Solving Last-Mile Logistics Problem in Spatiotemporal Crowdsourcing via Role Awareness With Adaptive Clustering

Last-mile logistics is a crucial phase of online commodity trades. In last-mile logistics, one of the critical problems is to reasonably assign couriers to distribute the products in time in order to ensure the quality of service, especially for fresh produce. The last-mile assignment problem (LMAP)...

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Vydané v:	IEEE transactions on computational social systems Ročník 8; číslo 3; s. 668 - 681
Hlavní autori:	Huang, Baoying, Zhu, Haibin, Liu, Dongning, Wu, Naiqi, Qiao, Yan, Jiang, Qian
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Piscataway IEEE 01.06.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Adaptive collaboration Clustering Collaboration Crowdsourcing group role assignment (GRA) IBM ILOG CPLEX optimization (CPLEX) package Insulation k-means++ method last-mile logistics Logistics Operations research Optimization Quality of service role-based collaboration (RBC) spatiotemporal crowdsourcing Spatiotemporal phenomena Task analysis
ISSN:	2329-924X, 2373-7476
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Abstract	Last-mile logistics is a crucial phase of online commodity trades. In last-mile logistics, one of the critical problems is to reasonably assign couriers to distribute the products in time in order to ensure the quality of service, especially for fresh produce. The last-mile assignment problem (LMAP) for fresh produce poses a challenge on traditional logistics since fresh produce is difficult to preserve. This article formalizes the LMAP for fresh produce via the group role assignment framework and proposes a role awareness method by using adaptive clustering in spatiotemporal crowdsourcing based on task granularity. The formalization of LMAP makes it easy to find a solution using the IBM ILOG CPLEX optimization package (CPLEX). The proposed method allows one to take the time and space factor into consideration, helps spatiotemporal crowdsourcing assign couriers for efficient delivering daily orders, and improves the quality of service in last-mile logistics. It is verified by simulation experiments. The experimental results demonstrate the practicability of the proposed solutions in this article.
AbstractList	Last-mile logistics is a crucial phase of online commodity trades. In last-mile logistics, one of the critical problems is to reasonably assign couriers to distribute the products in time in order to ensure the quality of service, especially for fresh produce. The last-mile assignment problem (LMAP) for fresh produce poses a challenge on traditional logistics since fresh produce is difficult to preserve. This article formalizes the LMAP for fresh produce via the group role assignment framework and proposes a role awareness method by using adaptive clustering in spatiotemporal crowdsourcing based on task granularity. The formalization of LMAP makes it easy to find a solution using the IBM ILOG CPLEX optimization package (CPLEX). The proposed method allows one to take the time and space factor into consideration, helps spatiotemporal crowdsourcing assign couriers for efficient delivering daily orders, and improves the quality of service in last-mile logistics. It is verified by simulation experiments. The experimental results demonstrate the practicability of the proposed solutions in this article.
Author	Qiao, Yan Zhu, Haibin Liu, Dongning Huang, Baoying Wu, Naiqi Jiang, Qian
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SubjectTerms	Adaptive collaboration Clustering Collaboration Crowdsourcing group role assignment (GRA) IBM ILOG CPLEX optimization (CPLEX) package Insulation k-means++ method last-mile logistics Logistics Operations research Optimization Quality of service role-based collaboration (RBC) spatiotemporal crowdsourcing Spatiotemporal phenomena Task analysis
Title	Solving Last-Mile Logistics Problem in Spatiotemporal Crowdsourcing via Role Awareness With Adaptive Clustering
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