Showing papers on "Heuristic (computer science) published in 1975"
TL;DR: In this article, the authors present a solution technique for large scale road network equilibrium assignment and related flow problems with nonlinear costs, without explicitly considering any of the constraints, and without storing all of the individual decision variables.
766 citations
IBM1
TL;DR: This paper presents a technique for generating statistically random sequences to test complex logic circuits and several techniques for assigning these weights and for varying them are discussed on the basis of the primary algorithm.
Abstract: A heuristic method for generating large-scale integration (LSI) test patterns is described. In particular, this paper presents a technique for generating statistically random sequences to test complex logic circuits. The algorithms used to obtain a set of tests by means of weighted logic signal variations are included. Several techniques for assigning these weights and for varying them are discussed on the basis of the primary algorithm. Also described is a means of obtaining a minimal number of test patterns. This approach has proved successful in obtaining fault-detecting patterns.
190 citations
TL;DR: Various uses, including those in group theory and in other integer programming algorithms, as well as applications from the literature, are discussed, and Dynamic programming, branch and bound, search enumeration, heuristic methods, and other solution techniques are presented.
Abstract: A unifying survey of the literature related to the knapsack problem; that is, maximize , subject to and xi ⩾ 0, integer; where vi, wi and W are known integers, and wi (i = 1, 2, …, N) and W are positive. Various uses, including those in group theory and in other integer programming algorithms, as well as applications from the literature, are discussed. Dynamic programming, branch and bound, search enumeration, heuristic methods, and other solution techniques are presented. Computational experience, and extensions of the knapsack problem, such as to the multi-dimensional case, are also considered.
143 citations
TL;DR: In this paper, a discrete minimization problem arising from storage allocation considerations is studied, and a heuristic is proposed and its performance is analyzed.
Abstract: In this paper, a discrete minimization problem arising from storage allocation considerations is studied. Owing to the complexity of finding an optimum solution, a heuristic is proposed and its performance is analyzed. The worst-case ratio of the cost by this algorithm to that by the optimum algorithm is shown to lie between 1.03 and 1.04, implying that this algorithm produces a solution within 4 per cent of the optimum. A generalization of this problem to a class of cost functions is also considered. The worst-case ratios for these functions tend, in the limit, to that of the cost function studied by Graham in his classical paper [1].
120 citations
TL;DR: A heuristic for the knapsack problem that recursively determines a solution by making a variable with smallest marginal unit cost as large as possible is analyzed.
Abstract: This paper analyzes a heuristic for the knapsack problem that recursively determines a solution by making a variable with smallest marginal unit cost as large as possible. Recursive necessary and sufficient conditions for the optimality of such “greedy” solutions and a “good” algorithm for verifying these conditions are given. Maximum absolute error for nonoptimal “greedy” solutions is also examined.
91 citations
01 Mar 1975
TL;DR: In this article, an empirical heuristic learning identification algorithm of Ivakhnenko was modified and used to model an environmental system producing high nitrate levels in agricultural drain water in the Corn Belt.
Abstract: An empirical heuristic learning identification algorithm of Ivakhnenko was modified and used to model an environmental system producing high nitrate levels in agricultural drain water in the Corn Belt. The method amounts to fitting a polynomial to a multi-input single-output response surface. The modifications result in a reduced number of terms in final model equations, a decrease in computational difficulties, and other improvements in the algorithm. This method appears to be advantageous with systems characterized by complexity with many variables and parameters, ill-defined mathematical structures, and limited data. In other words, this algorithm is useful for empirically generating hypotheses about systems of which relatively little is known.
66 citations
TL;DR: In this paper, an algorithm for the heuristic solution of redundancy optimization problems is presented, which takes into account the relative increment in reliability versus decrement in slacks, and a heuristic criterion is introduced to select the stage where redundancy is to be added.
Abstract: An algorithm for the heuristic solution of redundancy optimization problems is presented. There may be any number of constraints which need not be linear. For selecting the stage where redundancy is to be added, a heuristic criterion is introduced which takes into account the relative increment in reliability versus decrement in slacks. The algorithm is simple and requires minimum computation.
