Showing papers on "Heuristic (computer science) published in 1973"

Transmission Planning Using Discrete Dynamic Optimizing

Abstract: This paper is concerned with the application of a new method to the problem of transmission planning expressed as a large finite Markovian sequential process over time. The method recognizes that in solving large sequential problems the complete enumeration of strategies is seldom feasible. The basic idea of Discrete Dynamic Optimizing (DDO) is to combine the deterministic search procedure of dynamic programming with a probabilistic search and a heuristic stopping criterion. The method is designed to take advantage of whatever information is known about the problem. Test results obtained with an experimental program are also given.

MALB—A Heuristic Technique for Balancing Large Single-Model Assembly Lines

Abstract: This paper is concerned with the development of a heuristic algorithm (MALB) for the general solution of large single-model assembly line balancing problems. It is based on the optimum-seeking backtracking method proposed by Mansoor (1964). Excellent results were obtained from tests taken over a wide range of large ALB problems. Over a third of the tests yielded a 100% line efficiency and the overall computation time was 22 sec on an IBM 7040/7044.

Information theory applied to the conversion of decision tables to computer programs

Abstract: Using ideas from information theory, this paper develops a heuristic algorithm that converts a limited entry decision table to a tree structured computer program with near minimum average processing time. The method is applicable to any limited entry decision table and does not require that actions have single rules or that the cost of testing conditions be equal. It is thus more general than the previously published heuristic algorithms. Compared to the optimal algorithm of Reinwald and Soland, this algorithm is easy to code and takes a much smaller translation time; it is thus felt that it is more useful in practice. The algorithm is well suited for manual conversion of decision tables to flowcharts.

Heuristic solution of a signal design optimization problem

Abstract: This paper discusses a heuristic solution procedure for a combinatorial optimization problem that originates in designing signal constellations for modems. The design problem is to place m signals in a two-dimensional space to minimize the average error rate under specified noise conditions, using a maximum-likelihood decoding scheme. Intuitively, it amounts (roughly) to spreading the signal points as far apart as possible, according to the distance measurement implied by the noise function. We show how this problem can be reduced to a discrete one: Given an l by n matrix P, and m 1 , …, i m } of the rows of P that maximizes and then describe an efficient procedure for finding this maximizing set. Experiments indicate that the procedure is a useful tool, both for analysis of existing and proposed signal constellations and for finding new, near-optimum ones.

Design of long-distance telecommunication networks -The Telpak problem

Abstract: The government procures long-distance circuits from communication carriers under a bulk-rate tariff referred to as Telpaks. While physical implementation of circuits is performed by the carriers, the arrangement of circuits into Telpaks for billing purposes is the government's responsibility. Since Telpaks were established in 1961, the government has arranged its circuits into Telpaks on a manual basis. As a result, it has never been able to achieve a minimum cost grouping for over 60 000 circuits among more than 3000 service points. Further, the problem of minimizing the cost of Telpaks has defied classical mathematical approaches because of the large number of service points and circuits. By applying heuristic design techniques, the systematic procedure which is described in this paper has been developed for automating the design of the government Telpak network. Its use has resulted in substantial cost savings over the manually obtained configuration.

An Investigation of Branch and Bound Methods for Solving the Segregated Storage Problem

Abstract: The segregated storage problem consists of determining a minimum cost distribution of products among compartments such that only one product may be stored in a given compartment. This paper explores the computational aspects of using branch and bound methods to solve this problem. Such methods were first suggested for this problem by White and Francis. A variety of approaches are tested, utilizing heuristic node selection and branching decision rules. Three heuristics are also presented which generate solutions for use as initial upper bounds for improving efficiency of the branch and bound procedure or as solutions for very large problems.

An optimal schedule time of a job shop-like disjunctive graph

Abstract: This paper considers the shop scheduling problem which involves both job precedence and machine interference constraints. Based on the graph-theoretical representation of the problem, a branch-and-bound algorithm is proposed for implicitly producing an optimal schedule such that the schedule time is minimized. The algorithm utilizes a set of heuristic rules, in addition to a powerful bounding procedure, to guide the search. An upper bound is also employed to recognize an optimal solution in earlier stages. The procedure is illustrated by a sample problem and its rapid convergence is demonstrated by a set of published problems. The proposed algorithm is compared favorably with existing procedures.

