Contemporary Approaches to the Solution of the Integer Programming Problem (original) (raw)

Linear Integer Programming Methods and Approaches–A Survey

CYBERNETICS AND INFORMATION …, 2011

The paper presents a survey of methods and approaches solving linear integer problems, developed during the last 50 years. These problems belong to the class of NP-hard optimization problems. To find out exact optimal solutions for this class of problems requires use of considerable computational resources. The development of efficient hybrid methods, combining in a suitable way the best features of different approaches (exact or approximate) is the actual direction, in which many researchers devote their efforts to solve successfully varioushard practical problems.

AN ALGORITHM FOR SOLVING INTEGER LINEAR PROGRAMMING PROBLEMS

The paper describes a method to solve an ILP by describing whether an approximated integer solution to the RLP is an optimal solution to the ILP. If the approximated solution fails to satisfy the optimality condition, then a search will be conducted on the optimal hyperplane to obtain an optimal integer solution using a modified form of Branch and Bound Algorithm.

A method to improve integer linear programming problem with branch-and-bound procedure

Applied Mathematics and Computation, 2006

Integer linear programming (ILP) problems are harder to solve than linear programming (LP) problems. It doesn't work if try to round off the results of LP problems and claim they are the optimum solution. The branch-and-bound (B&B) is the popular method to solve ILP problems. In this paper, we propose a revised B&B, which is demonstrated to be more efficient most of time. This method is extraordinarily useful when facing ILP problems with large differences between constraints and variables. It could reduce the number of constraint and work efficiently when handling ILP problems with many constraints and less variables. Even if the ILP problems have fewer constraints but many variables, we suggest using duality concept to interchange variables with constraints. Then, the revised B&B could be used to compute results very quickly.

A Novel Alternative Algorithm for Solving Integer Linear Programming Problems Having Three Variables

Cybernetics and Information Technologies, 2020

In this study, a novel alternative method based on parameterization for solving Integer Linear Programming (ILP) problems having three variables is developed. This method, which is better than the cutting plane and branch boundary method, can be applied to pure integer linear programming problems with m linear inequality constraints, a linear objective function with three variables. Both easy to understand and to apply, the method provides an effective tool for solving three variable integer linear programming problems. The method proposed here is not only easy to understand and apply, it is also highly reliable, and there are no computational difficulties faced by other methods used to solve the three-variable pure integer linear programming problem. Numerical examples are provided to demonstrate the ease, effectiveness and reliability of the proposed algorithm.

Constraint Integer Programming: Techniques and Applications

2008

This article introduces constraint integer programming (CIP), which is a novel way to combine constraint programming (CP) and mixed integer programming (MIP) methodologies. CIP is a generalization of MIP that supports the notion of general constraints as in CP. This ap- proach is supported by the CIP framework SCIP, which also integrates techniques for solving satisability problems. SCIP is available

A Decomposition Technique For Solving Integer Programming Problems

GANIT: Journal of Bangladesh Mathematical Society, 2014

Dantzig-Wolfe decomposition as applied to an integer program is a specific form of problem reformulation that aims at providing a tighter linear programming relaxation bound due to the non-convexity of an integer problem. In this paper, we develop an algorithm for solving large scale integer program relying on column generation method. We implemented our algorithm for solving Capital budgeting and scheduling type problems. Moreover, we used the Computer Aided System (CAS) AMPL to convert our algorithm into programming codes and illustrated the same problem in our program. We demonstrate our method by illustrating some numerical examples. DOI: http://dx.doi.org/10.3329/ganit.v33i0.17649 GANIT J. Bangladesh Math. Soc.Vol. 33 (2013) 1-11

Integer Programming: Theory and Practice

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On the foundations of linear and integer linear programming I

Mathematical Programming, 1975

In this paper we consider the question: how does the flow algorithm and the simplex algorithm work? The usual answer has two parts: first a description of the "improvement process", and second a proof that if no further improvement can be made by this process, an optimal vector has been found. This second part is usually based on dualitya technique not available in the case of an arbitrary integer programming problem. We wish to give a general description of "improvement processes" which will include both the simplex and flow algorithms, which will be applicable to arbitrary integer programming problems, and which will in themselves assure convergence to a solution. Geometrically both the simplex algorithm and the flow algorithm may be described as follows. At the i th stage, we have a vertex (or feasible flow) to which is associated a finite set of vectors, namely the set of edges leaving that vertex (or the set of unsaturated paths). The algorithm proceeds by searching among this special set for a vector along which the gain function is increasing. If such a vector is found, the algorithm continues by moving along this vector as far as is possible while still remaining feasible. The search is then repeated at this new feasible point. We give a precise definition for sets of vectors, called test sets, which will include those sets described above arising in the simplex and flow algorithms. We will then prove that any "improvement process" which searches through a test set at each stage converges to an optimal point in a finite nmnber of steps. We also construct specific test sets which are the natural extensions of the test sets employed by the flow algorithm to arbitrary linear and integer linear programming problems.

Progress in Linear Programming-Based Algorithms for Integer Programming: An Exposition

INFORMS Journal on Computing, 2000

This paper is about modeling and solving mixed integer programming (MIP) problems. In the last decade, the use of mixed integer programming models has increased dramatically. Fifteen years ago, mainframe computers were required to solve problems with a hundred integer variables. Now it is possible to solve problems with thousands of integer variables on a personal computer and obtain provably good approximate solutions to problems such as set partitioning with millions of binary variables. These advances have been made possible by developments in modeling, algorithms, software, and hardware. This paper focuses on effective modeling, preprocessing, and the methodologies of branch-and-cut and branch-and-price, which are the techniques that make it possible to treat problems with either a very large number of constraints or a very large number of variables. We show how these techniques are useful in important application areas such as network design and crew scheduling. Finally, we discuss the relatively new research areas of parallel integer programming and stochastic integer programming.