Optimal maintenance budgetary control in generating systems
01 Mar 1976-International Journal of Systems Science (Taylor & Francis Group)-Vol. 7, Iss: 3, pp 289-297
TL;DR: The paper presents 0-1 integer programming models for the control of maintenance expenditure on thermal generating units and develops a new, simple and efficient tree search method for the solution of the problems.
Abstract: The paper presents 0-1 integer programming models for the control of maintenance expenditure on thermal generating units. Choosing a sound and effective maintenance policy reduces the system down-time and thus increases the revenue to the utility. The objective is aimed at selecting that sot of proposals which will maximize the not present value of its total expected return. The alternative proposals, the forecasts of the net present value of the returns associated with the alternatives, the number of repairmen required for each alternative, and cash outflow required over the next 5 years are available for the analysis. The problem is discussed both under conditions of certainty and uncertainty. A new, simple and efficient tree search method is developed for the solution of the problems. A computer algorithm is developed and results of computation are presented for sample applications.
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TL;DR: In this paper, a number of objective criteria for optimal maintenance scheduling of thermal generators are discussed, based on generation operating cost, reliability indices, deviations from a desired schedule and/or constraints violation penalties.
Abstract: A number of objective criteria for optimal maintenance scheduling of thermal generators is discussed. The criteria are based on generation operating cost, reliability indices, deviations from a desired schedule and/or constraints violation penalties. A comparison of the performance of all these criteria is presented by maintenance scheduling a realistic 30 thermal-unit system. Also, the relationships between the several realibility indices are dicussed.
49 citations
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TL;DR: In this paper, an algorithm for solving linear programs with variables constrained to take only one of the values 0 or 1 is proposed, where the only operations required under the algorithm are additions and subtractions.
Abstract: An algorithm is proposed for solving linear programs with variables constrained to take only one of the values 0 or 1. It starts by setting all the n variables equal to 0, and consists of a systematic procedure of successively assigning to certain variables the value 1, in such a way that after trying a small part of all the 2n possible combinations, one obtains either an optimal solution, or evidence of the fact that no feasible solution exists. The only operations required under the algorithm are additions and subtractions; thus round-off errors are excluded. Problems involving up to 15 variables can be solved with this algorithm by hand in not more than 3-4 hours. An extension of the algorithm to integer linear programming and to nonlinear programming is available, but not dealt with in this article.
677 citations
TL;DR: This paper describes a simple, easily-programmed method for solving discrete optimization problems with monotone objective functions and completely arbitrary (possibly nonconvex) constraints that is computationally feasible for problems in which the number of variables is fairly small.
Abstract: This paper describes a simple, easily-programmed method for solving discrete optimization problems with monotone objective functions and completely arbitrary (possibly nonconvex) constraints. The method is essentially one of partial enumeration, and is closely related to the “lexicographic” algorithm of Gilmore and Gomory for the “knapsack” problem and to the “additive” algorithm of Balas for the general integer linear programming problem. The results of a number of sample computations are reported. These indicate that the method is computationally feasible for problems in which the number of variables is fairly small.
176 citations
TL;DR: This paper extends Lawler and Bell's method for solving integer linear programs with 0--1 decision variables so that it can be generally applied to integer quadratic programs.
Abstract: 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.
55 citations