The heuristic synthesis of applications-oriented microcode

Abstract: This paper describes an algorithm for the synthesis of applications-oriented microcode for a dynamically microprogrammable computer. The need for such an algorithm is expressed by Reigel, Faber, and Fisher as an integral step in the solution of the tuning problem, or the problem of modifying a system architecture in order to optimally solve a given problem. This modification of architecture takes place through synthesis of microprograms that are stored in writable control storage. Writable control storage permits each class of user application programs to execute with a specialized instruction set, or architecture. A synthesis algorithm provides a method for generation of these specialized architectures. The required synthesis algorithm should be autonomous, should not require apriori knowledge of user application, and should be adaptable to day-by-day changes in user problems.Current attempts at tuning architectures can be considered as manual tuning. Heuristic tuning is suggested as an automization of the manual tuning process. Several phases of heuristic tuning are summarized. The architecture synthesis phase is considered in depth and an algorithm for microprogram synthesis is given. Several examples of the synthesis algorithm are presented and the expected execution improvements are shown.

A Two-Stage Algorithm For Sequential Decision Problems

Abstract: The Progressive Optimality Algorithm for solution of dynamic programming problems is introduced. An application to a practical production planning problem illustrates the operation of the algorithm. Solutions using both analytical and heuristic methods are compared with previous published solutions to illustrate the effectiveness of the algorithm.

Analysis of a shellsort algorithm

Abstract: D. L. Shell published in 1959, [4], a fast algorithm for internal sorting. R. M. Frank and R. B. Lazarus pointed out in 1960, [3], a disadvantage in the original design of the algorithm and changes were proposed based on heuristic arguments. J. Boothroyd took care of Frank and Lazarus objections in an elegant algorithm published 1963, [1]. If we change Boothroyds algorithm slightly we get an algorithm which, in the general case with the arguments of Frank and Lazarus, is even worse than the original one. On the other hand this algorithm has the advantage that it is easy to analyse theoretically. To the author's knowledge such an analysis has not yet been given for any of the other Shellsort algorithms.

Efficient utilization of algorithms through heuristic learning

Abstract: In the study of problem solving methods, problems have been classified into two broad categories:Structured Problems - for which it is possible to formulate one or more algorithms to find a solution of the problem.Ill-Structured Problems - in which, finding a solution is only possible through the application of a set of general rules and strategies.

Design OF Lo+Distance Telecommunitiation Networks- The Telpak Problem

Abstract: The government procures long-distance circuits from communication carriers under a bulk-rate tariff referred to as Tel- paks. While physical implementation of circuits is performed by the carriers, @e arrangement of cti,cuits into Telpaks for billing purposes is the government's responsibility. Since Telpaks were established in 1961, the government has arranged its circuits into Telpaks on a manual basis. As a result, it has never been able to achieve a mini- mum cost grouping for over 60 000 circuits among more than 3000 service points. Further, the problem of minimizing the cost of Telpaks has defied classical mathematical approaches because of the large number of service points and circuits. By applying heuristic design techniques, the systematic procedure which is described in this paper has been developed for automating the design of the government Telpak network. Its use has resulted in substantial cost savings over the manually obtained configuration.

Problems of complex system modeling and applied mathematical statistics

Abstract: Basic problems in the use of applied mathematical statistics for the modeling of complex systems are considered; the possibility of establishing the uniqueness of a mathematical model of optimal complexity by the group method of data handling (GMDH) is demonstrated. The basic shortcoming of contemporary mathematical statistics is that the models used are too simple because until now in regression analysis only one mean-squared error criterion has been used. To define a mathematical model of optimal complexity GMDH uses not one but two criteria and these two criteria assure a unique solution. The resulting equations are so complex that only the multilayered structure of GMDH allows us to write them down. The method works not only whenK ⩽N but also whenK >N(Kis the number of coefficients of the regression equation,N is the number of interpolation points). Increasing the area of optimization raises the accuracy of the model. The second criterion should be heuristic. Mean-squared error defined on a test sequence is used. The division of data into training and test sequences is the basic object of so-called “goal-directed regularization.” A second shortcoming of contemporary applied mathematical statistics is the absence of “freedom of decision” in the terminology of D. Gabor. The GMDH selection-type algorithm realizes both the self-organization and “freedom of decision” criteria. GMDH is a nonparametric procedure and does not require many of the concepts of mathematical statistics.