Showing papers on "Heuristic published in 1985"
Book•
01 Jan 1985TL;DR: In this article, a broad range of well-structured problems are solved in "expert systems" by the method of heuristic classification, which has a characteristic inference structure that systematically relates data to a pre-enumerated set of solutions by abstraction, heuristic association, and refinement.
Abstract: A broad range of well-structured problems--embracing forms of diagnosis, catalog selection, and skeletal planning--are solved in "expert systems" by the method of heuristic classification. These programs have a characteristic inference structure that systematically relates data to a pre-enumerated set of solutions by abstraction, heuristic association, and refinement. In contrast with previous descriptions of classification reasoning, particularly in psychology, this analysis emphasizes the role of a heuristic in routine problem solving as a non-hierarchical, direct association between concepts. In contrast with other descriptions of expert systems, this analysis specifies the knowledge needed to solve a problem, independent of its representation in a particular computer language. The heuristic classification problem-solving model provides a useful framework for characterizing kinds of problems, for designing representation tools, and for understanding non-classification (constructive) problem-solving methods.
972 citations
TL;DR: A graph matching approach for solving the task assignment problem encountered in distributed computing systems with a cost function defined in terms of a single unit, time, and a new optimization criterion, called the minimax criterion, based on which both minimization of interprocessor communication and balance of processor loading can be achieved.
Abstract: A graph matching approach is proposed in this paper for solving the task assignment problem encountered in distributed computing systems. A cost function defined in terms of a single unit, time, is proposed for evaluating the effectiveness of task assignment. This cost function represents the maximum time for a task to complete module execution and communication in all the processors. A new optimization criterion, called the minimax criterion, is also proposed, based on which both minimization of interprocessor communication and balance of processor loading can be achieved. The proposed approach allows various system constraints to be included for consideration. With the proposed cost function and the minimax criterion, optimal task assignment is defined. Graphs are then used to represent the module relationship of a given task and the processor structure of a distributed computing system. Module assignment to system processors is transformed into a type of graph matching, called weak homomorphism. The search of optimal weak homomorphism corresponding to optimal task assignment is next formulated as a state-space search problem. It is then solved by the well-known A* algorithm in artificial intelligence after proper heuristic information for speeding up the search is suggested. An illustrative example and some experimental results are also included to show the effectiveness of the heuristic search.
358 citations
TL;DR: Two versions of the problem are formulated and various heuristic approaches are presented and evaluated, finding the optimum solution is computationally prohibitive, even for modest size problems.
Abstract: Recently, a number of articles have appeared in the marketing literature dealing with single product design optimization. The present paper represents a start toward extending this research to product line decisions. We first formulate two versions of the problem and describe their various characteristics. Since finding the optimum solution is computationally prohibitive, even for modest size problems, various heuristic approaches are presented and evaluated. Applications involving synthetic and real data are discussed. The paper concludes with comments on related problems and future application areas.
332 citations
TL;DR: In this paper, a flight-to-gate assignment problem is solved using two methods: (1) a linear programming relaxation of an integer program formulation and (2) a heuristic.
Abstract: The airport flight-to-gate assignment problem is solved using two methods: (1) a linear programming relaxation of an integer program formulation and (2) a heuristic. The objective is to minimize passenger walking distances within the airport terminal area through a judicious gate assignment policy. An actual flight schedule for an average day at Toronto International Airport is used to compare existing walking distances, obtained from the original assignment, with results from the two methods. The results indicated that the original assignment had a 32% higher average per passenger walking distance than the minimum possible distance given by the LP solution. The heuristic’s performance was near optimal; it gave an average walking distance which was only 3.9% greater than the minimum. Computation times for the heuristic are 3.4 CPU seconds per run, while the linear program consumes 386 seconds per run on an IBM 370/168. In addition, if the heuristic is solved first and its solution is used as an initial fe...
181 citations
TL;DR: In this article, a simple heuristic for determining the p-centre of a finite set of weighted points in an arbitrary metric space is described. But the computational effort is O(np) for an n-point set and the ratio of the objective function value of the heuristic solution to that of the optimum is bounded.
