ISBN: 9783642080173
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ISBN: 9783642080173
Evolutionary scheduling is a vital research domain at the interface of two important sciences - artificial intelligence and operational research. Scheduling problems are generally complex… Más…
2010, ISBN: 3642080170
[EAN: 9783642080173], Neubuch, [PU: Springer Berlin Heidelberg], COMPUTATIONALINTELLIGENCE EVOLUTION EVOLUTIONARYALGORITHMS FUZZY OPERATIONSRESEARCH ALGORITHM ARTIFICIALINTELLIGENCE EVOLU… Más…
2010
ISBN: 9783642080173
Pasta blanda
[ED: Kartoniert / Broschiert], [PU: Springer Berlin Heidelberg], Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Demonstrate the applicabi… Más…
2007, ISBN: 3642080170
Pasta dura
Evolutionary Scheduling ab 208.49 € als Taschenbuch: Softcover reprint of hardcover 1st ed. 2007. Aus dem Bereich: Bücher, Ratgeber, Computer & Internet, Medien > Bücher, Springer Berlin … Más…
2010, ISBN: 9783642080173
Buch, Softcover, Softcover reprint of hardcover 1st ed. 2007, [PU: Springer Berlin], Springer Berlin, 2010
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Detalles del libro - Evolutionary Scheduling
EAN (ISBN-13): 9783642080173
ISBN (ISBN-10): 3642080170
Tapa dura
Tapa blanda
Año de publicación: 2010
Editorial: Springer Berlin
640 Páginas
Peso: 0,953 kg
Idioma: eng/Englisch
Libro en la base de datos desde 2012-02-22T04:13:18-06:00 (Mexico City)
Página de detalles modificada por última vez el 2023-01-26T15:51:02-06:00 (Mexico City)
ISBN/EAN: 9783642080173
ISBN - escritura alterna:
3-642-08017-0, 978-3-642-08017-3
Mode alterno de escritura y términos de búsqueda relacionados:
Autor del libro: peter chen
Título del libro: scheduling
Datos del la editorial
Autor: Keshav Dahal; Kay Chen Tan; Peter I. Cowling
Título: Studies in Computational Intelligence; Evolutionary Scheduling
Editorial: Springer; Springer Berlin
628 Páginas
Año de publicación: 2010-11-30
Berlin; Heidelberg; DE
Impreso en
Idioma: Inglés
160,49 € (DE)
164,99 € (AT)
177,00 CHF (CH)
POD
XI, 628 p.
BC; Hardcover, Softcover / Informatik, EDV/Informatik; Künstliche Intelligenz; Verstehen; Computational Intelligence; Evolution; Evolutionary Algorithms; Fuzzy; Operations Research; algorithm; artificial intelligence; evolutionary algorithm; evolutionary strategies; genetic algorithms; heuristics; intelligence; metaheuristic; optimization; programming; Artificial Intelligence; Mathematical and Computational Engineering Applications; Mathematik für Ingenieure; BB
Methodology.- Memetic Algorithms in Planning, Scheduling, and Timetabling.- Landscapes, Embedded Paths and Evolutionary Scheduling.- Classical and Non-Classical Models of Production Scheduling.- Scheduling of Flow-Shop, Job-Shop, and Combined Scheduling Problems using MOEAs with Fixed and Variable Length Chromosomes.- Designing Dispatching Rules to Minimize Total Tardiness.- A Robust Meta-Hyper-Heuristic Approach to Hybrid Flow-Shop Scheduling.- Hybrid Particle Swarm Optimizers in the Single Machine Scheduling Problem: An Experimental Study.- An Evolutionary Approach for Solving the Multi-Objective Job-Shop Scheduling Problem.- Timetabling.- Multi-Objective Evolutionary Algorithm for University Class Timetabling Problem.- Metaheuristics for University Course Timetabling.- Energy Applications.- Optimum Oil Production Planning using an Evolutionary Approach.- A Hybrid Evolutionary Algorithm for Service Restoration in Power Distribution Systems.- Particle Swarm Optimisation for Operational Planning: Unit Commitment and Economic Dispatch.- Evolutionary Generator Maintenance Scheduling in Power Systems.- Networks.- Evolvable Fuzzy Scheduling Scheme for Multiple-ChannelPacket Switching Network.- A Multi-Objective Evolutionary Algorithm for Channel Routing Problems.- Transport.- Simultaneous Planning and Scheduling for Multi-Autonomous Vehicles.- Scheduling Production and Distribution of Rapidly Perishable Materials with Hybrid GA's.- A Scenario-based Evolutionary Scheduling Approach for Assessing Future Supply Chain Fleet Capabilities.- Business.- Evolutionary Optimization of Business Process Designs.- Using a Large Set of Low Level Heuristics in a Hyperheuristic Approach to Personnel Scheduling.- A Genetic-Algorithm-Based Reconfigurable Scheduler.- Evolutionary Algorithm foran Inventory Location Problem.Demonstrate the applicability of evolutionary computational techniques to solve scheduling problems
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