Cooperation and profit allocation for two-echelon logistics pickup and delivery problems with state–space–time networks

Cooperation is a powerful strategy to reduce the costs of pickup and delivery services in the conventional two-echelon pickup and delivery problems (2E-PDPs). This study develops a cooperative 2E-PDP with state–space–time network (C2E-PDPSST) and incorporates vehicle routing problem and profit alloc...

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Vydané v:	Applied soft computing Ročník 109; s. 107528
Hlavní autori:	Wang, Yong, Zhang, Shuanglu, Guan, Xiangyang, Fan, Jianxin, Wang, Haizhong, Liu, Yong
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier B.V 01.09.2021
Predmet:	Minimum cost remaining savings State–space–time networks Time-dependent forward dynamic programming algorithm Transportation resource sharing Two-echelon pickup and delivery problem State–space–time networks Two-echelon pickup and delivery problem Minimum cost remaining savings Transportation resource sharing Time-dependent forward dynamic programming algorithm
ISSN:	1568-4946, 1872-9681
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Abstract	Cooperation is a powerful strategy to reduce the costs of pickup and delivery services in the conventional two-echelon pickup and delivery problems (2E-PDPs). This study develops a cooperative 2E-PDP with state–space–time network (C2E-PDPSST) and incorporates vehicle routing problem and profit allocation optimization. Transportation resource sharing (trucks/vehicles) is presented as a major cooperative strategy in C2E-PDPSST to reduce the number of vehicles and improve resource utilization. A bi-objective mixed integer model is proposed to minimize total operating costs and number of vehicles and is formulated based on state–space–time networks. A novel methodology, which combines the time-dependent forward dynamic programming algorithm and a hybrid heuristic algorithm comprising modified k-means clustering algorithm and improved multi-objective particle swarm optimization (IMOPSO) algorithm, is designed for solving C2E-PDPSST. The clustering process speeds up the solution by reducing the computational complexity. The IMOPSO algorithm combines inter- and intra-route operations to find the Pareto optimal solutions with predefined iteration and termination rules. The inter-route operations will improve the population diversity, while the intra-route operations will generate an optimal solution for each vehicle route. Thus, an effective combination of local and global solution search can be achieved. Profit allocation schemes are investigated using the criteria of minimum cost remaining savings. Our results on benchmark instances show that the proposed IMOPSO has better computational performance than the conventional MOPSO and multi-objective genetic algorithm. A case study based on the realistic logistics network in Chengdu City, China is conducted for validation. The proposed methodology considering state–space–time network performs better in terms of reducing traveling costs, and improving the flexibility and efficiency of the entire network. •Study cooperation and profit allocation strategies in 2E-PDPs with SST networks.•Propose a bi-objective mixed integer model for the C2E-PDPSST.•Devise a solution approach that combines TDDP, k-means and IMOPSO algorithms.•Explore shared truck types and cooperative sequences in the C2E-PDPSST.•Conduct a real-world case study to test the applicability of proposed approach.
AbstractList	Cooperation is a powerful strategy to reduce the costs of pickup and delivery services in the conventional two-echelon pickup and delivery problems (2E-PDPs). This study develops a cooperative 2E-PDP with state–space–time network (C2E-PDPSST) and incorporates vehicle routing problem and profit allocation optimization. Transportation resource sharing (trucks/vehicles) is presented as a major cooperative strategy in C2E-PDPSST to reduce the number of vehicles and improve resource utilization. A bi-objective mixed integer model is proposed to minimize total operating costs and number of vehicles and is formulated based on state–space–time networks. A novel methodology, which combines the time-dependent forward dynamic programming algorithm and a hybrid heuristic algorithm comprising modified k-means clustering algorithm and improved multi-objective particle swarm optimization (IMOPSO) algorithm, is designed for solving C2E-PDPSST. The clustering process speeds up the solution by reducing the computational complexity. The IMOPSO algorithm combines inter- and intra-route operations to find the Pareto optimal solutions with predefined iteration and termination rules. The inter-route operations will improve the population diversity, while the intra-route operations will generate an optimal solution for each vehicle route. Thus, an effective combination of local and global solution search can be achieved. Profit allocation schemes are investigated using the criteria of minimum cost remaining savings. Our results on benchmark instances show that the proposed IMOPSO has better computational performance than the conventional MOPSO and multi-objective genetic algorithm. A case study based on the realistic logistics network in Chengdu City, China is conducted for validation. The proposed methodology considering state–space–time network performs better in terms of reducing traveling costs, and improving the flexibility and efficiency of the entire network. •Study cooperation and profit allocation strategies in 2E-PDPs with SST networks.•Propose a bi-objective mixed integer model for the C2E-PDPSST.•Devise a solution approach that combines TDDP, k-means and IMOPSO algorithms.•Explore shared truck types and cooperative sequences in the C2E-PDPSST.•Conduct a real-world case study to test the applicability of proposed approach.
ArticleNumber	107528
Author	Wang, Haizhong Fan, Jianxin Liu, Yong Guan, Xiangyang Wang, Yong Zhang, Shuanglu
Author_xml	– sequence: 1 givenname: Yong orcidid: 0000-0001-7511-8888 surname: Wang fullname: Wang, Yong email: yongwx@cqjtu.edu.cn organization: School of Economics and Management, Chongqing Jiaotong University, Chongqing 400074, China – sequence: 2 givenname: Shuanglu surname: Zhang fullname: Zhang, Shuanglu email: shuangluzh6@163.com organization: School of Management, Shanghai University, Shanghai 200444, China – sequence: 3 givenname: Xiangyang surname: Guan fullname: Guan, Xiangyang email: guanxy@uw.edu organization: Department of Civil and Environmental Engineering, University of Washington, Seattle, WA 98195, USA – sequence: 4 givenname: Jianxin surname: Fan fullname: Fan, Jianxin email: jxfanw@cqjtu.edu.cn organization: School of River and Ocean Engineering, Chongqing Jiaotong University, Chongqing 400074, China – sequence: 5 givenname: Haizhong surname: Wang fullname: Wang, Haizhong email: Haizhong.Wang@oregonstate.edu organization: School of Civil and Construction Engineering, Oregon State University, Corvallis, OR, 97330, USA – sequence: 6 givenname: Yong surname: Liu fullname: Liu, Yong email: liuyongcs@cqjtu.edu.cn organization: School of Economics and Management, Chongqing Jiaotong University, Chongqing 400074, China
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Keywords	State–space–time networks Two-echelon pickup and delivery problem Minimum cost remaining savings Transportation resource sharing Time-dependent forward dynamic programming algorithm
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Snippet	Cooperation is a powerful strategy to reduce the costs of pickup and delivery services in the conventional two-echelon pickup and delivery problems (2E-PDPs)....
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SubjectTerms	Minimum cost remaining savings State–space–time networks Time-dependent forward dynamic programming algorithm Transportation resource sharing Two-echelon pickup and delivery problem
Title	Cooperation and profit allocation for two-echelon logistics pickup and delivery problems with state–space–time networks
URI	https://dx.doi.org/10.1016/j.asoc.2021.107528
Volume	109
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