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
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
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DOI：
10.3934/jimo.2011.7.31
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发表时间：
2011
影响因子：
1.3
通讯作者：
Tao Zhang;Yuejie Zhang;Q. Zheng;P. Pardalos
中科院分区：
文献类型：
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作者：
Tao Zhang;Yuejie Zhang;Q. Zheng;P. Pardalos
提出了基于订单生产和库存生产管理思想的钢铁制造企业生产计划管理体系。在此架构下，我们详细讨论了订单规划的过程，并建立了一个非线性整数规划模型的订单规划问题。该模型同时考虑了库存匹配和生产计划，考虑了提前/拖期惩罚总成本、交货期内拖期惩罚、生产、库存匹配和订单取消惩罚等多个目标。为了求解该非线性整数规划问题，设计了一种粒子群优化（PSO）和禁忌搜索（TS）混合算法，提出了新的启发式规则来修复不可行解，并通过仿真分析了PSO和组合算法的参数设置。本文还比较了单独使用PSO算法、单独使用TS算法以及PSO/TS混合算法求解三种不同订货量模型的结果。数值结果表明，混合PSO/TS算法提供了更好的解决方案，同时是计算效率。
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.