首页> 外文期刊>Journal of industrial and management optimization >THE HOT-ROLLING BATCH SCHEDULING METHOD BASED ON THE PRIZE COLLECTING VEHICLE ROUTING PROBLEM
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THE HOT-ROLLING BATCH SCHEDULING METHOD BASED ON THE PRIZE COLLECTING VEHICLE ROUTING PROBLEM

机译:基于奖品收集车辆选拔问题的热轧批量调度方法

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摘要

This paper studies a hot-rolling batch scheduling problem, which is a challenging problem commonly arising in the iron-steel industry. This problem deals with forming steel strips into rolling units and determining the rolling sequences while minimizing changes in characteristics (e.g., width, thickness and rigidity) of all neighbor steel strips and maximizing the machine utilization. Based on technical rules used in iron-steel production practice, we formulate this problem as a prize collecting vehicle routing problem, which is a hard combinatorial multi-objective optimization problem. We develop a new heuristic approach to solve this problem by enhancing the framework of particle swarm optimization (PSO). The key features of our approach are the utilization of number mapping function, which allows the PSO algorithm to deal with discrete problems, and the employment of tabu search at the end of every PSO iteration. We investigate and measure the performance of the proposed approach using three real life datasets obtained from a well-known iron-steel production company in China. The results suggest that our approach is very efficient and effective in providing high-quality and practical solutions, and our approach outperforms traditional PSO and tabu search algorithms based on these datasets.
机译:本文研究了热轧批量调度问题,这是钢铁行业中普遍出现的具有挑战性的问题。该问题涉及将钢带形成为轧制单元并确定轧制顺序,同时使所有相邻钢带的特性(例如,宽度,厚度和刚度)的变化最小化并且使机器利用率最大化。根据钢铁生产实践中使用的技术规则,我们将此问题表述为奖品运输路线问题,这是一个很难组合的多目标优化问题。我们开发了一种新的启发式方法,通过增强粒子群优化(PSO)框架来解决此问题。我们方法的关键特征是利用数字映射功能,该功能允许PSO算法处理离散问题,并在每次PSO迭代结束时使用禁忌搜索。我们使用从中国一家著名的钢铁生产公司获得的三个现实生活数据集来调查和衡量该方法的性能。结果表明,我们的方法在提供高质量和实用的解决方案方面非常有效,并且该方法优于基于这些数据集的传统PSO和禁忌搜索算法。

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