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Showing papers on "Heuristic published in 1983"


Journal ArticleDOI
TL;DR: Several formulations and heuristic algorithms for solving the vehicle routing problem when the demands at individual delivery (pickup) locations behave as random variables are presented.

278 citations


Journal ArticleDOI
TL;DR: The EURISKO program embodies this language, and it is described in this paper, along with its results in eight task domains: design of naval fleets, elementary set theory and number theory, LISP programming, biological evolution, games in general, the design of three-dimensional VLSI devices, the discovery of heuristics which help the system discover heuristic, and theiscovery of appropriate new types of 'slots' in each domain.

258 citations


Book ChapterDOI
01 Jan 1983
TL;DR: When augmented with a heuristic for noting common divisors, the BACON.4 system is able to replicate a number of early chemical discoveries, arriving at Proust’s law of definite proportions, Gay-Lussac's law of combining volumes, Cannizzaro's determination of the relative atomic weights, and Prout's hypothesis.
Abstract: BACON.4 is a production system that discovers empirical laws. The program represents information at varying levels of description, with higher levels summarizing the levels below them. BACON.4 employs a small set of data-driven heuristics to detect regularities in numeric and nominal data. These heuristics note constancies and trends, causing BACON.4 to formulate hypotheses, to define theoretical terms, and to postulate intrinsic properties. The introduction of intrinsic properties plays an important role in BACON.4’s rediscovery of Ohm’s law for electric circuits and Archimedes’ law of displacement. When augmented with a heuristic for noting common divisors, the system is able to replicate a number of early chemical discoveries, arriving at Proust’s law of definite proportions, Gay-Lussac’s law of combining volumes, Cannizzaro’s determination of the relative atomic weights, and Prout’s hypothesis. The BACON.4 heuristics, including the new technique for finding common divisors, appear to be general mechanisms applicable to discovery in diverse domains.

193 citations


Journal ArticleDOI
TL;DR: Computational tests with quadratic assignment problems (QAP) showed that it finds very good suboptimal solutions in moderate time and behaves computationally stable.

137 citations


Journal ArticleDOI
A. Nadas1
TL;DR: The currently used method of maximum likelihood, while heuristic, is shown to be superior under certain assumptions to another heuristic: the method of conditional maximum likelihood.
Abstract: The choice of method for training a speech recognizer is posed as an optimization problem. The currently used method of maximum likelihood, while heuristic, is shown to be superior under certain assumptions to another heuristic: the method of conditional maximum likelihood.

135 citations


Journal ArticleDOI
TL;DR: The results seem to indicate that an alternate location-allocation-savings procedure and a saving-drop procedure are promising, and a newspaper delivery system consisting of 4500 customers is solved.

122 citations


Journal ArticleDOI
TL;DR: An O(N2) heuristic is developed to solve the single vehicle many-to-many Euclidean Dial-A-Ride problem based on the Minimum Spanning Tree of the modes of the problem, which exhibits statistical stability over a broad range of problem sizes.
Abstract: We develop an O(N2) heuristic to solve the single vehicle many-to-many Euclidean Dial-A-Ride problem. The heuristic is based on the Minimum Spanning Tree of the modes of the problem. The algorithm's worst case performance is four times the length of the optimal Dial-A-Ride tour. An analysis of the algorithm's average performance reveals that in terms of sizes of single-vehicle problems that are likely to be encountered in the real world (up to 100 nodes) and in terms of computational complexity, the O(N2) heuristic performs equally well, or, in many cases, better than heuristics described earlier by Stein for the same problem. The performance of the heuristic exhibits statistical stability over a broad range of problem sizes.

