Statistical decision analysis of stochastic linear programming problems

This paper presents a statistical decision analysis of a one-stage linear programming problem with deterministic constraints and stochastic criterion function. Procedures for obtaining numerical results are given which are applicable to any problem having this general form. We begin by stating the statistical decision problems to be considered, and then discuss the expected value of perfect information and the expected value of sample information. In obtaining these quantities, use is made of the distribution of the optimal value of the linear programming problem with stochastic criterion function, and so we discuss Monte Carlo and numerical integration procedures for estimating the mean of this distribution. The case in which the random criterion vector has a multivariate Normal distribution is discussed separately, and more detailed methods are offered. We discuss dual problems, including some relationships of this work with other work in probabilistic linear programming. An example is given in Appendix A showing application of the methods to a sample problem. In Appendix B we consider the accuracy of a procedure for approximating the expected value of information.     

Solving generalized transportation problems via pure transportation problems

This paper investigates certain issues of coefficient sensitivity in generalized network problems when such problems have small gains or losses. In these instances, it might be computationally advantageous to temporarily ignore these gains or losses and solve the resultant “pure” network problem. Subsequently, the optimal solution to the pure problem could be used to derive the optimal solution to the original generalized network problem. In this paper we focus on generalized transportation problems and consider the following question: Given an optimal solution to the pure transportation problem, under what conditions will the optimal solution to the original generalized transportation problem have the same basic variables? We study special cases of the generalized transportation problem in terms of convexity with respect to a basis. For the special case when all gains or losses are identical, we show that convexity holds. We use this result to determine conditions on the magnitude of the gains or losses such that the optimal solutions to both the generalized transportation problem and the associated pure transportation problem have the same basic variables. For more general cases, we establish sufficient conditions for convexity and feasibility. © 2002 Wiley Periodicals, Inc. Naval Research Logistics 49: 666–685, 2002; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/nav.10034     

A note on the strategy of restriction and degenerate linear programming problems

Using the general computational strategy of restriction, necessary conditions for optimality provide an alternative criterion for entering variables when degeneracies arises in linear programming problems. Although cycling may still occur, it is shown that if it is possible to make progress at the next iteration, the criterion is guaranteed to identify a non-basic variable which increases the value of the basic solution, thereby reducing stalling. An alternative method for determining variables to exit the basis when degeneracies occur is also suggested.     

Time-minimizing transportation problems

A method is given for finding those solutions of a transportation problem which minimize the total time necessary for transporting goods from the suppliers to the consumers. Several extensions of the model are presented.     

Algorithmic insights and a convergence analysis for a Karmarkar-type of algorithm for linear programming problems

This paper is concerned with a modification of a recently proposed variant of Karmarkar's algorithm for solving linear programming problems. In analyzing this variant, we exhibit interesting and useful relationships of these types of algorithms with barrier function methods, and subgradient optimization procedures involving space dilation techniques, which subsume the well-known ellipsoidal type of algorithms. Convergence of this variant is established under certain regularity conditions. We also provide remarks on how to obtain dual variables or Lagrange multipliers at optimality.     

Finding equivalent transportation formulations for constrained transportation problems

This paper describes a procedure for determining if constrained transportation problems (i.e., transportation problems with additional linear constraints) can be transformed into equivalent pure transportation problems by a linear transformation involving the node constraints and the extra constraints. Our results extend procedures for problems in which the extra constraints consist of bounding certain partial sums of variables.     

Finite statistical games and linear programming

The dual linear programs associated with finite statistical games are investigated and their optimal solutions are interpreted. The usual statistical game is generalized to a two-sided (inference) game and its possible application as a tactical model is discussed.     

Vessel allocation by linear programming

This paper presents a linear programming model of a fleet of vessels which is required to transport quantities of cargo, such as coal, iron ore, limestone, and salt from certain producing ports to specific destination ports. This model has been implemented and is currently being used both for planning purposes and as an aid in scheduling the trips to be made by each vessel.     

