Algebraic Modeling Systems: Modeling and Solving Real World Optimization ProblemsJosef Kallrath This book Algebraic Modeling Systems – Modeling and Solving Real World Optimization Problems – deals with the aspects of modeling and solving real-world optimization problems in a unique combination. It treats systematically the major algebraic modeling languages (AMLs) and modeling systems (AMLs) used to solve mathematical optimization problems. AMLs helped significantly to increase the usage of mathematical optimization in industry. Therefore it is logical consequence that the GOR (Gesellschaft für Operations Research) Working Group Mathematical Optimization in Real Life had a second meeting devoted to AMLs, which, after 7 years, followed the original 71st Meeting of the GOR (Gesellschaft für Operations Research) Working Group Mathematical Optimization in Real Life which was held under the title Modeling Languages in Mathematical Optimization during April 23–25, 2003 in the German Physics Society Conference Building in Bad Honnef, Germany. While the first meeting resulted in the book Modeling Languages in Mathematical Optimization, this book is an offspring of the 86th Meeting of the GOR working group which was again held in Bad Honnef under the title Modeling Languages in Mathematical Optimization. |
Contents
| 2 | |
Part II Selected Algebraic Modeling Systems | 33 |
Part III Aspects of Modeling and Solving Real World Problems | 159 |
Other editions - View all
Algebraic Modeling Systems: Modeling and Solving Real World Optimization ... Josef Kallrath No preview available - 2014 |
Algebraic Modeling Systems: Modeling and Solving Real World Optimization ... Josef Kallrath No preview available - 2012 |
Common terms and phrases
algebraic modeling languages Algebraic Modeling Systems algorithm alternative AMLs applications array attributes automatic binary Boolean branch and bound computing Constraint Programming constraints controlled natural language CP Optimizer CPLEX cumul function declarations defined dict disjunctive programming equations example expression FICO FMathL formulation GAMS global GLPK Grossmann GUSS implementation input interface interval variable Kallrath knapsack Languages in Mathematical linear programs logical LogMIP machine mathematical optimization Mathematical Programming matrix minimal MINLP mixed integer model instance module Mosel Mosel model Neumaier nodes nonlinear Nonlinear Programming objective function operations optimization problem optional packages param parameters presenceOf(a production planning programming languages propagation provides queueing reformulation represented resource scenario scheduling problems Schodl semantic memory simulation solution SOLVE statement solver specific structure subproblem subroutines syntax task temporal tool tuple values vector VisPlainR WIDTHS ZIMPL