By Frédéric Gardi
ISBN-10: 1118966465
ISBN-13: 9781118966464
ISBN-10: 1118966473
ISBN-13: 9781118966471
ISBN-10: 1306958296
ISBN-13: 9781306958295
ISBN-10: 1848216866
ISBN-13: 9781848216860
This publication covers neighborhood look for combinatorial optimization and its extension to mixed-variable optimization. even if no longer but understood from the theoretical viewpoint, neighborhood seek is the paradigm of selection for tackling large-scale real-life optimization difficulties. modern end-users call for interactivity with selection help platforms. For optimization software program, this implies acquiring good-quality solutions �Read more...
summary: This ebook covers neighborhood look for combinatorial optimization and its extension to mixed-variable optimization. even supposing no longer but understood from the theoretical perspective, neighborhood seek is the paradigm of selection for tackling large-scale real-life optimization difficulties. brand new end-users call for interactivity with determination aid platforms. For optimization software program, this suggests acquiring good-quality strategies fast. quick iterative development tools, like neighborhood seek, are suited for enjoyable such wishes. right here the authors express neighborhood seek in a brand new gentle, particularly providing a brand new kinfolk
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Extra info for Mathematical Programming Solver Based on Local Search
Sample text
This generalization of the vehicle routing problem was often handled in two stages in the past: first stage – inventory; second stage – routing. On the contrary, a characteristic of our local search approach is the absence of decomposition, made possible by a fast volume assignment algorithm. Moreover, thanks to a large variety of randomized neighborhoods, a standard first-improvement descent is used instead of tuned, complex metaheuristics. An extensive computational study shows that our solution provides long-term savings exceeding 20 % on average compared to solutions built by a classical urgency-based constructive algorithm or even by expert planners.
Some attempts have been made to use pure and direct local search approaches in discrete mathematical programming. The most famous ones have been introduced by researchers from the artificial intelligence community. Indeed, some of the best provers for Satisfiability Testing (SAT) or pseudo-Boolean programming rely on stochastic local search (see for example Walksat [SEL 96] and WSAT(OIP) [WAL 99]). Some attempts have been made in binary programming [CON 92, NON 98] or in integer programming [ABR 99, RAN 01].
This generalization of the vehicle routing problem was often handled in two stages in the past: first stage – inventory; second stage – routing. On the contrary, a characteristic of our local search approach is the absence of decomposition, made possible by a fast volume assignment algorithm. Moreover, thanks to a large variety of randomized neighborhoods, a standard first-improvement descent is used instead of tuned, complex metaheuristics. An extensive computational study shows that our solution provides long-term savings exceeding 20 % on average compared to solutions built by a classical urgency-based constructive algorithm or even by expert planners.
Mathematical Programming Solver Based on Local Search by Frédéric Gardi
by Michael
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