180 citations
TL;DR: This paper presents an investigation of the performance of heuristics in a complex dynamic setting, characterized by repeated decisions with feedback, which indicates that task characteristics often studied in past research have less influence on performance relative to feedback-related aspects of the task.
Abstract: Research on cognitive processes in decision making has identified heuristics that often work well but sometimes lead to serious errors. This paper presents an investigation of the performance of heuristics in a complex dynamic setting, characterized by repeated decisions with feedback. There are three components: 1 A simulated task resembling medical decision problems diagnosis and treatment is described. 2 Computer models of decision strategies are developed. These include models based on cognitive heuristics as well as benchmark strategies that indicate the limit of the heuristic strategies' performance. The upper benchmark is based on statistical decision theory, the lower one on random trial and error. 3 Selected task characteristics are systematically varied and their influence on performance evaluated in simulation experiments. Results indicate that task characteristics often studied in past research e.g., symptom diagonosticity, disease base-rates have less influence on performance relative to feedback-related aspects of the task. These dynamic characteristics are a major determinant of when heuristics perform well or badly. The results also provide insights about the costs and benefits of various cognitive heuristics. In addition, the possible contribution of this research to the design and evaluation of decision aids is considered.
167 citations
Proceedings Article•
18 Aug 1985TL;DR: MORRIS is described, a heuristic problem solver that measures the utility of plan fragments to determine whether they are worth learning and comparing the performance of MORRIS to a less selective learning system.
Abstract: Problem solving programs that generalize and save plans in order to improve their subsequent performance inevitably face the danger of being overwhelmed by an ever-increasing number of stored plans. To cope with this problem, methods must be developed for selectively learning only the most valuable aspects of a new plan. This paper describes MORRIS, a heuristic problem solver that measures the utility of plan fragments to determine whether they are worth learning. MORRIS generalizes and saves plan fragments if they are frequently used, or if they are helpful in solving difficult subproblems. Experiments are described comparing the performance of MORRIS to a less selective learning system.
164 citations
164 citations
TL;DR: A heuristic for the effective cooperation of multiple decentralized components of a job scheduling function that can dynamically adapt to the quality of the state information being processed and is based on Bayesian decision theory.
Abstract: There is a wide spectrum of techniques that can be aptly named decentralized control. However, certain functions in distributed operating systems, e.g., scheduling, operate under such demanding requirements that no known optimal control solutions exist. It has been shown that heuristics are necessary. This paper presents a heuristic for the effective cooperation of multiple decentralized components of a job scheduling function. An especially useful feature of the heuristic is that it can dynamically adapt to the quality of the state information being processed. Extensive simulation results show the utility of this heuristic. The simulation results are compared to several analytical models and a baseline simulation model. The heuristic itself is based on the application of Bayesian decision theory. Bayesian decision theory was used because its principles can be applied as a systematic approach to complex decision making under conditions of imperfect knowledge, and it can run relatively cheaply in real time.
123 citations
TL;DR: In this article, the two levels of personnel scheduling, that is, determination of the days an employee should work and determination of when an employee's time should start each workday, are integrated.
105 citations
TL;DR: In this paper, the optimum placement of control actuators for correcting static deformations is described for the case where control locations have to be selected from a large set of available sites, so that integer programing methods are called for.
TL;DR: A multiple criteria model for combining quantitative and qualitative approaches to plant layout does an excellent job, especially in separating those departments which have an undesirable closeness rating.
Abstract: Thin paper presents a multiple criteria model for combining quantitative and qualitative approaches to plant layout. The quantitative factor (work flow) is weighted by the qualitative factor (closeness rating) to form the model. The objective is to minimize the total weighted work flow volume between departments. An heuristic approach is used on an initial layout to improve it in a multiple puss step-by-step pairwise exchange. The results indicate that the model does an excellent job, especially in separating those departments which have an undesirable closeness rating.
