IFORS2002

IFORS2002 - Applied Mathematics Systematic Diversification...

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Unformatted text preview: Applied Mathematics Systematic Diversification Metaheuristic for the Vehicle Routing Problem with Time Windows OLLI BRYSY, GEIR HASLE SINTEF Applied Mathematics, Department of Optimisation, P.O. Box 124 Blindern, N-0314 Oslo, Norway {Olli.Braysy;Geir.Hasle}@sintef.no JEAN BERGER and MOHAMED BARKAOUI Defence Research Establishment Valcartier, Decision Support Technology Section 2459 Pie-XI Blvd. North, Val-Blair, PQ, Canada, G3J 1X5 email: jean.berger@drev.dnd.ca, barkaoui@oricom.ca Applied Mathematics Introduction Development of a new metaheuristic, Systematic Diversification for combinatorial optimization problems Applied to three-phase multi-start local search framework of Brysy et al. (2002) for Vehicle Routing Problem with Time Windows (VRPTW) Coupled with a Threshold Accepting post-optimization procedure Computational results reported for the well-known 100-customer benchmarks of Solomon (1987). The findings indicate that the results are within 0.3% from the best known on the average. IFORS 2002, Edinburgh, Scotland, July 8-12 Applied Mathematics Generation of the LS solution Reduce the number of routes Reduce distance Injection trees Modified CROSS exchanges Build initial solutions Hybrid construction heuristic IFORS 2002, Edinburgh, Scotland, July 8-12 PROCESS METHOD Applied Mathematics Systematic Diversification The basic idea is to maximize differences between individuals in a population based search method Based on common attribute (arcs) Intensification using the local search Changes that increase the number of common attributes with respect to previously obtained individuals penalized or blocked Applied to multi-start framework Penalizes selection of common attributes or identifies the set of most diverse individuals Limit to worsening of the objective function value Used to guide the creation of initial solutions and distance improvement heuristics Applied Mathematics Hybrid sequential insertion heuristic Initialization: seed customer randomly selected among the farthest from the depot or among the ones having the earliest deadline Insert the cheapest customer in the best feasible insertion place Weighted cost function of additional detour and waiting time Subtract the distance of the customer to the depot...
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IFORS2002 - Applied Mathematics Systematic Diversification...

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