Cooperation and profit allocation in two-echelon logistics joint distribution network optimization

Wang, Yong; Ma, Xiaolei; Liu, Mingwu; Гонг, Ке; Liu, Yong; Xu, Maozeng; Wang, Yinhai

doi:10.1016/j.asoc.2017.02.025

Cited by 97 publications

(53 citation statements)

References 48 publications

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“…Wang et al. () propose a profit allocation method to improve negotiation power for logistics network optimization. The proposed approach, in opposition to most previous efforts, regards the willingness of logistics participants to participate.…”

Section: A Taxonomy For the Practical Issuesmentioning

confidence: 99%

“…They concluded that some cost allocation models, such as the modiclus or SMnucleolus, could help to maintain the coalition sustainable once they consider negotiation power. Wang et al (2017) propose a profit allocation method to improve negotiation power for logistics network optimization. The proposed approach, in opposition to most previous efforts, regards the willingness of logistics participants to participate.…”

Section: Designmentioning

confidence: 99%

See 1 more Smart Citation

A survey on obstacles and difficulties of practical implementation of horizontal collaboration in logistics

Basso

D’Amours

Rönnqvist

et al. 2018

Int Trans Operational Res

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During recent years, horizontal collaboration in logistics has gained attention because of achieved potential benefits such as cost reduction, an increase in fulfillment rates, and a decrease in CO2 emissions owing to reductions in traveled distances. Successful real‐world cases, however, are rare since horizontal cooperation in logistics is not usually sustainable. This paper pays attention to this paradox of the lack of cases and discusses 16 identified practical issues that could explain this phenomenon. We propose a taxonomy composed of four categories categorizing the practical issues according to a value chain approach: design, planning and operations, market/business, and behaviors. Furthermore, we propose and discuss some measures to mitigate these problems.

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Section: A Taxonomy For the Practical Issuesmentioning

confidence: 99%

Section: Designmentioning

confidence: 99%

A survey on obstacles and difficulties of practical implementation of horizontal collaboration in logistics

Basso

D’Amours

Rönnqvist

et al. 2018

Int Trans Operational Res

View full text Add to dashboard Cite

show abstract

“…Feng et al [16] designed multiple collaboration and decision-making mechanisms for efficient logistics transportation planning. Wang et al [17] optimized two-echelon pickup and delivery networks to reduce their total operation costs by establishing collaborative alliances.…”

Section: Literature Reviewmentioning

confidence: 99%

Collaborative Mechanism for Pickup and Delivery Problems with Heterogeneous Vehicles under Time Windows

et al. 2019

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The sustainability and complexity of logistics networks come from the temporally and spatially uneven distributions of freight demand and supply. Operation strategies without considering the sustainability and complexity could dramatically increase the economic and environmental costs of logistics operations. This paper explores how the unevenly distributed demand and supply can be optimally matched through collaborations, and formulates and solves a Collaborative Pickup and Delivery Problem under Time Windows (CPDPTW) to optimize the structures of logistics networks and improve city sustainability and liverability. The CPDPTW is a three-stage framework. First, a multi-objective linear optimization model that minimizes the number of vehicles and the total cost of logistics operation is developed. Second, a composite algorithm consisting of improved k-means clustering, Demand-and-Time-based Dijkstra Algorithm (DTDA) and Improved Non-dominated Sorting Genetic Algorithm-II (INSGA-II) is devised to solve the optimization model. The clustering algorithm helps to identify the feasible initial solution to INSGA-II. Third, a method based on improved Shapley value model is proposed to obtain the collaborative alliance strategy that achieves the optimal profit allocation strategy. The proposed composite algorithm outperforms existing algorithms in minimizing terms of the total cost and number of electro-tricycles. An empirical case of Chongqing is employed to demonstrate the efficiency of the proposed mechanism for achieving optimality for logistics networks and realizing a win-win situation between suppliers and consumers.

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“…Applications in the forest transportation domain and for oil operations have been used to verify the relevance of the proposed optimization model. Wang et al [15,16] introduced collaboration as means of optimizing a single echelon multiple centers vehicle routing problem and applied an improved version of the Shapley value model to allocate profit.…”

Section: Introductionmentioning

confidence: 99%

“…Guo et al [14] used GA and PSO to study forward and reverse flows of fresh food supply chains in Shanghai and verified the validity of the model under low-carbon emission environments. Wang et al [15,16] combined an improved ant colony algorithm with GA to optimize networks through the reassignment of customers to proper logistics facilities. Alshamsi and Diabat [17] introduced a GA capable of solving complex problems with thousands continuous and binary variables, constraints, and multiple integer variables.…”

Section: Introductionmentioning

confidence: 99%

Design and Profit Allocation in Two-Echelon Heterogeneous Cooperative Logistics Network Optimization

Wang

Yuan

Assogba

et al. 2018

Journal of Advanced Transportation

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In modern supply chain, logistics companies usually operate individually and optimization researches often concentrate on solving problems related to separate networks. Consequences like the complexity of urban transportation networks and long distance deliveries or pickups and pollution are leading problems to more expenses and more complaints from environment protection organizations. A solution approach to these issues is proposed in this article and consists in the adoption of two-echelon heterogeneous cooperative logistics networks (THCLN). The optimization methodology includes the formation of cooperative coalitions, the reallocation of customers to appropriate logistics facilities, and the determination of the best profit allocation scheme. First, a mixed integer linear programing model is introduced to minimize the total operating cost of nonempty coalitions. Thus, the Genetic Algorithm (GA) and the Particle Swarm Optimization (PSO) algorithm are hybridized to propose GA-PSO heuristics. GA-PSO is employed to provide good solutions to customer clustering units' reallocation problem. In addition, a negotiation process is established based on logistics centers as coordinators. The case study of Chongqing city is conducted to verify the feasibility of THCLN in practice. The grand coalition and two heterogeneous subcoalitions are designed, and the collective profit is distributed based on cooperative game theory. The Minimum Cost Remaining Savings (MCRS) model is used to determine good allocation schemes and strictly monotonic path principles are considered to evaluate and decide the most appropriate coalition sequence. Comparisons proved the combination of GA-PSO and MCRS better as results are found closest to the core center. Therefore, the proposed approach can be implemented in real world environment, increase the reliability of urban logistics network, and allow decision makers to improve service efficiency.

show abstract

Cooperation and profit allocation in two-echelon logistics joint distribution network optimization

Cited by 97 publications

References 48 publications

A survey on obstacles and difficulties of practical implementation of horizontal collaboration in logistics

A survey on obstacles and difficulties of practical implementation of horizontal collaboration in logistics

Collaborative Mechanism for Pickup and Delivery Problems with Heterogeneous Vehicles under Time Windows

Design and Profit Allocation in Two-Echelon Heterogeneous Cooperative Logistics Network Optimization

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