TL;DR: Two new marking algorithms for AND/OR graphs called CF and CS are presented and it is proved that CF can be followed by CS to get optimal solutions, provided the sumcost criterion is used and the first discriminant equals the second.
Abstract: Two new marking algorithms for AND/OR graphs called CF and CS are presented. For admissible heuristics CS is not needed, and CF is shown to be preferable to the marking algorithms of Martelli and Montanari. When the heuristic is not admissible, the analysis is carried out with the help of the notion of the first and second discriminants of an AND/OR graph. It is proved that in this case CF can be followed by CS to get optimal solutions, provided the sumcost criterion is used and the first discriminant equals the second. Estimates of time and storage requirements are given. Other cost measures, such as maxcost, are also considered, and a number of interesting open problems are enumerated.
23 Aug 1985
TL;DR: An outline of an attack that is used successfully to break iterated knapsacks is presented, although it is not provided that the attack almost always works.
Abstract: This paper presents an outline of an attack that we have used successfully to break iterated knapsacks. Although we do not provide a proof that the attack almost always works, we do provide some heuristic arguments. We also give a detailed description of the examples we have broken.
TL;DR: A mobile robot is required to navigate around barriers in an unexplored environment and some heuristic strategies to aid such navigation are discussed, showing their usefulness in obstacle avoidance.
Abstract: A mobile robot is required to navigate around barriers in an unexplored environment. Some heuristic strategies to aid such navigation are discussed here. Being heuristic, these methods can be neither exhaustively tested nor proved effec tive in all cases. However, examples are given to demonstrate their usefulness in obstacle avoidance. In a simple case of sufficient generality, the heuristics are shown to be effective.
TL;DR: The competing-hypotheses heuristic is discussed within the context of diagnostic problem-solving models derived from the literature on medical decision making and clinicopathological conference case records and suggested that the heuristic may be useful as a complement to clinical judgment.
Abstract: Evaluating the same diagnostic information across the plausible competing diagnoses is a practical strategy (ie, heuristic) to guide decision making in the face of uncertainty. The prevalence of use of this competing-hypotheses heuristic by 89 first-year house officers was examined in three simulated patient cases. Results indicated that only a minority (24%) of the house officers selected optimal diagnostic information consistent with this Bayesian heuristic across all three cases. Almost all (97%) of the house officers selecting optimal diagnostic information were able to identify the most probable diagnosis specified by Bayes' theorem, while only a chance number (53%) of house officers selecting nonoptimal information were able to identify the most probable diagnosis. The competing-hypotheses heuristic is discussed within the context of diagnostic problem-solving models derived from the literature on medical decision making and clinicopathological conference case records. It is suggested that the heuristic, which does not necessitate any mathematical calculations, may be useful as a complement to clinical judgment. ( JAMA 1985;253:2858-2862)
TL;DR: The results show that, depending on the range of values that apply in a given practical situation, either i any of a large number of methods will yield good performance or ii a carefully selected method can achieve superior performance.
Abstract: This paper studies the numerical computation of the two parameters the reorder level s and the order up to level S of inventory policies for discrete time shortage cost systems. Our goal is to obtain approximately optimal policies with little computational effort. The paper introduces three new methods that are designed to achieve this goal. Two of the methods are shortcuts based on the method of Freeland and Porteus and one is a heuristic that makes several modifications to a standard continuous review approximation. The paper provides a fairly detailed survey of other methods for easily computing approximately optimal inventory policies. It then numerically compares all these methods on a reasonably broad range of problems. One of the shortcuts and the new heuristic method performed very well: the percentage error of their average costs was approximately 1%. Some commonly cited competing methods had percentage errors of over 10% and a commonly cited continuous review approximation had a percentage error of over 80%. To study the effect of extreme parameter choices in the test bed, the paper introduces a procedure to determine a subset of the parameter values, called the 1% contiguous test bed, for which each method performed well. The results show that, depending on the range of values that apply in a given practical situation, either i any of a large number of methods will yield good performance or ii a carefully selected method can achieve superior performance.
