Showing papers on "Heuristic published in 1989"
01 Jan 1989
1,697 citations
TL;DR: An efficient probabilistic set covering heuristic is presented that provides the best known solutions to all other instances attempted to solve set covering problems that arise from Steiner triple systems.
1,038 citations
TL;DR: The results show that distributed scheduling is effective even in a hard real-time environment and that the relative performance of these algorithms is a function of the system state.
Abstract: A set of four heuristic algorithms is presented to schedule tasks that have headlines and resource requirements in a distributed system. When a task arrives at a node, the local scheduler at that node attempts to guarantee that the task will complete execution on that node before its deadline. If the attempt fails, the scheduling components on individual nodes cooperate to determine which other node in the system has sufficient resource surplus to guarantee the task. Simulation studies are performed to compare the performance of these algorithms with respect to each other as well to two baselines. The first baseline is the noncooperative algorithm where a task that cannot be guaranteed locally is not sent to any other node. The second is an (ideal) algorithm that behaves exactly like the bidding algorithm but incurs no communication overheads. The simulation studies examine how communication delay, task laxity, load differences on the nodes, and task computation times affect the performance of the algorithms. The results show that distributed scheduling is effective even in a hard real-time environment and that the relative performance of these algorithms is a function of the system state. >
363 citations
TL;DR: Results indicate that the history heuristic combined with transposition tables significantly outperforms other alpha-beta enhancements in application-generated game trees.
Abstract: Many enhancements to the alpha-beta algorithm have been proposed to help reduce the size of minimax trees. A recent enhancement, the history heuristic, which improves the order in which branches are considered at interior nodes is described. A comprehensive set of experiments is reported which tries all combinations of enhancements to determine which one yields the best performance. In contrast, previous work on assessing their performance has concentrated on the benefits of individual enhancements or a few combinations. The aim is to find the combination that provides the greatest reduction in tree size. Results indicate that the history heuristic combined with transposition tables significantly outperforms other alpha-beta enhancements in application-generated game trees. For trees up to depth 8, this combination accounts for 99% of the possible reductions in tree size, with the other enhancements yielding insignificant gains. >
249 citations
01 May 1989
TL;DR: Some heuristic channel-assignment algorithms for cellular systems are described, developed, in part, by suitably adapting some of the ideas previously introduced in heuristic graph-coloring algorithms.
Abstract: Some heuristic channel-assignment algorithms for cellular systems are described. These algorithms have yielded optimal, or near-optimal assignments, in many cases. The channel-assignment problem can be viewed as a generalized graph-coloring problem, and these algorithms have been developed, in part, by suitably adapting some of the ideas previously introduced in heuristic graph-coloring algorithms. The channel-assignment problem is formulated as a minimum-span problem, i.e. a problem wherein the requirement is to find the minimum bandwidth necessary to satisfy a given demand. Examples are presented, and algorithm performance results are discussed. >
242 citations
TL;DR: This survey considers emerging approaches of heuristic search for solutions to combinatorially complex problems that arise in business applications, such as in manufacturing operations, financial investment, capital budgeting and resource management.
234 citations
01 Jun 1989
TL;DR: This paper investigates the use of heuristics in optimizing queries with a large number of joins and finds that two combinations of the augmentation heuristic and iterative improvement perform the best under most conditions.
Abstract: We investigate the use of heuristics in optimizing queries with a large number of joins. Examples of such heuristics are the augmentation and local improvement heuristics described in this paper and a heuristic proposed by Krishnamurthy et al. We also study the combination of these heuristics with two general combinatorial optimization techniques, iterative improvement and simulated annealing, that were studied in a previous paper. Several interesting combinations are experimentally compared. For completeness, we also include simple iterative improvement and simulated annealing in our experimental comparisons. We find that two combinations of the augmentation heuristic and iterative improvement perform the best under most conditions. The results are validated using two different cost models and several different synthetic benchmarks.
218 citations
21 Jun 1989
TL;DR: An improvement to a heuristic introduced by Chaitin for use in graph coloring register allocation produces better colorings, with less spill code, and has similar compile-time and implementation requirements.
Abstract: We describe an improvement to a heuristic introduced by Chaitin for use in graph coloring register allocation. Our modified heuristic produces better colorings, with less spill code. It has similar compile-time and implementation requirements. We present experimental data to compare the two methods.
216 citations
01 Jan 1989
TL;DR: The authors present Pool2, a generic system for cognitive map development and decision analysis that is based on negative-positive-neutral (NPN) logics and NPN relations, and a theorem is presented that provides conditions for the existence and uniqueness of heuristic transitive closures of an NPN relation.
