为了研究轧钢库区货车入库高效调度和无人天车作业合理分配协同优化问题,针对轧钢成品库区产品多品种小批量、出入库频繁等特点,建立了以订单服务时间最小为目标的整数规划模型,然后通过仿真试验对天车分配规则进行学习,并在不同订单规模下对经验规则、遗传算法和自适应遗传算法3种调度方法进行了对比试验。试验结果和现场验证均表明在各个订单规模下自适应遗传算法调度方案能高效准确地找出最优调度方案,从而为钢厂无人仓库天车调度进行指导,有效地优化了仓库物流库存管理。
Abstract
To study the collaborative scheduling of delivery vehicles and unmanned cranes in steel plant logistics warehouses,an integer programming model aiming at minimizing the service time of the cranes was established in view of the characteristics of multiple varieties and small batches of products and frequent entry and exit in cold-rolled steel product warehouse,and then the rules of cranes allocation were set up. Experiments were carried out under different order sizes by using heuristics-based scheduling,traditional genetic algorithm based scheduling and adaptive genetic algorithm (AGA) based scheduling. Experiment results shown that,compared with the other two scheduling schemes,the adaptive genetic algorithm based scheduling scheme can find out optimal scheduling results quickly and efficiently under different order sizes,which can guide the crane scheduling of unmanned warehouses in steel plants and effectively optimize the warehouse logistics inventory management.
关键词
无人仓库 /
天车分配 /
车辆调度 /
协同优化 /
自适应遗传算法
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Key words
unmanned warehouse /
crane allocation /
vehicle scheduling /
collaborative optimization /
adaptive genetic algorithm
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脚注
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基金
国家自然科学基金资助项目(61903031); 中央高校基本科研业务费资助项目(FRF-TP-20-050A2)
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