61 citations
TL;DR: In this paper, the authors applied the techniques of optimal control and optimization to a practical problem of reducing energy consumed by the Montreal Metro (subway) system, where the problem considered is the determination of tunnel trajectories in the equivalent vertical plane when trains traveling in both directions must follow the same trajectory.
Abstract: The techniques of optimal control and optimization are applied to a practical problem of reducing energy consumed by the Montreal Metro (subway) system The problem considered is the determination of tunnel trajectories in the "equivalent" vertical plane when trains traveling in both directions must follow the same trajectory The problem is first formulated as a control problem with control and state constraints Then, under certain simplifying assumptions, an heuristic method employing a direct search algorithm is presented and used in the trajectory optimization The trajectories are optimized to reduce the sum of the energy consumed by the trains traveling in both directions on the trajectory The results show an average reduction of 773 percent in energy consumption as compared with existing trajectories The trajectories found using the method presented here will be followed in future tunnel construction
51 citations
TL;DR: In this paper, a new approximate method for finding optimal or near optimal solutions to the fixed charge problem is described, which is very rapid compared with previous methods and achieves results which are at least as good or better than previously published results.
Abstract: A new approximate method for finding optimal or near optimal solutions to the fixed charge problem is described. It is very rapid, compared with previous methods and achieves results which are at least as good or better than previously published results. The method is useful in its own right. However, it will also form the basis for the development of an exact solution method to be described in subsequent work.
34 citations
TL;DR: In this article, a heuristic optimization procedure was proposed to find a good ordering and pairing for fixed-point digital filters under dynamic range constraints, where the output noise due to accumulation of roundoff errors is highly dependent upon the order of the sections.
Abstract: In the cascade realization of fixed-point digital filters under dynamic range constraints, the output noise due to accumulation of roundoff errors is highly dependent upon the order of the sections. For recursive filters it also depends on the pole-zero pairing that forms the individual second-order sections. The output noise may vary over several orders of magnitude for different cascade realizations of high-order filters. Therefore an optimization procedure to find a good ordering and pairing is very desirable. We propose a heuristic optimization procedure for finding a "near optimal" solution. The procedure is completely automatic and does not require any knowledgeable judgment. The number of function evaluations required for a filter of N-cascaded sections is proportional to N2. By using this procedure, "near optimal" solutions have been found for a 22nd-order recursive filter in 23 s, and for a 55th-order nonrecursive filter in 37.5 s, on an IBM 360-91 computer.
TL;DR: A problem oriented linear string representation for trees and the concept of grammar are introduced as basic tools for symbolic computation of trees and planar graphs and the need of optimal algorithmic techniques is pointed out.
Abstract: SUMMARY Difficulties in recognition of planarity of graphs in the context of layout problems are reviewed. A problem oriented linear string representation for trees and the concept of grammar are then introduced as basic tools for symbolic computation of trees and planar graphs. A heuristic method to solve the layout problem is next discussed and the need of optimal algorithmic techniques is pointed out. Finally the application of list processing techniques for manipulation by computer is suggested.
19 May 1975
TL;DR: An appropriate, integrated support methodology to support the needs of the computer center manager vis-a-vis these issues is lacking.
Abstract: The design, sizing and tuning of computer systems is a persistent, expensive, time-consuming, and difficult problem. An appropriate, integrated support methodology to support the needs of the computer center manager vis-a-vis these issues is lacking. Although the performance literature related to this topic is voluminous, much of it is directed to the vendor rather than the computer center manager.
TL;DR: A general model of the learning decision process which applies artificial intelligence techniques to programming decision-making by weighing evidence, under conditions of uncertainty, in recurrent situations is developed.
