考虑订单组批特性的特种铝锭组炉优化

Furnace-grouping optimization with order-grouping for special aluminum ingots

中文关键词  组炉优化  订单组批  粒子群算法  局部聚性算子  特种铝铸锭

英文关键词  Furnace-grouping optimization  order-grouping  particle swarm optimization  locally convergent operator  special aluminum ingots

中文摘要

      铝锭熔炼是特种铝合金生产的首道工序，直接影响后面工序的生产效率和产成品质量。针对熔炼过程中，由于产品种类多样以及批量大小不一导致的制定组炉计划效率低以及组炉结果不优等问题，本文考虑熔炼炉工艺、设备特点以及订单组批规则等约束条件，建立以最小炉次数和最大订单铸锭占用比例为目标的组炉优化模型。分析该模型的特点，采用以订单分配百分比为决策变量的实数编码规则。提出一种基于冯诺伊曼拓扑结构的粒子群算法对其进行求解，并融入局部聚性算子改善算法寻优能力。设计基于真实生产数据的仿真实验，实验结果说明该模型和所提出的求解算法能够有效地解决特种铝锭组炉优化问题，符合企业实际需求。

英文摘要

      In special aluminum alloy production, smelting for aluminum ingots is the first process that affects production efficiency and product quality in subsequent processes directly. To solve the problems that furnace-grouping plan is made inefficiently and furnace-grouping results are not optimal in the smelting process because of product variety and different batch size, furnace-grouping optimization model that is formulated with two objectives of minimizing the number of smelting batch and maximizing occupation ratio of ingots in orders is built with some constraints such as capacity of melting furnace and order-grouping rules in this paper. According to the characteristic of the model, real number coding rule is employed taking the percentage of order allocation as decision variable. A novel Particle Swarm Optimization algorithm is proposed with Von Neumann Topology to solve this optimization model, and locally convergent operator is employed to improve optimizing capacity in this algorithm. The simulation experiment is designed on the basis of the actual data in production. The experiment results show that this optimization model and the proposed algorithm can solve the furnace-grouping optimization problem for special aluminum ingots and meets the requirements of enterprises.