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A Graph-Based Fuzzy Evolutionary Algorithm for Solving Two-Echelon Vehicle Routing Problems
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DOI：10.1109/TEVC.2019.2911736.png)
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Abstract
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Two-echelon vehicle routing problem (2E-VRP) is a challenging problem that involves both the strategic and tactical planning decisions on both echelons. The satellite locations and the customer distribution affect the cost of different components on the second echelon, thus the possibilities of satellite-to-customer assignment complicates the problem. In this paper, we propose a graph-based fuzzy evolutionary algorithm for solving 2E-VRP. The proposed method integrates a graph-based fuzzy assignment scheme into an iteratively evolutionary learning process to minimize the total cost. To resolve the possibilities of the satellite-to-customer assignment, graph-based fuzzy operator is used to take advantage of population evolution and avoid excessive fitness evaluations of unpromising moves in different satellites. Each offspring is produced via graph-based fuzzy assignment procedure out of an assignment graph from parent individuals, and fuzzy local search procedure is used to further improve the offspring. The experimental results on the public test sets demonstrate the competitiveness of the proposed method.
Keywords:
Satellites
Routing
Vehicle routing
Sociology
Statistics
Evolutionary computation
Optimization
Assignment graph
evolutionary algorithm (EA)
fuzzy subset
two-echelon vehicle routing problem (2E-VRP)
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