TL;DR: The simple incremental rule (IPPA) was compared to three heuristic procedures frequently used in material requirements planning (MRP) lot-sizing research and consistently generated lower total order/setup and carrying costs than the three heuristics.
Abstract: A simple incremental cost approach to lot sizing was tested in a multilevel inventory environment. The incremental approach has not previously been tested in a large-scale study involving multiple product-structure levels. Using the Wagner-Whitin (WW) algorithm as a benchmark, the simple incremental rule (IPPA) was compared to three heuristic procedures (LFL, EOQ, and POQ) frequently used in material requirements planning (MRP) lot-sizing research. The incremental rule consistently generated lower total order/setup and carrying costs than the three heuristics across the 3,200 multilevel test situations examined. In many of the test situations, the incremental rule also outperformed the WW benchmark.
TL;DR: A persistent bias in judgments of means and standard deviations within an intuitive inference task is interpreted in terms of the anchoring and adjustment heuristic.
TL;DR: A new algorithm, FLAC (Facility Layout by Analysis of Clusters), is described which emulates the visual methods used by industrial engineers in solving facility layout problems and is found to perform well in problems with high as well as low flow dominance.
Abstract: Due to the combinatorial nature of the facility layout problem, current heuristic computer procedures do not always provide better solutions than visual methods. A new algorithm, FLAC (Facility Layout by Analysis of Clusters), is described which emulates the visual methods used by industrial engineers in solving facility layout problems. Initially side-stepping the combinatorial nature of the problem, FLAC is found to perform well in problems with high as well as low flow dominance, and in the presence and absence of line dominance. Computation time is attractive, especially on larger problems.
01 Jul 1985
TL;DR: In both step response and tracking experiments the self-organising controller was found to perform better than a conventional PID controller under similar circumstances, although some tuning was required for the former system.
Abstract: The paper reports results of the practical application of the self-organising controller to the simultaneous control of two interacting joints of a robot. The rationale for its use and the architecture of this heuristic fuzzy rule-based dynamic control approach are outlined. This shows it to be a relatively simple intelligent knowledge-based system capable of both learning and displaying a variable nonlinear input/output relationship. In both step response and tracking experiments the self-organising controller was found to perform better than a conventional PID controller under similar circumstances, although some tuning was required for the former system.
01 Jun 1985
TL;DR: The performance of simulated annealing is compared to that of other Monte Carlo methods for optimization and it is shown that these other methods often perform better than simulatedAnnealing.
Abstract: The performance of simulated annealing is compared to that of other Monte Carlo methods for optimization. Our experiments show that these other methods often perform better than simulated annealing.
TL;DR: A new two-phase approximate method for real-time scheduling in a flexible manufacturing system (FMS) that combines a reduced enumeration schedule generation algorithm with a 0–1 optimization algorithm and heuristic rules are introduced for the selection of jobs to be scheduled.
Abstract: This paper presents a new two-phase (TP) approximate method for real-time scheduling in a flexible manufacturing system (FMS). This method combines a reduced enumeration schedule generation algorithm with a 0–1 optimization algorithm. In order to make the combined algorithm practicable, heuristic rules are introduced for the selection of jobs to be scheduled. The relative performance of the TP method vis-a-vis conventional heuristic dispatching rules such as SPT, LPT, FCFS, MWKR, and LWKR is investigated using combined process-interaction/discrete-event simulation models. An efficient experimental procedure is designed and implemented using these models, and the statistical analysis of the results is presented. For the particular case investigated, the conclusions are very encouraging. In terms of mean flow time, the TP method performs significantly better than any other tested heuristic dispatching rules. Also, the experimental results show that using global information significantly improves the FMS performance.
TL;DR: In this paper, a methodology to obtain optimal reservoir operation policies for a large-scale reservoir system is presented, where river flows are characterized as a multivariate autoregressive process and are forecasted using maximum likelihood estimators.
