乘盘问题的多原子退火启发式

Songguang Ho, R. Pandi, Sarat Chandra Nagavarapu, J. Dauwels
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引用次数: 1

摘要

“叫车”问题(DARP)处理用户在指定时间窗口的上下车地点之间的交通问题。本文提出了一种新的多原子退火算法(MATA)来解决拨号乘车问题。开发了两个新的局部搜索操作符(burn和reform),一个新的构造启发式和两个请求排序机制(Sorted_List和Random_List)。在各种标准DARP基准实例上进行的计算实验证明,与其他现有方法相比,MATA是一种快速的元启发式方法。在所有的实验中,MATA都证明了它能够获得高质量的解,更快的收敛,更快地获得第一可行解。与其他算法相比,MATA在60秒内获得第一可行解的速度提高了29.8 ~ 65.1%,最终解的速度提高了3.9 ~ 5.2%。
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Multi-atomic Annealing Heuristic for the Dial-a-ride Problem
Dial-a-ride problem (DARP) deals with the transportation of users between pickup and drop-off locations associated with specified time windows. This paper proposes a novel algorithm called multi-atomic annealing (MATA) to solve the dial-a-ride problem. Two new local search operators (burn and reform), a new construction heuristic and two request sequencing mechanisms (Sorted_List and Random_List) are developed. Computational experiments conducted on various standard DARP benchmark instances prove that MATA is an expeditious meta-heuristic in contrast to other existing methods. In all experiments, MATA demonstrates the capability to obtain high quality solutions, faster convergence, and quicker attainment of a first feasible solution. It is observed that MATA attains a first feasible solution 29.8 to 65.1% faster, and obtains a final solution that is 3.9 to 5.2% better, when compared to other algorithms within 60 sec.
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