The shortest path routing problem is a multiobjective nonlinear optimization problem with a set of constraints. This problem has been addressed by considering delay and cost objectives simultaneously and as a weighted...
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The shortest path routing problem is a multiobjective nonlinear optimization problem with a set of constraints. This problem has been addressed by considering delay and cost objectives simultaneously and as a weighted sum of both objectives for comparison. Multiobjective evolutionary algorithms can find multiple pareto-optimal solutions in one single run and this ability makes them attractive for solving problems with multiple and conflicting objectives. This paper uses an elitist multiobjective evolutionary algorithm based on the nondominated sorting genetic algorithm (NSGA), for solving the dynamic shortest path routing problem in computer networks. A priority-basedencoding scheme is proposed for population initialization. Elitism ensures that the best solution does not deteriorate in the succeeding generations. Results for a sample test network have been presented to demonstrate the capabilities of the proposed approach to generate well-distributed pareto-optimal solutions of dynamic routing problem in one single run. The results obtained by NSGA are compared with single objective weighting factor method for which genetic algorithm (GA) is applied. (C) 2011 Elsevier Ltd. All rights reserved.
This study investigates the reverse logistics network design problem, including collection/inspection, recovery and disposal centers that a mixed integer linear programming model is considered. In this network, return...
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This study investigates the reverse logistics network design problem, including collection/inspection, recovery and disposal centers that a mixed integer linear programming model is considered. In this network, returned products from customer zones are collected in collection/inspection centers and after quality inspection, and also after separation, recoverable products are shipped to recovery centers and scrapped ones are transported to disposal centers. NP-hardness of this problem is proved in many papers, so a novel meta-heuristic solution method aiming minimization of total costs comprised fixed opening cost of collection/inspection, recovery and disposal centers and transportation cost of products between opened centers using priority based encoding presentation is proposed. Comparison of outputs from this proposed algorithm and a modified genetic algorithm shows the excellence of this new solution method. Finally, some directions for future research are proposed.
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