Metaheuristics: Computer Decision-Making:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Boston, MA
Springer US
2004
|
| Schriftenreihe: | Applied Optimization
86 |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Beschreibung: | Combinatorial optimization is the process of finding the best, or optimal, solution 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, location, logic, and assignment of resources. The economic impact of combinatorial optimization is profound, affecting sectors as diverse as transportation (airlines, trucking, rail, and shipping), forestry, manufacturing, logistics, aerospace, energy (electrical power, petroleum, and natural gas), telecommunications, biotechnology, financial services, and agriculture. While much progress has been made in finding exact (provably optimal) solutions 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 optimization 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 solutions that are of better quality than those found by the simple heuristics alone: Modern metaheuristics include simulated annealing, genetic algorithms, tabu search, GRASP, scatter search, ant colony optimization, variable neighborhood search, and their hybrids |
| Beschreibung: | 1 Online-Ressource (XV, 719 p) |
| ISBN: | 9781475741377 9781441954039 |
| ISSN: | 1384-6485 |
| DOI: | 10.1007/978-1-4757-4137-7 |
Internformat
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| 490 | 1 | |a Applied Optimization |v 86 |x 1384-6485 | |
| 500 | |a Combinatorial optimization is the process of finding the best, or optimal, solution 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, location, logic, and assignment of resources. The economic impact of combinatorial optimization is profound, affecting sectors as diverse as transportation (airlines, trucking, rail, and shipping), forestry, manufacturing, logistics, aerospace, energy (electrical power, petroleum, and natural gas), telecommunications, biotechnology, financial services, and agriculture. While much progress has been made in finding exact (provably optimal) solutions 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 optimization 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 solutions that are of better quality than those found by the simple heuristics alone: Modern metaheuristics include simulated annealing, genetic algorithms, tabu search, GRASP, scatter search, ant colony optimization, variable neighborhood search, and their hybrids | ||
| 650 | 4 | |a Mathematics | |
| 650 | 4 | |a Computational complexity | |
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| 650 | 4 | |a Künstliche Intelligenz | |
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Datensatz im Suchindex
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| author | Resende, Mauricio G. C. |
| author_facet | Resende, Mauricio G. C. |
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| author_sort | Resende, Mauricio G. C. |
| author_variant | m g c r mgc mgcr |
| building | Verbundindex |
| bvnumber | BV042421579 |
| classification_tum | MAT 000 |
| collection | ZDB-2-SMA ZDB-2-BAE |
| ctrlnum | (OCoLC)864092753 (DE-599)BVBBV042421579 |
| dewey-full | 519.6 |
| dewey-hundreds | 500 - Natural sciences and mathematics |
| dewey-ones | 519 - Probabilities and applied mathematics |
| dewey-raw | 519.6 |
| dewey-search | 519.6 |
| dewey-sort | 3519.6 |
| dewey-tens | 510 - Mathematics |
| discipline | Mathematik |
| doi_str_mv | 10.1007/978-1-4757-4137-7 |
| format | Electronic eBook |
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| id | DE-604.BV042421579 |
| illustrated | Not Illustrated |
| indexdate | 2024-07-10T01:21:09Z |
| institution | BVB |
| isbn | 9781475741377 9781441954039 |
| issn | 1384-6485 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-027856996 |
| oclc_num | 864092753 |
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| physical | 1 Online-Ressource (XV, 719 p) |
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| publishDate | 2004 |
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| publisher | Springer US |
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| series | Applied Optimization |
| series2 | Applied Optimization |
| spelling | Resende, Mauricio G. C. Verfasser aut Metaheuristics: Computer Decision-Making by Mauricio G. C. Resende, Jorge Pinho Sousa Boston, MA Springer US 2004 1 Online-Ressource (XV, 719 p) txt rdacontent c rdamedia cr rdacarrier Applied Optimization 86 1384-6485 Combinatorial optimization is the process of finding the best, or optimal, solution 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, location, logic, and assignment of resources. The economic impact of combinatorial optimization is profound, affecting sectors as diverse as transportation (airlines, trucking, rail, and shipping), forestry, manufacturing, logistics, aerospace, energy (electrical power, petroleum, and natural gas), telecommunications, biotechnology, financial services, and agriculture. While much progress has been made in finding exact (provably optimal) solutions 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 optimization 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 solutions that are of better quality than those found by the simple heuristics alone: Modern metaheuristics include simulated annealing, genetic algorithms, tabu search, GRASP, scatter search, ant colony optimization, variable neighborhood search, and their hybrids Mathematics Computational complexity Artificial intelligence Mathematical optimization Optimization Discrete Mathematics in Computer Science Artificial Intelligence (incl. Robotics) Mathematical Modeling and Industrial Mathematics Künstliche Intelligenz Mathematik Sousa, Jorge Pinho Sonstige oth Applied Optimization 86 (DE-604)BV010841718 86 https://doi.org/10.1007/978-1-4757-4137-7 Verlag Volltext |
| spellingShingle | Resende, Mauricio G. C. Metaheuristics: Computer Decision-Making Applied Optimization Mathematics Computational complexity Artificial intelligence Mathematical optimization Optimization Discrete Mathematics in Computer Science Artificial Intelligence (incl. Robotics) Mathematical Modeling and Industrial Mathematics Künstliche Intelligenz Mathematik |
| title | Metaheuristics: Computer Decision-Making |
| title_auth | Metaheuristics: Computer Decision-Making |
| title_exact_search | Metaheuristics: Computer Decision-Making |
| title_full | Metaheuristics: Computer Decision-Making by Mauricio G. C. Resende, Jorge Pinho Sousa |
| title_fullStr | Metaheuristics: Computer Decision-Making by Mauricio G. C. Resende, Jorge Pinho Sousa |
| title_full_unstemmed | Metaheuristics: Computer Decision-Making by Mauricio G. C. Resende, Jorge Pinho Sousa |
| title_short | Metaheuristics: Computer Decision-Making |
| title_sort | metaheuristics computer decision making |
| topic | Mathematics Computational complexity Artificial intelligence Mathematical optimization Optimization Discrete Mathematics in Computer Science Artificial Intelligence (incl. Robotics) Mathematical Modeling and Industrial Mathematics Künstliche Intelligenz Mathematik |
| topic_facet | Mathematics Computational complexity Artificial intelligence Mathematical optimization Optimization Discrete Mathematics in Computer Science Artificial Intelligence (incl. Robotics) Mathematical Modeling and Industrial Mathematics Künstliche Intelligenz Mathematik |
| url | https://doi.org/10.1007/978-1-4757-4137-7 |
| volume_link | (DE-604)BV010841718 |
| work_keys_str_mv | AT resendemauriciogc metaheuristicscomputerdecisionmaking AT sousajorgepinho metaheuristicscomputerdecisionmaking |