Abstract: Most of decision-making in the real world takes place under conditions of uncertainty, because usually the probability laws characterizing the decision situation are initially unknown. Formal treatment of such situations requires programming "judgement" or "intelligence". Thus this paper presents a computeroriented conceptual framework for decision analysis under conditions of uncertainty which enables the application of artificial intelligence techniques. We have developed a general model of the learning decision process which applies artificial intelligence techniques to programming decision-making by weighing evidence, under conditions of uncertainty, in recurrent situations. Specifically, we use generalized perceptron-type pattern recognition techniques, heuristic methods and learning system theory. An illustration is given in investment analysis and the experimental results indicate that through machine learning algorithms we can gradually reduce uncertainty in decision analysis and improve the decision system's performance.
TL;DR: A simple algorithm for the rapid approximate solution of the single terminal traveling salesman problem is put forward and is shown to be least costly when computation cost dominates the total cost.
01 Jan 1975
TL;DR: This paper will give a survey of the different methods to approach combinatorial optimization problems, the main emphasis will lie upon integer programming modelling, tree-search (branch and bound) methods, and heuristic methods.
Abstract: This paper will give a survey of the different methods to approach combinatorial optimization problems. The main emphasis will lie upon integer programming modelling, tree-search (branch and bound) methods, and heuristic methods. The paper is divided into the following sections: 1. Objectives of the paper; 2. Morphology of combinatorial problems; 3. The general approach to solving combinatorial problems; 4. Integer programming formulations; 5. Explicit enumeration; 6. Tree-search (branch and bound) methods; 7. Heuristic methods; 8. Conclusions.
01 Dec 1975
TL;DR: Numerical results for a variety of network configurations indicate that the heuristic algorithm, while not theoretically convergent, yields practicable low cost solutions with substantial savings in computer processing time and storage requirements.
Abstract: The problems of file allocation and capacity assignment in a fixed topology distributed computer network are examined. These two aspects of the design are tightly coupled through an average message delay constraint. The objective is to allocate copies of information files to network nodes and capacities to network links so that a minimum cost is achieved subject to network delay and file availability constraints. A model for solving the problem is formulated and the resulting optimization problem is shown to fall into a class of non-linear integer programming problems. Deterministic techniques for solving this class of problems are computationally cumbersome even for small sized problems. A new heuristic algorithm is developed, based on a decomposition technique which greatly reduces the computational complexity of the problem. Numerical results for a variety of network configurations indicate that the heuristic algorithm, while not theoretically convergent, yields practicable low cost solutions with substantial savings in computer processing time and storage requirements. Moreover, it is shown that this algorithm is capable of solving realistic network problems whose solution using deterministic techniques is computationally intractable.
Journal Article•
TL;DR: In this paper, a branch and bound optimization procedure is presented for finding the routes which minimize travel time for several vehicles to serve customers from a central depot, where branches are formed by successively eliminating infeasibilities (such as partial routes) which do not connect to the depot or routes which exceed constraints on the vehicles.
Abstract: In this paper, a branch and bound optimizing procedure is presented for finding the routes which minimize travel time for several vehicles to serve customers from a central depot. The branches are formed by successively eliminating infeasibilities (such as partial routes) which do not connect to the depot or routes which exceed constraints on the vehicles. The lower bounds are determined by solving assignment problems without regard to feasibility. A good initial upper bound is determined by using a heuristic. The method requires more computing time than for the m-salesman traveling salesman problem; and some suggestions are made for reducing this time. (A) /TRRL/
01 Jan 1975
01 Oct 1975
TL;DR: In this paper, the problem of locating capacitated plants at some of a number of potential locations, so as to minimize the discounted sum of fixed costs incurred for open plants and variable supply costs is considered.
Abstract: : The problem is that of locating capacitated plants at some of a number of potential locations, so as to minimize the discounted sum of fixed costs incurred for open plants and variable supply costs. Both the costs and demands may vary arbitrarily over time. The results of optimal LIFO branch-and-bound and of heuristic algorithms using various branching variable selection rules and some new bounds are presented. (Author)
TL;DR: In this paper, the authors extended the CBH algorithm for solving capital budgeting problems to solve problems with chance constraints of the form Pr where each αi is specified, and the objective function is linear, being the expectation-variance form of utility function.
