OPTIMIZATION OVER INTEGERS PDF
Request PDF on ResearchGate | On Jan 1, , Dimitris Bertsimas and others published Optimization over Integers. Get this from a library! Optimization over integers. [Dimitris Bertsimas; Robert Weismantel]. Dimitris Bertsimas and Robert Weismantel: Optimization over Integers. Dynamic Solving optimization problems with variables restricted to take integer values.
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Optimization Over Integers [Dimitris Bertsimas, Robert Weismantel] on Amazon. com. *FREE* shipping on qualifying offers. The book provides a unified, insightful . integer optimization, and algorithms for both binary and integer optimiza- tion. The basic algebraic objects in this chapter are polynomials over complex. The study of mixed integer nonlinear optimization problems is still in Slot Nonlinear parametric optimization over independence systems.
We show several optimization and approximation results for the robust with respect to cost, or few constraints counterpart of an integer problem under the condition that one can optimize or approximate the original integer problem with respect to a piecewise linear objective respectively piecewise linear constraints.
We demonstrate the applicability of our approach on two classes of integer programs, namely, totally unimodular integer programs and integer programs with two variables per inequality.
Further, for combinatorial optimization problems our method yields polynomial time approximations and pseudopolynomial, exact algorithms for Robust Unbounded Knapsack Problems. Preview Unable to display preview.
Download preview PDF. References Special issue on robust optimization. Aissi, H.
Bar-Yehuda, R. Ben-Tal, A. Bertsimas, D. Feige, U.
In: Fischetti, M. IPCO LNCS, vol.
Optimization Over Integers
Fischetti, M. In: Ahuja, R.
These considerations occur frequently in practice and so integer linear programming can be used in many applications areas, some of which are briefly described below.
Production planning[ edit ] Mixed integer programming has many applications in industrial production, including job-shop modelling.
One important example happens in agricultural production planning involves determining production yield for several crops that can share resources e. Land, labor, capital, seeds, fertilizer, etc.
A possible objective is to maximize the total production, without exceeding the available resources. In some cases, this can be expressed in terms of a linear program, but variables must be constrained to be integer.
Scheduling[ edit ] These problems involve service and vehicle scheduling in transportation networks. For example, a problem may involve assigning buses or subways to individual routes so that a timetable can be met, and also to equip them with drivers.
Here binary decision variables indicate whether a bus or subway is assigned to a route and whether a driver is assigned to a particular train or subway. It is used in a special case of integer programming, in which all the decision variables are integers.Extensions of integer optimization includes Chapters 12 and 13, and treats mixed integer optimization and robust discrete optimization.
Rather than covering methodology, the book introduces decision support systems through real world applications, and uses spreadsheets to model and solve problems. Part IV: Extensions of integer optimization includes Chapters 12 and 13, and treats mixed integer optimization and robust discrete optimization. View all subjects More like this Similar Items.
A smaller or larger initial range can give better results when the default value is inappropriate. Computer Methods in Applied Mechanics and Engineering, 2—4 , pp.
SIAM J. The integer variables represent decisions e. A possible objective is to maximize the total production, without exceeding the available resources.
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