2011Advanced materials researchOpen access

Research on the Vehicle Routing Problem with Fuzzy Demands

Lian Xue, Xiao Xia Dai

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Abstract

In this paper, the vehicle routing problem with fuzzy demands is considered, and a fuzzy chance constrained programming mathematical model is established based on fuzzy possibility theory. Then fuzzy simulation and differential evolution algorithm are integrated to design a hybrid intelligent algorithm to solve the fuzzy vehicle routing model. Moreover, under the target that the total driving distance of vehicles is the shortest, the influence of the decision-maker’s preference on the final objective of the problem is discussed using the method of stochastic simulation, and the rational range of the preference number is obtained.

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What this paper is about

In this paper, the vehicle routing problem with fuzzy demands is considered, and a fuzzy chance constrained programming mathematical model is established based on fuzzy possibility theory. Then fuzzy simulation and differential evolution algorithm are integrated to design a hybrid intelligent algorithm to solve the fuzzy vehicle routing model. Moreover, under the target that the total driving distance of vehicles is the shortest, the influence of the decision-maker’s preference on the final objective of the problem is discussed using the method of stochastic simulation, and the rational range of the preference number is obtained.

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Available abstract

In this paper, the vehicle routing problem with fuzzy demands is considered, and a fuzzy chance constrained programming mathematical model is established based on fuzzy possibility theory. Then fuzzy simulation and differential evolution algorithm are integrated to design a hybrid intelligent algorithm to solve the fuzzy vehicle routing model. Moreover, under the target that the total driving distance of vehicles is the shortest, the influence of the decision-maker’s preference on the final objective of the problem is discussed using the method of stochastic simulation, and the rational range of the preference number is obtained.

Key concepts: Fuzzy logic, Preference, Mathematical optimization, Routing (electronic design automation), Vehicle routing problem, Computer science, Decision maker, Range (aeronautics)

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