An interval type-2 fuzzy model for Vehicle Routing Problems in supply chains

Zarandi, M. H. Fazel; Kalhori, M. Rostam Niakan

doi:10.1109/nafips.2012.6290984

Cited by 1 publication

(1 citation statement)

References 27 publications

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“…The model is developed by using change-constraint program model with credibility measurement. Zarandi and Kalhori [27] presented a vehicle routing problem in which parameters (demand, service time, service cost) and continuous decision variables are assumed to be interval type-2 fuzzy sets; genetic algorithm is applied to solve the problem. Belmecheri et al (2012) proposed a particle swarm optimization approach using a Fuzzy Logic Controller to solve a Vehicle Routing Problem.…”

Section: Literature Reviewmentioning

confidence: 99%

A fuzzy milp-model for the optimization of vehicle routing problem

Björk

Mezei

2014

Journal of Intelligent &Amp; Fuzzy Systems

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In this article, a novel model for the solution of a fuzzy vehicle routing problem is presented. The model originates from a crisp MILP (Mixed Integer Linear Programming) model previously presented on a conference. This work is motivated by a business context of timber transportation. Within this context, uncertainties arise from the fact that the distances and times between pickup points are inherently fuzzy. The decisions to be made are routing decisions, truck assignment and the determination of the pickup order for a set of loads and available trucks. The paper also presents briefly how the model is implemented in the Microsoft Excel environment, utilizing the LP-solve freeware as the optimization engine. The model is also illustrated with a numerical example. To the authors knowledge, there are no previously reported vehicle routing MILP models, where the times and distances are allowed to be fuzzy numbers.

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Section: Literature Reviewmentioning

confidence: 99%