98 citations


Journal ArticleDOI
TL;DR: In this paper, the problem of identifying the promising positions, given information on a sample of individuals, is formulated as a Mixed Integer Nonlinear Program (MILP) and an exact solution algorithm which is computationally feasible for small samples is developed.
Abstract: This study examines the problem faced by a firm which wishes to position a new choice object in an existing product class. It is assumed that both the consumer and the firm are involved in a two-stage decision process. The consumer first decides on his budget for the product class. He then evaluates, within the product class, that subset of competing objects which have prices approximately equal to his budget constraint. This evaluation is performed through a weighted multi-attribute utility model. The product classes considered here are ones for which the consumer has a finite ideal level on each attribute. The consumer is hypothesized to choose, without error, that object which is closest to his ideal. Different individuals are assumed to be heterogeneous in both attribute weights and ideal levels. The firm is assumed to first identify, in the attribute space which contains ideal points and competing objects, promising product positions which would attract a large number of consumers. It then evaluates these positions in terms of costs and resulting profits. The problem of identifying the promising positions, given information on a sample of individuals, is formulated as a Mixed Integer Nonlinear Program. Due to the inability of such a program to solve even small sample problems, the spatial properties of the problem are examined. An exact solution algorithm which is computationally feasible for small samples is developed. It is based on an examination of intersections of indifference hyperellipsoids. For larger sample problems an efficient heuristic which is an extension of the random point search used in nonlinear programming is provided. It involves random line search procedures for our noncontinuous-type problem. The positioning approach and the heuristic are illustrated in a simulated positioning problem in the small car market.

97 citations


Journal ArticleDOI
TL;DR: A branch and bound algorithm which can solve assembly line balancing probems with nine modifications to the originally formulated problem of minimizing the required number of assembly stations, given a cycle time, a set of tasks with given deterministic performance times, and between-task precedence relationships.
Abstract: This paper describes a branch and bound algorithm which can solve assembly line balancing probems with nine modifications to the originally formulated problem of minimizing the required number of assembly stations, given a cycle time, a set of tasks with given deterministic performance times, and between-task precedence relationships. The first two formulation modifications are those of permitting planned imbalance in the total of task performance times at each assembly station, and allowing specific tasks to be assigned to specific types of assembly stations. Seven further problem modifications can be solved by the proposed algorithm, or by any algorithm or heuristic that can solve problems containing these first two modifications. They are: treatment of stochastic task performance times on unpaced lines; requirement of particular tasks to be assigned to particular stations; requirement of task groupings according to task skill levels; requirement of particular tasks to be assigned to only a left-of-line or right-of line station; required task separations; some mixed model situations; and where paralleling of a specified task into two or more stations is permitted. The algorithm is presented in both conceptual and detailed form. Computer computation times to solve a selected cross-sectional sample of problems are provided.

96 citations


Proceedings Article
Eugene Charniak1
22 Aug 1983
TL;DR: It is shown that the objections most frequently raised against the use of Bayesian statistics within the Al-in-medicine community do not seem to hold and it is argued thatBayesian statistics is perfectly compatible with heuristic solutions to the multiple disease problem.
Abstract: In this paper, we show that the objections most frequently raised against the use of Bayesian statistics within the Al-in-medicine community do not seem to hold. In particular, we will show that the independence assumptions required to make Bayesian statistics computationally feasible are not nearly as damaging as has been claimed. We will also argue that Bayesian statistics is perfectly compatible with heuristic solutions to the multiple disease problem.

95 citations


Journal ArticleDOI
TL;DR: Three heuristic search algorithms, called algorithms a, b and c, are presented and it is shown that on the whole a and b are inferior to c, which is a slightly modified version of b.
Abstract: Three heuristic search algorithms, called algorithms a, b and c, are presented. Their performance, with the admissibility condition relaxed, is compared using the following two criteria: (i) number of node expansions and (ii) cost of solution found. First, a general comparison is made. In this process some variations and extensions of c are also considered. Subsequently, two types of heuristic estimates, called proper and path dependent, are defined, and the algorithms are reexamined. It is shown that on the whole a (Nilsson's algorithm) and b (Martelli's algorithm) are inferior to c, which is a slightly modified version of b. 7 references.

Journal ArticleDOI

Journal ArticleDOI
TL;DR: Based on reported costs, it is shown that the initial sequences synthesized for the test problems by the new heuristic method are cheaper than those obtained by other ordered heuristic methods.
Abstract: A simple heuristic method for the systematic synthesis of initial sequences for multicomponent separations is proposed and applied to a number of synthesis problems which have been solved previously using other methods. Based on reported costs, it is shown that the initial sequences synthesized for the test problems by the new heuristic method are cheaper than those obtained by other ordered heuristic methods. These initial sequences are also either identical to or at most a few percents higher in costs than those optimum sequences obtained by other algorithmic, heuristic-algorithmic and heuristic-evolutionary methods. The new method is straightforward to apply by hand and it does not require any mathematical background and computational skill from the user.

Journal ArticleDOI
TL;DR: An algorithm based on the branch-and-bound technique to get an optimal solution of job lateness is presented and a heuristic rule is also presented which gives a near optimal solution.

