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    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Metaheuristics: Computer Decision-Making

Metaheuristics: Computer Decision-Making

Pinho De Sousa, J.
Resende, Mauricio G. C.

Hardcover

Series: Applied Optimization, Book 86

Technology & Engineering

ISBN10: 1402076533
ISBN13: 9781402076534
Publisher: Springer
Published: Nov 30 2003
Pages: 719
Weight: 2.40
Height: 1.50 Width: 6.36 Depth: 9.72
Language: English
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.

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