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RT Book, Whole SR Electronic DC OPAC T1 Metaheuristics: Computer Decision-Making / by Mauricio G. C. Resende, Jorge Pinho de Sousa T2 Applied Optimization A1 Resende, Mauricio G. C. A1 Sousa, Jorge Pinho de A1 SpringerLink (Online service) YR 2004 FD 2004 SP XV, 719 p K1 Computer science K1 Computer science -- Mathematics K1 Artificial intelligence K1 Mathematical models K1 Mathematical optimization K1 Computer Science K1 Artificial Intelligence (incl. Robotics) K1 Optimization K1 Discrete Mathematics in Computer Science K1 Mathematical Modeling and Industrial Mathematics PB Springer US : Imprint: Springer PP Boston, MA SN 9781475741377 LA English (英語) CL DC23:006.3 NO Combinatorial optimization is the process of finding the best, or optimal, so lution for problems with a discrete set of feasible solutions. Applications arise in numerous settings involving operations management and logistics, such as routing, scheduling, packing, inventory and production management, lo cation, logic, and assignment of resources. The economic impact of combi natorial optimization is profound, affecting sectors as diverse as transporta tion (airlines, trucking, rail, and shipping), forestry, manufacturing, logistics, aerospace, energy (electrical power, petroleum, and natural gas), telecommu nications, biotechnology, financial services, and agriculture. While much progress has been made in finding exact (provably optimal) so lutions to some combinatorial optimization problems, using techniques such as dynamic programming, cutting planes, and branch and cut methods, many hard combinatorial problems are still not solved exactly and require good heuristic methods. Moreover, reaching "optimal solutions" is in many cases meaningless, as in practice we are often dealing with models that are rough simplifications of reality. The aim of heuristic methods for combinatorial op timization is to quickly produce good-quality solutions, without necessarily providing any guarantee of solution quality. Metaheuristics are high level procedures that coordinate simple heuristics, such as local search, to find solu tions that are of better quality than those found by the simple heuristics alone: Modem metaheuristics include simulated annealing, genetic algorithms, tabu search, GRASP, scatter search, ant colony optimization, variable neighborhood search, and their hybrids NO 書誌ID=1002994248; LK [E Book]http://dx.doi.org/10.1007/978-1-4757-4137-7 OL 30