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The stochastic VRP differs from the VRP by the introduction of some element of variability within the system in question. A key difficulty in optimization under uncertainty is in dealing with an uncertainty space that is huge and frequently leads to a very large scale optimization models. Also many decisions, in real life problems are based on information which is both fuzzily imprecise and probabilistically uncertain. Therefore, it is necessary to develop routing and scheduling tools which could handle the uncertainty in every parameter. The main focus of this work is to investigate the variants of an uncertain VRP when general VRP solutions cause some violation when parameters are contaminated with perturbation. This work explores Genetic Algorithm with Fuzzy Logic, a rare but a promising combination, to discover some interesting results not only in single objective VRP but also in multiobjective stochastic VRPs.This book is an attempt to ignite interest in researcher in the field of hard combinatorial optimization problems by introducing new strategies in which a multiobjective stochastic VRP can be effectively handled to give reliable multiple solutions and set of routes.
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