Quadratic as parametric linear programming

This paper describes an approximate solution procedure for quadratic programming problems using parametric linear programming. Limited computational experience suggests that the approximation can be expected to be “good”.     

Generalized linear programming and sensitivity analysis techniques

In this article we report on numerical experiments conducted to assess the performance of c-programming algorithms for generalized linear programming problems involving the maximization of composite-convex objective functions. The results indicate that the standard parametric sensitivity analysis techniques of the simplex method can play a central role in such algorithms. We also comment on issues concerning the use of commercial LP packages to solve problems of this type. © 1996 John Wiley & Sons, Inc.     

A mutual primal-dual linear programming algorithm

A new primal-dual linear programming algorithm is exhibited. A proof is given that optimal solutions to both primal and dual problems (when such solutions exist) are found in a finite number of steps by this algorithm. A numerical example is included to illustrate the method.     

New purification algorithms for linear programming

Two new algorithms are presented for solving linear programs which employ the opposite-sign property defined for a set of vectors in m space. The first algorithm begins with a strictly positive feasible solution and purifies it to a basic feasible solution having objective function value no less under maximization. If this solution is not optimal, then it is drawn back into the interior with the same objective function value, and a restart begins. The second algorithm can begin with any arbitrary feasible point. If necessary this point is purified to a basic feasible solution by dual-feasibility–seeking directions. Should dual feasibility be attained, then a duality value interval is available for estimating the unknown objective function value. If at this juncture the working basis is not primal feasible, then further purification steps are taken tending to increase the current objective function value, while simultaneously seeking another dual feasible solution. Both algorithms terminate with an optimal basic solution in a finite number of steps.     

A note on integer solutions to linear fractional interval programming problems by a branch & bound technique

This paper provides a method for solving linear fractional interval programming problems in integers with the help of a branch and bound technique.     

具有模糊系数约束的多目标线性规划

研究了一类具有模糊系数约束的多目标线性规划问题.根据各目标函数的梯度方向来量化目标之间的冲突程度,以此提出了一种确定目标权重的新方法,然后基于惩罚函数运用梯度上升算法求问题的有效解.最后给出了一个数值例子.     

Chance-constrained linear programming with location scale distributions

M. Kress, in 1984, studied the chance-constrained critical path problem. The author proved that if the project time random variables follow a class of location-scale probability distributions, then there exists a specific threshold confidence level, such that the critical path for the system remains the same for all higher confidence levels. The purpose of this article is to study the similar properties for a general chance-constrained linear programming (C²LP) problems with location-scale probability distributions. We present results for chance-constrained linear programming which parallel those in Kress's article. © 1992 John Wiley & Sons, Inc.     

A linear programming approach to geometric programs

A cutting plane method, based on a geometric inequality, is described as a means of solving geometric programs. While the method is applied to the primal geometric program, it is shown to retain the geometric programming duality relationships. Several methods of generating the cutting planes are discussed and illustrated on some example problems.     

The interactive fixed charge linear programming problem

In many decision-making situations, each activity that can be undertaken may have associated with it both a fixed and a variable cost. Recently, we have encountered serveral practical problems in which the fixed cost of undertaking an activity depends upon which other activities are also undertaken. To our knowledge, no existing optimization model can accomodate such a fixed cost structure. To do so, we have therefore developed a new model called the interactive fixed charge linear programming problem (IFCLP). In this paper we present and motivate problem (IFCLP), study some of its characteristics, and present a finite branch and bound algorithm for solving it. We also discuss the main properties of this algorithm.     

A note on integer linear fractional programming

This note consists of developing a method for enforcing additional constraints to linear fractional programs and showing its usefulness in solving integer linear fractional programs.     

一类多目标模糊系数线性规划问题

讨论了一类所有系数均为模糊数的多目标线性规划问题 .通过对模糊数的比较 ,将模糊多目标线性规划模型转化为清晰的多目标模型 ,并应用一种基于线性隶属函数的模糊规划算法求其协调解 .最后给出了一个数值例子 .