Abstract: The authors present Pool2, a generic system for cognitive map development and decision analysis that is based on negative-positive-neutral (NPN) logics and NPN relations. NPN logics and relations are extensions of two-valued crisp logic, crisp (binary) relations, and fuzzy relations, NPN logics and relations assume logic values in the NPN interval (-1, 1) instead of values in (0, 1). A theorem is presented that provides conditions for the existence and uniqueness of heuristic transitive closures of an NPN relation. It is shown that NPN logic and NPN relations can be used directly to model a target world with a combination of NPN relationships of attributes and/or concepts for the purposes of cognitive map understanding, and decision analysis. Two algorithms are presented for heuristic transitive closure computation and for heuristic path searching, respectively. Basic ideas are illustrated by example. A comparison is made between this approach and others. >
175 citations
TL;DR: The performance of scheduling algorithms for a certain kind of manufacturing environment, called the “Flexible Flowshop”, which consists of a certain number of machine centers, is discussed and heuristic algorithms are presented and studied in the worst and average case performance contexts.
168 citations
TL;DR: The results indicate that MACLEARN'S filtering heuristics all improve search performance, sometimes dramatically, and when the system was given practice on simpler training problems, it learned a set of macros that led to successful solutions of several much harder problems.
Abstract: This paper describes a heuristic approach to the discovery of useful macro-operators (macros) in problem solving. The approach has been implemented in a program, MACLEARN, that has three parts: macro-proposer, static filter, and dynamic filter. Learning occurs during problem solving, so that performance improves in the course of a single problem trial. Primitive operators and macros are both represented within a uniform representational framework that is closed under composition. This means that new macros can be defined in terms of others, which leads to a definitional hierarchy. The representation also supports the transfer of macros to related problems. MACLEARN is embedded in a supporting system that carries out best-first search. Experiments in macro learning were conducted for two classes of problems: peg solitaire (generalized “Hi-Q puzzle”), and tile sliding (generalized “Fifteen puzzle”). The results indicate that MACLEARN'S filtering heuristics all improve search performance, sometimes dramatically. When the system was given practice on simpler training problems, it learned a set of macros that led to successful solutions of several much harder problems.
TL;DR: A fast and effective branch-and-bound algorithm, which incorporates this heuristic for use in bounding, is developed, which introduces heuristic fathoming as a technique for reducing the size of the branch- and-bound tree.
Abstract: A simple, fast and effective heuristic for the Simple Assembly Line Balancing Type I problem (minimizing the number of workstations) is proposed. A fast and effective branch-and-bound algorithm, which incorporates this heuristic for use in bounding, is developed. The algorithm introduces heuristic fathoming as a technique for reducing the size of the branch-and-bound tree. Methods to solve the Simple Assembly Line Balancing Type II problem (maximizing the production rate) are also described. Upper bounds on all heuristics for both problems are provided.
TL;DR: A heuristic model is presented for determining the ordering schedule when an inventoried item is subject to deterioration and demand changes linearly over time, which produces a better solution than optimizing models.
Abstract: In this paper a heuristic model is presented for determining the ordering schedule when an inventoried item is subject to deterioration and demand changes linearly over time. While the optimizing model developed by researchers fixes the ordering interval and varies the ordering size, the heuristic permits variation in both replenishment-cycle length and the size of the order. As a result, the heuristic produces a better solution than optimizing models in the study presented here.
Proceedings Article•
20 Aug 1989TL;DR: A new decision tree learning algorithm called IDX is described, more general than existing algorithms, that addresses issues of decision tree quality largely overlooked in the artificial intelligence and machine learning literature.
Abstract: A new decision tree learning algorithm called IDX is described. More general than existing algorithms, IDX addresses issues of decision tree quality largely overlooked in the artificial intelligence and machine learning literature. Decision tree size, error rate, and expected classification cost are just a few of the quality measures it can exploit. Furthermore, decision trees of varying quality can be induced simply by adjusting the complexity of the algorithm. Quality should be addressed during decision tree construction since retrospective pruning of the tree, or of a derived rule set, may be unable to compensate for inferior splitting decisions. The complexity of the algorithm, the basis for the heuristic it embodies, and the results of three different sets of experiments are described.
21 Jun 1989
TL;DR: This paper presents a new coherent set of heuristic methods for reducing the amount of spill code generated, which results in more efficient (and shorter) compiled code.
