连铸组中间包问题的MTSP模型及混合优化算法

针对连铸计划中的组中间包问题,建立了多旅行商问题(MTSP)模型,提出了一种结合启发式、k-opt邻域搜索和EDA进化的混合优化算法.该算法首先利用启发式规则确定虚拟炉次的个数,从而确定染色体编码长度,每个染色体代表一种中包组合方案,然后设计了基于概率矩阵模型的EDA进化算法对染色体进行全局寻优,并使用k-opt邻域搜索进行局部优化.EDA算法不需要设计如遗传算法(GA)那样的交叉算子,避免了交叉导致的编码非法性问题.通过对企业实际生产数据进行仿真计算,其结果表明了算法具有良好的优化性能和实用性.

Hybrid Optimization Algorithm for Solving Combining Tundish MTSP Model on Continuous Casting Plan

The combining tundish problem on continuous casting plan was described and the multiple traveling salesman problem (MTSP) model was constructed. A hybrid optimization algorithm composed of the heuristic method, the k-opt neighborhood search, and the estimation of distribution algorithms (EDA) was proposed to solve the model. First, a heuristic method was used to determine the counts of dummy furnace which were involved in chromosome code to fix on the code length. Each chromosome presented a scheme of combining tundish and then the probability matrix model of the EDA was designed to optimize chromosome globally. Moreover, the k-opt was used as local search strategy. Unlike genetic algorithm(GA), the EDA had no crossover operator, therefore the illegal code resulted from crossover operator was avoided. Simulation results on the real production data indicated that the proposed algorithm had fairly good performance and utility.