CAJ | 학술논문

多车场多车型多任务的车辆调度优化是城市配送中的典型问题.针对该问题从空驶成本、运输成本和时间成本三个维度构建了一个VRP的数学模型,并采用自适应多态蚁群算法对模型加以求解.通过实例仿真,将仿真优化结果与未优化的随机结果进行了比较.结果发现优化后的成本比未优化的成本低,并且证明了对多车场多车型多任务的VRP模型进行优化非常必要.
다차장다차형다임무적차량조도우화시성시배송중적전형문제.침대해문제종공사성본、운수성본화시간성본삼개유도구건료일개VRP적수학모형,병채용자괄응다태의군산법대모형가이구해.통과실례방진,장방진우화결과여미우화적수궤결과진행료비교.결과발현우화후적성본비미우화적성본저,병차증명료대다차장다차형다임무적VRP모형진행우화비상필요.
The multi-depot and multi-task and multi-type vehicle routing problem is the typical problem in city distribution. For this issue, a VRP model is constructed based on deadheading cost, transport cost and time cost. To solve this mathematical model, a self-Adaptive and Polymorphic Ant Colony Algorithm(APACA)has been introduced. A case study is presented to compare the results based on APACA with that under stochastic condition.