دانلود رایگان مقاله انگلیسی توزیع سود در مسائل مسیریابی خودروهای مرکزی متعدد - الزویر 2017

ABSTRACT

A collaborative multiple-center vehicle routing problem (CMCVRP) is a multiconstraint combinatorial and game optimization issue containing both vehicle routing optimization and profit distribution procedures. The CMCVRP is generally used to study the logistics network structure adjustment from a non-optimal network structure to a collaborative multiple DCs network optimization structure. The optimization of CMCVR can effectively improve vehicle loading rate and reduce the crisscross transportation phenomenon. Designing a reasonable profit distribution mechanism is a critical step of CMCVR optimization. Collaboration can be organized through a negotiation process by a logistics service provider. This paper establishes an integerprogramming model that contains transportation costs among distribution centers (DCs) and vehicle routing costs in each DC to minimize the total costs of CMCVRP. A multiphase hybrid approach with clustering, dynamic programming, and heuristic algorithm is presented to solve the model formulation. The clustering procedure increases the likelihood that the solution will converge to an optimal value, between-route operations (relocate, 2-opt* exchange, and swap move) in the heuristic algorithm will improve the initial solution, and the within-route (dynamic programming) procedure will calculate a good feasible solution for each vehicle route. Both between- and within-route operations are recursively executed to find the best solution. Profit distribution plans are then established using the improved Shapley value model. Optimal sequential coalitions are selected based on strictly monotonic path, cost reduction model, and best strategy of sequential coalition selection in cooperative game theory. An empirical study in Chongqing, China suggests that the proposed approach outperforms other algorithms, and the best sequential coalition can be selected and adjusted to increase the negotiation power for network optimization of logistics distribution. Keywords: Multiple-center vehicle routing optimization; Profit distribution; Integerprogramming model; Multi-phase hybrid approach; Improved Shapley value model.

7. Conclusions

This paper studies the MCVR optimization and profit distribution problem, where vehicle routes among DCs can be optimized and adjusted from a global optimization perspective. The optimization process is organized by either LSP or existing participants in a logistics network. A multi-phase hybrid approach with clustering, dynamic programming, and heuristic algorithm is presented to optimize the multicenter network. A profit distribution method based on an improved Shapley value model is then proposed to distribute the gained profits among DCs. MCVRP optimization and profit distribution problems are interrelated, and LSP can organize the negotiation procedure to distribute cost savings from MCVRP optimization among DCs. Through the above procedures, robustness and reliability of large-scale logistics distribution network can be enhanced, and network complexity can be reduced.

The MCVRP optimization model is first constructed to optimize the total costs of nonempty coalition logistics network, followed by the multi-phase hybrid approach to solve the model. Then, profit distribution is calculated based on the improved Shapley value model among DCs from non-empty coalitions. Finally, cost reduction percentages and optimal sequential coalitions can be obtained based on the SMP theory, cost reduction model, and best sequential coalition selection strategy. The proposed approach is successfully applied to a multi-center distribution network in Chongqing City, China, and compared with other prevailing algorithms based on cooperative game theory. Results demonstrated that the proposed approach outperforms other algorithms, and the best sequential coalition can be selected. Moreover, the synergy requirement value can be adjusted to increase the negotiation power for logistics distribution network optimization.