and Systems Engineering, University of Florida, Gainesville, FL 32611

Received: October 31, 2009

Revised: August 31, 2010

Published: December 2010

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Abstract

This paper presents the production planning management architecture for iron-steel manufacturing factories based on Make-To-Order (MTO) and Make-To-Stock (MTS) management ideas. Within this architecture, we discuss the procedures of order planning in details and construct a nonlinear integer programming model for the order planning problem. This model takes into account inventory matching and production planning simultaneously, and considers multiple objectives, such as the total cost of earliness/tardiness penalty, tardiness penalty in delivery time window, production, inventory matching and order cancelation penalty. In order to solve this nonlinear integer program, this paper designs a hybrid Particle Swarm Optimization (PSO) and Tabu Search (TS) algorithm, in which new heuristic rules to repair infeasible solutions are proposed, and then analyzes the parameter settings for PSO and the combined algorithm by simulations. This paper also compares the results of using PSO individually, TS individually, and the hybrid PSO/TS algorithm to solve the models with three different order quantities. Numerical results show that the hybrid PSO/TS algorithm provides better solutions while being computationally efficient.

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Mathematics Subject Classification: Primary: 90B30, 90C59; Secondary: 90B35.

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