CAJ | 학술논문

将热轧批量计划编制问题归结为奖金收集的车辆路径问题,按照热轧工艺规程,同时考虑轧制计划中钢板宽度、厚度的反跳约束,设计了反跳惩罚表,提出一种多目标数学规划模型,即最小化轧制计划数、最小化惩罚值、最小化未轧制板坯数.应用粒子群(PSO)算法进行求解,对PSO算法的求解过程进行了改进,使其适用于热轧批量计划问题,在实验中测试了算法的参数(惯性权重、加速因子)对解的影响,并进行了实验分析,获得了满意的结果.实验表明,经过改进的PSO算法所获得的最优解以及平均使用时间上都是有效的.
장열알비량계화편제문제귀결위장금수집적차량로경문제,안조열알공예규정,동시고필알제계화중강판관도、후도적반도약속,설계료반도징벌표,제출일충다목표수학규화모형,즉최소화알제계화수、최소화징벌치、최소화미알제판배수.응용입자군(PSO)산법진행구해,대PSO산법적구해과정진행료개진,사기괄용우열알비량계화문제,재실험중측시료산법적삼수(관성권중、가속인자)대해적영향,병진행료실험분석,획득료만의적결과.실험표명,경과개진적PSO산법소획득적최우해이급평균사용시간상도시유효적.
This paper formulates the hot-milling batch planning problem as a prize collecting vehicle routing problem (PCVRP). According to the hot-milling technics rules, this paper considers the inverse bounce of the width and the thickness of the steel strips and presents a multi-objective mathematics programming model. The objectives include minimizing the number of hot-milling plans, minimizing the total penalties, and minimizing the number of slabs without being milled. To solve this problem, an improved particle swarm optimization (PSO) algorithm is presented. This paper also analyzes the effect of parameter settings on the swarm convergence for the hot strip mill batch planning problem. The computational results with the best parameters show that both the solution obtained by this algorithm and the average used time are effective.