Integer Optimization by Local Search [electronic resource] : A Domain-Independent Approach / edited by Joachim Paul Walser.
Material type: TextSeries: Lecture Notes in Computer Science ; 1637Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg, 1999Description: XX, 144 p. online resourceContent type: text Media type: computer Carrier type: online resourceISBN: 9783540483694Subject(s): Computer science | Computer software | Computational complexity | Artificial intelligence | Mathematical optimization | Management information systems | Computer Science | Artificial Intelligence (incl. Robotics) | Algorithm Analysis and Problem Complexity | Discrete Mathematics in Computer Science | Business Information Systems | Calculus of Variations and Optimal Control; OptimizationAdditional physical formats: Printed edition:: No titleDDC classification: 006.3 LOC classification: Q334-342TJ210.2-211.495Online resources: Click here to access onlineCurrent library | Home library | Call number | Materials specified | URL | Status | Date due | Barcode |
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IMSc Library | IMSc Library | Link to resource | Available | EBK6439 |
Frameworks for Combinatorial Optimization -- Local Search for Integer Constraints -- Case Studies Methodology -- Time-Tabling and Sports Scheduling -- Covering and Assignment -- Capacitated Production Planning -- Extensions.
Integer Optimization addresses a wide spectrum of practically important optimization problems and represents a major challenge for algorithmics. The goal of integer optimization is to solve a system of constraints and optimization criteria over discrete variables. Integer Optimization by Local Search introduces a new approach to domain-independent integer optimization, which, unlike traditional strategies, is based on local search. It develops the central concepts and strategies of integer local search and describes possible combinations with classical methods from linear programming. The surprising effectiveness of the approach is demonstrated in a variety of case studies on large-scale, realistic problems, including production planning, timetabling, radar surveillance, and sports scheduling. The monograph is written for practitioners and researchers from artificial intelligence and operations research.
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