The Vehicle Routing Problem (VRP) is a complex combinatorial optimization problem which can be seen as a merge of two well-known problems: the traveling sales problem (TSP) and the Bin Packing Problem (BPP). The paper will introduce the problem starting with the type time window and presented some of the prevailing strategies for solving these, focusing on the genetic learning algorithm to solve the problem depending on the fuzzy-due time which is a part of population and evaluate the system performance when mixing genetic algorithm with fuzzy logic to minimize the time consumption to explore and exploit the search space.
Keywords: Artificial intelligent, customer's preferences, fuzzy-due time, genetic algorithm, genetic fuzzy system, knowledge base system, membership function, vehicle routing problem, vehicle routing types
[...] We attempt to present a priority-based genetic algorithm to obtain a compromise solution of the model VRP Types Usually, in real world VRP, many side constraints appear some of the must important restrictions which are[6]:*Capacitated Vehicle Routing Problem(CVRP) Is a VRP in which a fixed fleet of delivering vehicles of uniform capacity must service known customer demands for a single commodity from a common depot at minimum transit cost. * Multiple Depot Vehicle Routing Problem(MDVRP) The vendor uses many depots to supply the customers. [...]
[...] The best solution for the problem occurred when used the large size of population for both methods that are used because was extending the search space Refrences J.Berger, S.Martin & R.Begin," A hybrid genetic algorithm for vehicle routing problem with time window", proceeding of 12th Biennual conference of the Canadian society for computational studies of intelligence, ,pp.114-127, Berlin,1998. D.Goldberg,”Genetic algorithms”,Addition Wesly,1989. D.E.Goldberg,”Genetic algorithms in search ,optimization & machine learning",3rd impression, India L.S.Hasan & S.A.M.Rizvi," Development of knowledge base using fuzzy logic and genetic algorithm", International Joint journal conference in engineering IJJCE,Vol.1,May 2009. [...]
[...] as perception, reasoning, and learning and develops systems to perform those tasks. The subject of artificial intelligence spans a wide horizon. It deals with the various kinds of knowledge representation schemes, different techniques of intelligence search, various methods for resolving uncertainty of data and knowledge, different schemes for automated machine learning and many others. There are mainly two different directions of methods in AI. The first group is more concerned with the application of logic theories and the second one tries to understand human thinking and rebuilds at least a single aspect of the vague understanding of the human way of information processing with some artificial intelligence methods[9]. [...]
[...] Algorithm Begin Compute initial chromosome Judge=0 Generate random number(1,node) & store in xi Compute fuzzy-due time ti I=0 While i [...]
[...] The decision version of this problem is conceptually equivalent to a VRP model in which all edge costs are taken to be zero (so that all feasible solutions have the same cost) VRP Solutions J. Berger et al.[1] proposed a method based on the hybridization of a genetic algorithm with well-known construction heuristics. The initial population is created with nearest neighbor heuristic inspired from Solomon(1987). The fitness values of the individuals are based on the number of routes and total distance of the corresponding solution. The crossover operator combines iteratively various routes r1 of parent solution p1 with a subset of customers. Formed by r2 nearest-neighbor routes from parent solution p2. [...]
APA Style reference
For your bibliographyOnline reading
with our online readerContent validated
by our reading committee
