TY  - BOOK
AU  - Gardi,Fr�ed�eric
TI  - Mathematical programming solver based on local search
T2  - Focus Computer Engineering Series,
SN  - 9781118966471
AV  - T57.7 
U1  - 519.7 22
PY  - 2014///
CY  - London, Hoboken
PB  - ISTE Ltd., Wiley
KW  - Mathematical optimization
KW  - Optimisation math�ematique
KW  - MATHEMATICS
KW  - Applied
KW  - bisacsh
KW  - Probability & Statistics
KW  - General
KW  - fast
N1  - Includes bibliographical references and index; Cover; Title Page; Copyright; Contents; Acknowledgments; Preface; Introduction; Chapter 1. Local Search: Methodology and Industrial Applications; 1.1. Our methodology: back to basics; 1.1.1. What are the needs in business and industry?; 1.1.2. The main ingredients of the recipe; 1.1.3. Enriching and enlarging neighborhoods; 1.1.4. High-performance software engineering; 1.2. Car sequencing for painting and assembly lines; 1.2.1. Search strategy and moves; 1.2.2. Enriching the moves and boosting their evaluation; 1.2.3. Experimental results and discussion; 1.3. Vehicle routing with inventory management1.3.1. State-of-the-art; 1.3.2. Search strategy and moves; 1.3.3. Incremental evaluation machinery; Chapter 2. Local Search for 0-1 Nonlinear Programming; 2.1. The LocalSolver project; 2.2. State-of-the-art; 2.3. Enriching modeling standards; 2.3.1. LocalSolver modeling formalism; 2.3.2. LocalSolver programming language; 2.4. The core algorithmic ideas; 2.4.1. Effective local search moves; 2.4.2. Incremental evaluation machinery; 2.5. Benchmarks; 2.5.1. Car sequencing; 2.5.2. Machine scheduling; 2.5.3. Quadratic assignment problem; 2.5.4. MIPLIB 2010Chapter 3. Toward an Optimization Solver Based on Neighborhood Search; 3.1. Using neighborhood search as global search strategy; 3.2. Extension to continuous and mixed optimization; 3.3. Separating the computation of solutions and bounds; 3.4. A new-generation, hybrid mathematical programming solver; Bibliography; Lists of Figures and Tables; Index
N2  - This book covers local search for combinatorial optimization and its extension to mixed-variable optimization. Although not yet understood from the theoretical point of view, local search is the paradigm of choice for tackling large-scale real-life optimization problems. Today's end-users demand interactivity with decision support systems. For optimization software, this means obtaining good-quality solutions quickly. Fast iterative improvement methods, like local search, are suited to satisfying such needs. Here the authors show local search in a new light, in particular presenting a new kin
UR  - https://onlinelibrary.wiley.com/doi/book/10.1002/9781118966464
ER  -