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- W110025503 abstract "Enrique Lopez Gonzalez, Miguel A. Rodriguez Fernandez, Cristina Mendana Cuervo and Raquel Florez LopezDepartment of Economy and Business ManagementUniversity of Leon24071-Leon, Spaindde{elg/mrf/cmc/rfl}@unileon.es1. ABSTRACTMany different optimisation problems can be formulatedon networks: For instance, we might be interested infinding the shortest path from one node to another,finding the cheapest way to connect all the nodes, orfinding the best way to move objects through a network.The movement of objects through a network is called aflow.In this paper we pretend to show a new approach to aclassical network optimisation problem: the transportationproblem. Firstly, allowing the vehicles that can establishroutes, and, also, permitting to manage with imprecise orvague information. We propose to use the Fuzzy SetsTheory [9]; [4]; [10], with the aim of being able to handlethe uncertainty, which is characteristic of decision-making processes in distribution problems.Moreover, to optimise the distribution network wesuggest to use a Genetic Algorithm (GA) [3]; [1]; [7]. Themain reason for this is that GAs are heuristic optimisationmethods which don’t impose restrictions to the posing ofthe problem. In this study, the algorithm is characterisedby its use of a Fuzzy Fitness Function that allows theevaluation of imprecise information.Keywords: transportation and distribution problems,shortest route problem, fuzzy sets, genetic algorithms.2. FUZZY DISTRIBUTION MODELThe information necessary is related to:• Destination or client demands. We consider amore realist representation using fuzzy sets. So, for ndestinations we could have:~ ~ ~ ~.(1)• Sources number and its supply capacity. Wesuppose that each source has a supply capacity,normally known in a strict way. So for m sources wecould have:" @default.
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- W110025503 date "1999-01-01" @default.
- W110025503 modified "2023-09-28" @default.
- W110025503 title "The distribution problem in management accounting with genetic algorithm and fuzzy sets." @default.
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