Integer Optimization by Local Search A Domain-Independent Approach /
Walser, Joachim P.
Integer Optimization by Local Search A Domain-Independent Approach / [electronic resource] : by Joachim P. Walser. - 1st ed. 1999. - XX, 144 p. online resource. - Lecture Notes in Artificial Intelligence, 1637 2945-9141 ; . - Lecture Notes in Artificial Intelligence, 1637 .
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.
9783540483694
10.1007/3-540-48369-1 doi
Artificial intelligence.
Algorithms.
Computer science--Mathematics.
Discrete mathematics.
Business information services.
Mathematical optimization.
Calculus of variations.
Artificial Intelligence.
Algorithms.
Discrete Mathematics in Computer Science.
IT in Business.
Calculus of Variations and Optimization.
Q334-342 TA347.A78
006.3
Integer Optimization by Local Search A Domain-Independent Approach / [electronic resource] : by Joachim P. Walser. - 1st ed. 1999. - XX, 144 p. online resource. - Lecture Notes in Artificial Intelligence, 1637 2945-9141 ; . - Lecture Notes in Artificial Intelligence, 1637 .
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.
9783540483694
10.1007/3-540-48369-1 doi
Artificial intelligence.
Algorithms.
Computer science--Mathematics.
Discrete mathematics.
Business information services.
Mathematical optimization.
Calculus of variations.
Artificial Intelligence.
Algorithms.
Discrete Mathematics in Computer Science.
IT in Business.
Calculus of Variations and Optimization.
Q334-342 TA347.A78
006.3