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Memory-based variable neighborhood search for green vehicle routing problem with passing-by drivers: a comprehensive perspective.
Cao, Lei; Ye, Chun-Ming; Cheng, Ran; Wang, Zhen-Kun.
Afiliação
  • Cao L; School of Business, University of Shanghai for Science and Technology, Shanghai, 200093 China.
  • Ye CM; School of Business, University of Shanghai for Science and Technology, Shanghai, 200093 China.
  • Cheng R; Guangdong Key Laboratory of Brain-Inspired Intelligent Computation, Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, 518055 China.
  • Wang ZK; School of System Design and Intelligent Manufacturing, Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, 518055 China.
Complex Intell Systems ; 8(3): 2507-2525, 2022.
Article em En | MEDLINE | ID: mdl-35155081
ABSTRACT
A business delivery model with professional vehicles as well as occasional passing-by vehicles is investigated in this paper. The drivers deliver parcels from the distribution center to customers and the passing-by driver can get a certain amount of compensation in return. To give a satisfactory solution from the perspective of platform owner, customers, professional drivers, occasional drivers, and authority, a multi-layer comprehensive model is proposed. To effectively solve the proposed model, we introduce an improved variable neighborhood search (VNS) with a memory-based restart mechanism. The new algorithm is evaluated on instances derived from Solomon's benchmark and real-life beer delivery instances. Taguchi experiment is used to tune parameters in the proposed VNS, followed by component analysis and real-life experiments. Experimental results indicate that the proposed strategies are effective and the new delivery model in this paper has some advantages over traditional and single-delivery ones from the comprehensive perspectives of stakeholders in the crowdsourcing logistics system.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Revista: Complex Intell Systems Ano de publicação: 2022 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Revista: Complex Intell Systems Ano de publicação: 2022 Tipo de documento: Article