Two-echelon logistics delivery and pickup network optimization based on integrated cooperation and transportation fleet sharing

Wang, Yong; Peng, Shouguo; Xu, Chengcheng; Assogba, Kevin; Wang, Haizhong; Xu, Maozeng; Wang, Yinhai

doi:10.1016/j.eswa.2018.06.037

Cited by 40 publications

(25 citation statements)

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“…A game theoretic approach has been developed to maximize productivity while ensuring fair profit allocation in collaborative multi-echelon supply chains [52]. Wang et al [53] proposed a cooperative strategy to minimize carbon emissions in pickup and delivery processes and designed a fair method of profit distribution based on cooperative game theory to stabilize alliances. In comparison with the aforementioned studies in the domain, the main contributions of this paper are listed in the following aspects: (1) it considers different types of resource sharing schemes, including sharing both within the same DC and across DCs to optimize a collaborative multicenter delivery network; (2) it establishes a bi-objective optimization model for the total operational cost and number of vehicles minimization based on resource sharing in multiple service periods; (3) it employs an AGPSO based on k-means customer clustering, which incorporates an efficient selective endowment mechanism and thus performs well in global and local research; and (4) it implements a real-world case study to evaluate the applicability of the proposed CMCLDN-RS model and approach, which contributes empirically to the literature on collaborative multi-echelon multi-period logistics network optimization, and then lays a foundation for the construction and sustainable development of intelligent transport systems.…”

Section: Profit Allocation In Collaborative Logistics Networkmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

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Collaborative multicenter logistics delivery network optimization with resource sharing

et al. 2020

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Collaboration among logistics facilities in a multicenter logistics delivery network can significantly improve the utilization of logistics resources through resource sharing including logistics facilities, vehicles, and customer services. This study proposes and tests different resource sharing schemes to solve the optimization problem of a collaborative multicenter logistics delivery network based on resource sharing (CMCLDN-RS). The CMCLDN-RS problem aims to establish a collaborative mechanism of allocating logistics resources in a manner that improves the operational efficiency of a logistics network. A bi-objective optimization model is proposed with consideration of various resource sharing schemes in multiple service periods to minimize the total cost and number of vehicles. An adaptive grid particle swarm optimization (AGPSO) algorithm based on customer clustering is devised to solve the CMCLDN-RS problem and find Pareto optimal solutions. An effective elite iteration and selective endowment mechanism is designed for the algorithm to combine global and local search to improve search capabilities. The solution of CMCLDN-RS guarantees that cost savings are fairly allocated to the collaborative participants through a suitable profit allocation model. Compared with the computation performance of the existing nondominated sorting genetic algorithm-II and multi-objective evolutionary algorithm, AGPSO is more computationally efficient. An empirical case study in Chengdu, China suggests that the proposed collaborative mechanism with resource sharing can effectively reduce total operational costs and number of vehicles, thereby enhancing the operational efficiency of the logistics network.

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Section: Profit Allocation In Collaborative Logistics Networkmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Collaborative multicenter logistics delivery network optimization with resource sharing

et al. 2020

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“…Wang et al [23] used a mixed-integer linear programming model to minimize the total operating cost of nonempty coalitions of two-echelon heterogeneous cooperative logistics networks and the GA-PSO algorithm to solve customer clustering units' reallocation problem. Wang et al [24] established a linear mathematical model to minimize the total cost of the two-echelon logistics delivery and pickup network (2E-LDPN) and solved it by combining an improved PSO algorithm with the ant colony optimization algorithm to create hybrid metaheuristics. In the above studies, different heuristics are used to solve the problem in combination with various models.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In formula (24), C 0 (i) indicates that the participant i decides not to join the cooperation. In the next section, the cost reduction percentages η(i, π, u) are used to explain the SMP, a sequence wherein the cost reduction percentages for all committed participants monotonically increase with each new member.…”

Section: Mps Strategymentioning

confidence: 99%

Collaborative Multidepot Petrol Station Replenishment Problem with Multicompartments and Time Window Assignment

Wang

et al. 2020

Journal of Advanced Transportation

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Energy supply is an important system that affects the overall efficiency of urban transportation. To improve the system operational efficiency and reduce costs, we formulate and solve a collaborative multidepot petrol station replenishment problem with multicompartments and time window assignment by establishing a mixed-integer linear programming model. The hybrid heuristic algorithm composed of genetic algorithm and particle swarm optimization is used as a solution, and then the Shapley value method is applied to analyze the profit allocation of each petrol depot under different coalitions. The optimal membership sequence of the cooperation is determined according to the strict monotone path. To analyze and verify the effectiveness of the proposed method, a regional petrol supply network in Chongqing city in China is investigated. Through cooperation between petrol depots in the supply network, the utilization of customer clustering, time window coordination, and distribution truck sharing can significantly reduce the total operation costs and improve the efficiency of urban transportation energy supply. This approach can provide theoretical support for relevant government departments and enterprises to make optimal decisions. The implementation of the joint distribution of energy can promote the sustainable development of urban transportation.

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“…Research by Niknejad and Petrovic (2014) studied the inventory control and production plan optimization of integrated reverse logistics network, and the forward path, repair and remanufacturing and two recovery paths and processing methods of integrated reverse logistics network are studied. Research by Wang, Peng et al (2018) showed through the establishment of cooperative alliance, the optimization of secondary logistics distribution and loading network can be effectively realized. With the coordination of existing network or logistics facilities of logistics service providers, the high operating cost of cross-long-distance transportation can be reduced by inclusive restructuring of the entire network.…”

Section: Network Competition and Cooperation Or Coordinationmentioning

confidence: 99%

Genetic algorithm for the cargo shunting cooperation between two hub-and-spoke logistics networks

Huang

2019

JIEM

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Purpose: The overstocked goods flow in the hub of hub-and-spoke logistics network should be disposed of in time, to reduce delay loss and improve the utilization rate of logistics network resources. The problem we need to solve is to let logistics network cooperate by sharing network resources to shunt goods from one hub-and-spoke network to another hub-and-spoke network.Design/methodology/approach: This paper proposes the hub shunting cooperation between two hub-and-spoke networks. Firstly, a hybrid integer programming model was established to describe the problem, and then a multi-layer genetic algorithm was designed to solve it and two hub-and-spoke networks are expressed by different gene segments encoded by genes. The network data of two third-party logistics companies in southern and northern China are used for example analysis at the last step. Findings: The hub-and-spoke networks of the two companies were constructed simultaneously. The transfer cost coefficient between two networks and the volume of cargo flow in the network have an impact on the computation of hubs that needed to be shunt and the corresponding cooperation hubs in the other network.Originality/value: Previous researches on hub-and-spoke logistics network focus on one logistics network, while we study the cooperation and interaction between two hub-and-spoke networks. It shows that two hub-and-spoke network can cooperate across the network to shunt the goods in the hub and improve the operation efficiency of the logistics network.

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Two-echelon logistics delivery and pickup network optimization based on integrated cooperation and transportation fleet sharing

Cited by 40 publications

References 50 publications

Collaborative multicenter logistics delivery network optimization with resource sharing

Collaborative multicenter logistics delivery network optimization with resource sharing

Collaborative Multidepot Petrol Station Replenishment Problem with Multicompartments and Time Window Assignment

Genetic algorithm for the cargo shunting cooperation between two hub-and-spoke logistics networks

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