基于改进教与学算法的铸造企业订单发退货计划调度研究

Scheduling of Order Delivery and Return of Foundry Enterprises Based on Improved Teaching and Learning Algorithm

针对现有的铸造企业订单发退货过程中人工调度运输导致的成本虚高、运输效率低、车载利用率低等问题,开展了铸造企业订单发退货调度研究。建立以运输成本、交货期、车载利用率为指标的多目标订单发退货运输数学模型;提出了双演化淘汰制教与学算法(DP-TLBO),并设计了连续的编码方式。通过引入正弦加速因子的反向学习来增强局部搜索能力,防止在搜索后期陷入局部最优;采用教与学算法和贫富优化算法的双演化机制竞争淘汰调度方案。其次,进行多个测试函数的试验表明,双演化淘汰制教与学算法拥有更好的性能。最后,通过针对两个不同铸造企业的多个规模仿真试验结果表明,对比人工调度运输的方式,使用本课题提出的数学模型和算法求解时,可以获得更低的成本、更高的运输效率及车载利用率。

In view of falsely high costs,low transportation efficiency and low vehicle utilization caused by manual dispatch of transportation in the process of order delivery and returning of existing foundry enterprises,the order dispatch of foundry enterprises was investigated.A mathematical model of multi-target order dispatch and return transportation with transportation cost,delivery date,and vehicle utilization as indicators was established.A double evolutionary elimination system of teaching and learning algorithm(Diverse poor and rich algorithm-Teaching and learning algorithm,DP-TLBO)was proposed,and a continuous coding method was designed.The reverse learning of the sine acceleration factor was introduced to enhance the local search ability and prevent from falling into the local optimum in the later stage of the search.The dual evolution mechanism of teaching and learning algorithms as well as rich and poor optimization algorithms was adopted to compete and eliminate scheduling schemes.The results of experiments with multiple test functions indicate that the double-evolution elimination teaching and learning algorithm has desirable performance.Finally,the results of multiple scale simulation experiments for two different casting companies reveal that the mathematical model and algorithm proposed lead to the lower costs,higher transportation efficiency and higher vehicle utilization compared with manual transportation methods.