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Xiaolei Ma

Researcher at Beihang University

Publications -  83
Citations -  8640

Xiaolei Ma is an academic researcher from Beihang University. The author has contributed to research in topics: Deep learning & Vehicle routing problem. The author has an hindex of 29, co-authored 83 publications receiving 5977 citations. Previous affiliations of Xiaolei Ma include Chinese Ministry of Public Security & University of Washington.

Papers
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A Methodology to Exploit Profit Allocation in Logistics Joint Distribution Network Optimization

TL;DR: An improved particle swarm optimization (PSO) algorithm is presented to tackle the model formulation by assigning distribution centers to distribution units, and a Shapley value model based on cooperative game theory is proposed to obtain the optimal profit allocation strategy among distribution centers from nonempty coalitions.

Developing a GPS-Based Truck Freight Performance Measure Platform

TL;DR: In this paper, the Washington State Department of Transportation (WSDOT), Transportation Northwest (TransNow) at the University of Washington (UWUW), and the Washington Trucking Associations (WTA) have partnered on a research effort to collect and analyze GPS truck data from commercial, in-vehicle, truck fleet management systems used in the central Puget Sound region.
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Vehicle routing problem based on a fuzzy customer clustering approach for logistics network optimization

TL;DR: The results indicate the approach performs very well to identify similar customer groups and incorporate individual customer's service priority into VRP.
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Location optimization of multiple distribution centers under fuzzy environment

TL;DR: Wang et al. as mentioned in this paper presented a comprehensive algorithm to address the multiple distribution center locations (MDCLs) problem using fuzzy integration and clustering approach using the improved axiomatic fuzzy set (AFS) theory.
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Customized bus route design with pickup and delivery and time windows: Model, case study and comparative analysis

TL;DR: This work develops a new type of problem scenario: Multi-Trip Multi-Pickup and Delivery Problem with Time Windows, to describe CBRDP by simultaneously optimizing the operating cost and passenger profit, where excess travel time is introduced to estimate passenger extra cost compared with taxi service.