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Genetic algorithms for a supply management problem: MIP-recombination vs greedy decoder
Authors:P Borisovsky  A Dolgui  A Eremeev
Institution:1. Omsk State Technical University, Prospect Mira 11, 644050 Omsk, Russia;2. Ecole des Mines de Saint Etienne, 158, Cours Fauriel 42023 Saint Etienne cedex 2, France;3. Omsk Branch of Sobolev Institute of Mathematics, Siberian Branch of Russian Academy of Sciences, Pevtsov St., 13, 644099, Omsk, Russia
Abstract:Two variants of genetic algorithm (GA) for solving the Supply Management Problem with Lower-Bounded Demands (SMPLD) are proposed and experimentally tested. The SMPLD problem consists in planning the shipments from a set of suppliers to a set of customers minimizing the total cost, given lower and upper bounds on shipment sizes, lower-bounded consumption and linear costs for opened deliveries. The first variant of GA uses the standard binary representation of solutions and a new recombination operator based on the mixed integer programming (MIP) techniques. The second GA is based on the permutation representation and a greedy decoder. Our experiments indicate that the GA with MIP-recombination compares favorably to the other GA and to the MIP-solver CPLEX 9.0 in terms of cost of obtained solutions. The GA based on greedy decoder is shown to be the most robust in finding feasible solutions.
Keywords:Genetic algorithms  Integer programming  Recombination operators  Supply chain management
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