Abstract: The capital budgeting heuristic (CBH) algorithm for solving capital budgeting problems is extended to solve problems with chance constraints of the form Pr where each αi is specified. The objective function is linear, being the expectation-variance form of utility function. Computational experience is described using the modified CBH to solve a set of 24 problems, each having 30 independent projects and 5 budget periods.
TL;DR: In this paper, an approach to a large scale network routing problem with nonlinear cost function is described, along with an example of its application, which involves a multistage construction process.
Abstract: An approach to a large scale network routing problem with nonlinear cost function is described, along with an example of its application. The approach to the problem involves a multistage construction process. This approach is applied to the telpaking problem. Results are obtained in applying this method to a 53 node sample problem.
Journal Article•
01 Apr 1975
TL;DR: An heuristical method (the Eight-and-a-Half-Fold Way) for attacking this dilemma by means of seven overlapping interdisciplinary models and dichotomous systems policy pairings is disclosed.
Abstract: : The state of the art in data processing systems for tactical military intelligence is storage and retrieval. The report is directed at creating a system for higher-level man-machine inference. This broad objective results in a dilemma because of the difficulty in making it a well posed problem. An heuristical method (the Eight-and-a-Half-Fold Way) for attacking this dilemma by means of seven overlapping interdisciplinary models and dichotomous systems policy pairings is disclosed. Consideration of the models leads to: A robust basis for introducing a priori intelligence, and information theory measure of performance for system control, and a flow-chart design. The design reflects the Blue-Red interactive coupling of warfare and is directed at exposing the military risk caused by intelligence production by comparing Game Theoretic and Bayesian style inferences. The report stresses heuristic chronology rather than system description and justification. Some insights on heuristics creativity in system engineering are described and justified.
01 Feb 1975
TL;DR: A reasonably efficient implementation of Scolnik's linear programming approach is described, but computational experience leads us to believe that the method is too costly even for this modest objective.
Abstract: In this note we describe a reasonably efficient implementation of Scolnik's linear programming approach. We became interested in using the code to test the usefulness of this approach as a starting heuristic or "crashing" technique, as the method is known to fail in general. Our computational experience, however, leads us to believe that the method is too costly even for this modest objective.
TL;DR: When first conceived by Wilkes and his Cambridge colleagues, the concepts of microprogramming were a valuable extension and formalization of the more heuristic, function-by-function, logic-process which had previously been used to design and implement computers using elementary electronic and logical components and circuits.
Abstract: When first conceived by Wilkes and his Cambridge colleagues, the concepts of microprogramming were a valuable extension and formalization of the more heuristic, function-by-function, logic-process which had previously been used to design and implement computers using elementary electronic and logical components and circuits. As such, I have no quarrel either with the concept or its implementation.
01 Jan 1975
TL;DR: In this article, a modification and extension to the Burgess and Killebrew heuristic resource leveling procedure for project networks is presented, where the objective function is the minimization of the sum of the squared errors in each time period (deviations around the mean usage).
Abstract: This thesis presents a modification and extension to the Burgess and Killebrew heuristic resource leveling procedure for project networks. In contrast to previous algorithms appearing in the literature, the objective function of this algorithm is the minimization of the sum of the squared errors in each time period (deviations around the mean usage) of all resources over the duration of the project. This objective function continues the search for an improved schedule beyond that of previous algorithms with their associated objective functions. One important feature is that the algorithm tends to reduce the number of periods that a resource is idle during its duration on the project.
01 Jan 1975
TL;DR: In this article, a model of a distributed computer system for transaction processing is described and the system configuration problem is formulated as a problem of determining transaction allocation, routing, processor allocation and line allocation to satisfy certain performance requirements and design constraints.
Abstract: A model of a distributed computer system for transaction processing is described. The system configuration problem is formulated as a problem of determining transaction allocation, routing, processor allocation and line allocation to satisfy certain performance requirements and design constraints. A heuristic design procedure is described. Topics for further investigation are discussed.