现场数据采集实时位置推荐系统

Aris Prawisudatama, I. B. Nugraha
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引用次数: 0

摘要

实地数据收集是各国国家统计机构开展的主要活动之一。数据收集活动具有与多站点车辆路线问题(MDVRP)相似的工作流程。使用MDVRP生成预先计算的路线导致总路线成本具有较高的标准偏差。提出了利用发布/订阅模式和基于协同进化算法(coea)的MDVRP相结合的实时机制,以减少完工时间的不平等(大变化)。测试结果表明,与仅基于coea的MDVRP产生的预计算路由相比,发布/订阅模式与coea相结合产生的路由在枚举员的总路由时间中更为普遍。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Real-time location recommendation system for field data collection
Field data collection is one of the main activities performed by national statistical agencies in every country. Data collection activities have a similar workflow with Multi-Depot Vehicle Routing Problem (MDVRP). The use of MDVRP to generate pre-calculated routes resulted in total route costs with high standard deviation. The real-time mechanism by utilizing the publish/subscribe paradigm combined with MDVRP based on Cooperative Coevolution Algorithms (CoEAs) is proposed to reduce the inequality (large variation) of the completion time. The test results show that routes produced by the combination of publish/subscribe paradigm and CoEAs are more prevalent in enumerator's total route times compared with the pre-calculated routes produced by MDVRP based on CoEAs only.
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