Abstract: This paper presents a methodology to obtain optimal reservoir operation policies for a large- scale reservoir system. The model yields medium-term (one-year-ahead) optimal release policies that allow the planning of activities within the current water year, with the possibility of updating preplanned activities to account for uncertain events that affect the state of the system. River flows are characterized as a multivariate autoregressive process and are forecasted using maximum likelihood estimators. The solution method is a sequential dynamic decomposition algorithm that keeps computational requirements and dimensionality problems at low levels. The model maximizes the system annual energy generation while satisfying constraints imposed on the operation of the reservoir network. Several alternative versions of the model are also presented, which can be used under different assumptions. The model is applied to a large-scale multireservoir system, the northern portion of the California Central Valley Project. The optimal release policies show a potential increase in the system total annual energy with respect to heuristic schedules currently in use.
TL;DR: It is concluded that a heuristic procedure is a suitable way to handle project selection problems in organizational settings by means of an existing heuristic algorithm by Toyoda, and a hierarchical multiperiod multidimensional model is proposed.
Abstract: Decomposable systems, decomposition and 0-1 integer programming techniques are reviewed relative to project selection problems. It is concluded that a heuristic procedure is a suitable way to handle such problems in organizational settings. A hierarchical multiperiod multidimensional model is proposed. Good results are obtained based on an existing heuristic algorithm by Toyoda, by means of a decomposable interactive formulation. Examples and discussions are included that show how the use of this model can improve management decisions. A major advantage of this formulation is its flexibility to handle many different R&D situations, a feature which should increase its usefulness over many other R&D Project Selection Models. The model can be used in a multihierarchy ambience, can use parametric budgeting, and can be used as a control tool. The interactive feature provides the means for improving organizational communications and integrating divergent viewpoints.
01 Dec 1985
TL;DR: It is proved that the average time required for the move-to-front heuristic is no more than π/2 times that of the optimal order and this bound is best possible.
Abstract: In this paper we describe a general technique which can be used to solve an old problem in analyzing self-organizing sequential search. We prove that the average time required for the move-to-front heuristic is no more than p/2 times that of the optimal order and this bound is best possible. Hilbert's inequalities will be used to derive large classes of inequalities some of which can be applied to obtain tight worst-case bounds for several self-organizing heuristics.
Journal Article•
TL;DR: An efficient algorithm is described which necessarily finds a routing in a given grid whenever it exists, and works for a rather large class of grids, called convex grids, including the grids of rectangular, T-, L-, or X-shape boundaries.
Abstract: In this paper, we consider the channel routing problem involving two-terminal nets on rectilinear grids. An efficient algorithm is described which necessarily finds a routing in a given grid whenever it exists. The algorithm is not a heuristic but an exact one, and works for a rather large class of grids, called convex grids, including the grids of rectangular, T-, L-, or X-shape boundaries. Both the running time and required space are linear in the number of vertices of a grid.
TL;DR: This paper addresses the problem commonly faced by RD that is, the heuristic always found the optimal investment plan, and the unified approach offered by the methodology represented a major improvement over current analytic techniques.
Abstract: This paper addresses the problem commonly faced by RD that is, the heuristic always found the optimal investment plan. In general, computational times were minimal when compared to data collection efforts, with the latter perhaps being the principal hurdle to full implementation. The sponsoring organization, however...
TL;DR: In this article, the authors compared the performance of a set of rectangular layout heuristics on the basis of their packing densities and time performance, with a view to increase their applicability in manufacturing situations.
Abstract: We compare the performance of a set of rectangular layout heuristics on the basis of their packing densities and time performance, with a view to increase their applicability in manufacturing situations. Among the techniques is a class of heuristics introduced by the authors in an earlier work (Israni And Sanders, 1982). The experimental comparison is made over two attributes defined for the bill of materials; the area and the aspect ratio distributions of the pieces. In addition, some of the heuristics considered permit limited human intervention. Our study shows that the two attributes play a significant part in determining the performance of a heuristic. Length-sorted heuristics are found to perform differently as a class from height-sorted heuristics. The study shows that human intervention, even in limited amounts, usually improves the quality of a solution substantially. The heuristics' worst case time complexities are presented. For certain specific regions of the attributes, the best heuristic has...