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The article presents comments on the management science paper "On Sequencing n Jobs on One Machine to Minimize the Number of Late Jobs," by William L. Maxwell. The paper focused on an integer programming formulation of a one-machine job shop problem. The author contends that Maxwell made in error in proving the validity of an optimal algorithm by applying cutting plane constraints to the problem. The author explains that the problem cannot be solved through traditional cutting plane procedures. The author provides a mathematical model in an attempt to correct the proof.

The paper considers a class of optimization problems. The problems are linear programming problems: maximize ex subject to Ax = b with the additional constraint that x must also be an extreme point of a second convex polyhedron Dx = d. X ≥ 0. A cutting-plane algorithm for solving such problems is presented. Two numerical examples are also included. [ABSTRACT FROM AUTHOR]

The article presents an algorithm for solving a linear integer programming problem and describes the theoretical foundations for a new approach to integer programming. According to the authors, the approach may be described as an extension of the methods using enumeration, but by using a new, more powerful approach based on population statistics. The optimal solution to the integer programming problem in this paper is viewed as one of selecting the optimal function among a certain class of functions which map elements of one set into another. With this method in mind, the authors intend to view the different problems by characterizing the statistical structure of the associated finite class of maps.

The usefulness of integer programming as a tool of capital budgeting hinges on the development of an efficient solution technique. An algorithm based on partial enumeration has been developed by E. L. Lawler and M. D. Bell for solving integer linear programs with 0-1 decision variables; however their algorithm is not general enough to deal with all problems in which the objective function is quadratic. This paper extends Lawler and Bell's method so that it can be generally applied to integer quadratic programs. The new algorithm is illustrated by examples from capital budgeting. [ABSTRACT FROM AUTHOR]

This paper attempts to present the major methods, successful or interesting uses, and computational experience relating to integer or discrete programming problems. Included are descriptions of general algorithms for solving linear programs in integers, as well as some special purpose algorithms for use on highly structured problems. This reflects a belief, on the author's part, that various clever methods of enumeration and other specialized approaches are the most efficacious means existent by which to obtain solutions to practical problems. A serious try at gathering computational experience has been made—but facts are difficult to uncover. The paper is written with intent to enable readers to read selected sections without having to read the whole. [ABSTRACT FROM AUTHOR]

It has been established that integer programs with integral, recursive constraint matrices have integral extreme points for integral, nonnegative requirement vectors. In this paper, it is first shown that the assignment problem can be transformed to such a program, but with upper bounds on some variables. This equivalent program leads to additional results for solving the assignment problem. Algorithms are then delineated to transform matching, covering and travelling salesman problems to equivalent recursive programs of the aforementioned type, but with bounds on some variables. These results are extended to programs with special structure in their constraint matrices. [ABSTRACT FROM AUTHOR]

This paper gives efficient methods for solving four specially structured network problems that arise in connection with certain integer programming methods developed by Cook and Cooper, Hillier, and Glover. Such problems have also independently been studied in inventory theory by Ignall and Veinott, who have developed extensive qualitative (nonalgorithmic) implications of their structures. In the integer programming context, special subclasses of these network problems are generated and solved as part of a strategy for solving more general integer linear programs. By providing particularly efficient methods for accommodating somewhat broader network structures, our results enable the development of related integer programming solution strategies that generate more complex subproblems. We show that the first of the four network problems can be solved by a procedure that assigns each variable a value exactly once, without subsequent revision. Properties of optimal solutions for the remaining problems are developed that enable the algorithm for the first to be extended to the second and then, by suitable transformation of variables, to the third and last problems as well. Moreover, while the last three problems involve an additional linear constraint that nullifies the "integer extreme point property", we show that optimal integer solutions to these problems can nevertheless easily be obtained from the optimal continuous solutions. [ABSTRACT FROM AUTHOR]

The methods of R. E. Gomory for the iterative solution of the mixed-integer linear programming problem are extended directly to the case where some or all of the variables are nonuniformly discrete, i.e., they are restricted to assume values from certain specified sets of unequally-spaced constants. The algorithm presented is shown to converge in a finite number of steps for a discrete-valued objective function. [ABSTRACT FROM AUTHOR]

An algorithm is presented for the special integer linear program known as the set partitioning problem. This problem has a binary coefficient matrix, binary variables, and unit resources. Furthermore, all of its constraints are equations: In spite of its very special form, the set partitioning problem has many practical interpretations. The algorithm is of the branch and bound type. A special class of finite mappings is enumerated rather than the customary set of binary solution vectors. Linear programming is used to obtain bounds on the minimal costs of the subproblems that arise. Computational results are reported for several large problems. [ABSTRACT FROM AUTHOR]

A unifying framework is developed to facilitate the understanding of most known computational approaches to integer programming. A number of currently operational algorithms are related to this framework, and prospects for future progress are assessed. [ABSTRACT FROM AUTHOR]

The usefulness of group theoretic methods in solving integer programming (IP) problems is extended by procedures for controlling the size of the groups. The main procedure given shows how an optimal linear programming basis can be altered to reduce the magnitude of its determinant thereby reducing the size of the group induced by the basis. An adaption of Benders' mixed IP algorithm is given which uses these methods. Some limited computational experience is given. [ABSTRACT FROM AUTHOR]

Some new properties associated with the special class of integer programs known as weighted set covering problems are derived. While it is well known that an optimal integer solution to the set covering problem is a basic feasible solution to the corresponding linear program, we show that there exists an optimal basis which is involutory (i.e., B = B[sup -l]). This property and others are used to develop a new algorithm which uses strong cutting planes. The cutting planes are strong in the sense that they exclude both integer and noninteger solutions. Computational experience is presented. [ABSTRACT FROM AUTHOR]

In this report, twenty-nine integer linear programming problems, ranging from very simple to apparently rather difficult, are introduced. The results of running these problems on four different computer codes, involving five distinct algorithms, are presented as both time and iteration data. A brief description of the codes used is also included. [ABSTRACT FROM AUTHOR]

A branch and bound algorithm for solution of the "knapsack problem," max Σ v[sub i],x[sub i] where Σ w[sub i]x[sub i] ≤ W and x[sub i] = 0, 1, is presented which can obtain either optimal or approximate solutions. Some characteristics of the algorithm are discussed and computational experience is present. Problems involving 50 items from which approximately 25 items were loaded were solved in an average of 0.07 minutes each by a coded version of this algorithm for the IBM 7094 computer. [ABSTRACT FROM AUTHOR]

An integer programming formulation for the problem of sequencing jobs on a single machine to minimize the number of late jobs is developed. The structure of the formulation is exploited to obtain the easily stated algorithm (previously developed by Moore [5]) for the solution of the problem and then to compare it to other similar allocation problems, among which is the Knapsack problem. [ABSTRACT FROM AUTHOR]