Journal ArticleDOI
TL;DR: This article develops the notion of domain size in fields such as chess, borrowing the concept of a chunk from human memory, and develops a heuristic rule for choiceamongmethods, proposing this rule as one of many possible rules.
Abstract: This article attempts to characterize the general problem of selecting methods for decision-making. The traditional rational approach to choice is from economics, which offers expected-value maximization. The task environment of decision method selection, however, does not seem to provide the data necessary for carrying out the expected-value calculations. Our approach uses methods from the field of cognitive science. We develop the notion of domain size in fields such as chess, borrowing the concept of a chunk from human memory. We then develop a heuristic rule for choiceamongmethods. We propose this rule as one of many possible rules.

Journal ArticleDOI
TL;DR: In this paper, an optimal portfolio subject to a size constraint can be found by an implicit enumeration algorithm, that is much faster than a previous approach and moreover allows the inclusion of securities whose β-coefficient is negative.
Abstract: Portfolios that are risk-return efficient in the sense of Markowitz sometimes contain too many securities to be attractive to the small investor. An optimal portfolio subject to a size constraint can be found by an implicit enumeration algorithm, that is much faster than a previous approach and moreover allows the inclusion of securities whose β-coefficient is negative. A simple and computationally very efficient heuristic method that almost always produces optimal portfolios is described as well.

Journal ArticleDOI
TL;DR: It is demonstrated that these heuristics can be obtained by the process of deleting constraints from the original problem and solving the relaxed problem which ensues, and a scheme for generating such heuristic mechanically is outlined.
Abstract: This paper explores the paradigm that heuristics are discovered by consulting simplified models of the problem domain. After describing the features of typical heuristics on some popular problems, we demonstrate that these heuristics can be obtained by the process of deleting constraints from the original problem and solving the relaxed problem which ensues. We then outline a scheme for generating such heuristics mechanically, which involves systematic refinement and deletion of constraints from the original problem specification until a semidecomposable model is identified. The solution to the latter constitutes a heuristic for the former.

Journal ArticleDOI
TL;DR: Some of the issues that arise with the use of a heuristic method which, on the basis of experience of judgement, seems likely to yield good solutions but which cannot guarantee optimality are discussed.
Abstract: For a variety of reasons, the finding of an optimal solution is impractical for many O.R. problems. A common way of overcoming this unhappy state of affairs is the development of heuristic (approximate) methods. The purpose of this paper is to discuss some of the issues that arise with such an approach-that is, the use of a method which, on the basis of experience of judgement, seems likely to yield good solutions but which cannot guarantee optimality. The use of such methods is motivated by the emergence of the theory of NP-completeness, i.e. the study of the complexity of algorithms, which is briefly introduced. A number of heuristic methods are presented in order to illustrate some of the ideas discussed. Heuristic procedures are classified according to design. Some of the problems of both how to design effective heuristics and how to use heuristics in the real world are discussed.



Journal ArticleDOI
TL;DR: It is shown here that the requirement that the heuristic be consistent can be relaxed to the one that theHeuristic be merely admissible, which should encourage wider use of HS in applications.

Proceedings ArticleDOI
01 Dec 1983
TL;DR: A practical method for generating optimal schedules for online train traffic control in disturbed situations and it is shown that each subproblem generated in the second part, which is a linear programming problem, can be easily calculated without using the ordinary simplex method.
Abstract: A practical method for generating optimal schedules for online train traffic control in disturbed situations is proposed. This scheduling problem is formulated to a 0-1 mixed integer programming problem. The method of solution proposed here is mainly divided into two parts: the first part generates a suboptimal solution by a heuristic method based upon "Production System", and the second produces an optimal one by the branch-and-bound method using the above sub-optimal value for the initial bound. It is shown that each subproblem generated in the second part, which is a linear programming problem, can be easily calculated without using the ordinary simplex method. Some examples show that the proposed method has enough efficiency for practical use in both computational time and storage.

