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作 者:徐静如 董红斌[1] 赵炳旭 冀若含 XU Jingru;DONG Hongbin;ZHAO Bingxu;JI Ruohan(College of Computer Science and Technology,Harbin Engineering University,Harbin 150000,China)
机构地区:[1]哈尔滨工程大学计算机科学与技术学院,黑龙江哈尔滨150000
出 处:《智能系统学报》2023年第2期293-304,共12页CAAI Transactions on Intelligent Systems
基 金:黑龙江自然科学基金项目(LH2020F023)。
摘 要:为了适应社区众包配送中投递箱或代收点收纳量有限等情况,本文提出社区服务型时空众包任务分配问题,根据用户定义的时间将物品较为实时地配送到地。针对该问题,本文通过基于角色的协作模型ECARGO(environments-classes, agents, roles, groups, objects)形式化问题,针对高资格值工人配送高价值量订单集的目标,提出基于贪婪分配的PQGR(places-qualification-based greedy)算法、基于考虑代理和角色冲突的团队多角色分配方法的PQGM(places-qualification-based GMAC)算法以及进一步缩短运行时间的改进PQGM算法。数据处理和量化方面,提出基于核密度聚类的新型角色感知方法以实现任务的有效划分,提出基于学习遗忘曲线的代理地点资格值多阶段量化模型,实现代理地点资格值的在线学习和自适应更新。最后,本文在gMission数据集和合成数据集上进行实验,验证了算法的有效性和效率。In order to adapt to the limited amount of delivery boxes or collection points in Community Crowdsourcing,this paper proposes Community Service-oriented Spatiotemperal Crowdsourcing(CSSC)task allocation problem.According to the user-defined time will be relatively real-time delivery of items to the ground.To solve this problem,this paper proposes a PQGR algorithm based on greedy allocation,a PQGM algorithm based on GMRACRA method and a PQGMA algorithm based on further shortening the running time,aiming at the goal of delivering high-value order sets by workers with high qualification value,through the formalization problem of E-CARGO based on role cooperation model.In terms of data processing and quantification,a new role perception method based on kernel density clustering is proposed to realize effective task division.Based on learning and forgetting curve,A agent’s Places-Qualification-based Multistage Quantitative Model is proposed to realize online learning and adaptive updating of agent location qualification value.Finally,experiments on gMission dataset and synthetic dataset are carried out to verify the effectiveness and efficiency of the algorithm.
关 键 词:社区服务型时空众包 任务分配 E-CARGO 基于角色协作 核密度聚类 角色感知 学习遗忘曲线 地点资格值多阶段量化
分 类 号:TP311[自动化与计算机技术—计算机软件与理论]
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