Proceedings Article
22 Aug 1983
TL;DR: This paper examines the optimality of A*, in the sense of expanding the least number of distinct nodes, over three classes of algorithms which return solutions of comparable costs to that found by A*.
Abstract: This paper examines the optimality of A*, in the sense of expanding the least number of distinct nodes, over three classes of algorithms which return solutions of comparable costs to that found by A*. We first show that A* is optimal over those algorithms guaranteed to find a solution at least as good as A*'s for every heuristic assignment h. Second, we consider a wider class of algorithms which, like A*, are guaranteed to find an optimal solution. (i.e., admissible) if all cost estimates are optimistic (i.e., h≤h*). On this class we show that A* is not optimal and that no optimal algorithm exists unless h is also consistent, in which case A* is optimal. Finally we show that A* is optimal over the subclass of best-first algorithms which are admissible whenever h≤h*.

Journal ArticleDOI
TL;DR: The Steiner problem in graphs is to find a subgraph of an undirected graph which spans a set of vertices and edges of the graph as discussed by the authors. But this problem is NP-complete.
Abstract: Consider an undirected graph G = (V, E) with positive edge weights and a nonempty set S ⊆ V, where Vand E are the sets of vertices and edges of G respectively. The Steiner problem in graphs is that of finding a subgraph of G which spans S and is of minimum total edge weight. In this paper we survey solution procedures for this problem. As the associated decision problem is NP-Complete we place special emphasis on heuristic methods of solution. We also examine special cases, related problems, and applications. The paper concludes with ideas for the development of new, efficient heuristics.

Book ChapterDOI
28 Mar 1983
TL;DR: Algorithms are presented, based on Breuer's grow factor algorithm, for finding common subexpressions in a set of multivariate polynomials with integer coefficients, and for communicating subresults between both sets, called "noise conquering".
Abstract: Heuristic, structure preserving algorithms are presented, based on Breuer's grow factor algorithm, for finding common subexpressions in a set of multivariate polynomials with integer coefficients. It is shown how these polynomials can be decomposed into a set of linear expressions and a set of monomials, interrelated via hierarchical structure information, the "noise". Then algorithms are shortly discussed to find common subexpressions, by reducing the arithmetic complexity of both sets, when viewing them as blocks of straightline code, and for communicating subresults between both sets, called "noise conquering". Further extensions are shortly indicated.

Journal ArticleDOI
TL;DR: In this article, a portfolio manager selects a portfolio by maximizing the returnto-risk ratios of the securities that constitute the portfolio, the performance of this "heuristic" is sensitive to the choice of risk measure in the return-to-risk ratio.
Abstract: Assuming that a portfolio manager selects a portfolio by maximizing the returnto-risk ratios of the securities that constitute the portfolio, the performance of this “heuristic” is sensitive to the choice of risk measure in the return-to-risk ratio. Using sixty month holding periods and second degree stochastic dominance to evaluate the performance of the portfolio selection heuristic; the mean absolute deviation, beta and target semivariance were found to be superior to the variance and the mean semivariance. In addition, the heuristic with the superior risk measures provided performance comparable to the optimal single index model.

Journal ArticleDOI
Alan Farley1
TL;DR: In this paper, two procedures are presented which take an existing pattern and attempt to rearrange it to meet the restrictions imposed by the glass industry, which imposes limitations upon the positioning of cuts within the stock-plate relative to other cuts.

Journal ArticleDOI
TL;DR: A heuristic to find a good matching on n points in the plane that has the advantages of being trivial to code and of being indifferent to the choice of metric or the probability distribution from which the points are drawn.

Journal ArticleDOI
TL;DR: Characterizing typical training programs on “learning to think,” in terms of their breadth, generality and specificity, and setting out criteria appropriate for an aptitude-treatment interaction (ATI) study of the relationship between tasks, training methods and student attributes are characterized.
Abstract: This paper begins by characterizing typical training programs on “learning to think,” in terms of: their breadth, generality and specificity; the types of tasks and solution methods; and the guidance to students (assigning them to alternative programs; registering and reacting to their mistakes, requests for help, etc.). The paper then outlines some strengths and weaknesses of past research and sets out criteria appropriate for an aptitude-treatment interaction (ATI) study of the relationship between tasks, training methods and student attributes. Finally, an account is given of exemplary ATI experiments, administered via a computer, on the immediate and delayed effects of teaching algorithmic and heuristic solution methods. The tasks involve inductive reasoning (the extrapolation of number series) and deductive reasoning (the evaluation of syllogisms).

Journal ArticleDOI
TL;DR: Algorithms for performing strategic search using both deterministic and non-deterministic (multiple) strategies are examined and some examples are given which indicate that strategic search can outperform standard heuristic search methods.