Abstract: Global register allocation and spilling is commonly performed by solving a graph coloring problem. In this paper we present a new coherent set of heuristic methods for reducing the amount of spill code generated. This results in more efficient (and shorter) compiled code. Our approach has been compared to both standard and priority-based coloring algorithms, universally outperforming them.In our approach, we extend the capability of the existing algorithms in several ways. First, we use multiple heuristic functions to increase the likelihood that less spill code will be inserted. We have found three complementary heuristic functions which together appear to span a large proportion of good spill decisions. Second, we use a specially tuned greedy heuristic for determining the order of deleting (and hence coloring) the unconstrained vertices. Third, we have developed a “cleaning” technique which avoids some of the insertion of spill code in non-busy regions.
01 Jun 1989
TL;DR: A time constrained query evaluation algorithm is described, which is implemented in the prototype DBMS, and iteratively samples from input relations, and evaluates the associated estimators developed in the previous work, until a stopping criterion is satisfied.
Abstract: We consider those database environments in which queries have strict timing constraints, and develop a time-constrained query evaluation methodology. For aggregate relational algebra queries, we describe a time constrained query evaluation algorithm. The algorithm, which is implemented in our prototype DBMS, iteratively samples from input relations, and evaluates the associated estimators developed in our previous work, until a stopping criterion (e.g., a time quota or a desired error range) is satisfied.To determine sample sizes at each stage of the iteration (so that the time quota will not be overspent) we need to have (a) accurate sample selectivity estimations of the RA operators in the query, (b) precise time cost formulas, and (c) good time-control strategies. To estimate the sample selectivities of RA operators, we use a runtime sample selectivity estimation and improvement approach which is flexible. For query time estimations, we use time-cost formulas which are adaptive and precise. To use the time quota efficiently, we propose statistical and heuristic time-control strategies to control the risk of overspending the time quota. Preliminary evaluation of the implemented prototype is also presented.
TL;DR: In this article, a mixed-integer programming (MILP) model is proposed to minimize the sum of train costs, car time costs, and classification yard costs, while not exceeding limits on train size and yard volumes.
Abstract: Railroad managers must determine (1) which pairs of terminals are to be provided with direct train connections, (2) the frequency of service, (3) how the individual cars are routed through the available configuration of trains and intermediate terminals, and (4) how cars are physically grouped or “blocked” within trains. The objective is to minimize the sum of train costs, car time costs, and classification yard costs, while not exceeding limits on train size and yard volumes. These decisions are modeled as a mixed-integer programming problem, where the decision to operate a given train connection corresponds to 0–1 variable. With no limits on train size, the model can be solved very efficiently using Lagrangian relaxation. If the solution contains some overloaded trains, which is likely, heuristic adjustments are necessary to obtain a feasible operating plan.
TL;DR: The design and implementation of an interactive optimization system for routing freight over a less-than-truckload motor carrier network is described, using a local improvement heuristic in such a way as to keep the “man-in-the-loop.”
Abstract: We describe the design and implementation of an interactive optimization system for routing freight over a less-than-truckload motor carrier network. We formulate a very large, mixed integer programming problem, and develop a decomposition strategy based partly on the mathematical structure of the problem as well as a range of important, real-world issues and constraints. Then we develop and implement a local improvement heuristic in such a way as to keep the “man-in-the-loop,” using the analyst to make judgments regarding certain complex constraints and tradeoffs. Important aspects of the system include a range of modeling approximations to keep the problem tractable and the way the analyst evaluates the quality of the different numbers. The package was implemented and is currently being used on an ongoing basis by a major motor carrier. An overview of the major elements of the package is given as well as a summary of important implementation issues that arose during the three year project.
01 Jan 1989
TL;DR: This research investigates the use of behavior-structure models for adapting the designs of physical devices by unifying case-based methods, associative methods, heuristic search methods, decomposition methods, and model- based methods into one architecture for adaptive design problem solving.
Abstract: In the case-based approach to design, a novel problem is solved by adapting a design known to solve a related problem Adapting a known design to solve a related problem by the commonly used methods of heuristic association and search, however, can be computationally expensive if the adaptation search space is not small The adaptation space, then, needs to be decomposed into smaller and simpler spaces that can be searched more efficiently and effectively The knowledge for decomposing the adaptation search space can be represented as a behavior-structure model that specifies how the structure of the known design results in its output behaviors This research investigates the use of such behavior-structure models for adapting the designs of physical devices
Comprehension of how the output behaviors of a design arise from its structure is represented as a behavioral component-substance model for the design The model explicitly specifies (i) the expected output behaviors of the design including its functions, (ii) the elementary structural and behavioral interactions between components and substances constituting the structure of the design, and (iii) the internal causal behaviors of the design that compose the elementary interactions into its output behaviors The causal behaviors of the design, in this model, are indexed by the expected output behaviors for which they are responsible
The model aids case-based design in several ways First, it identifies conceptual primitives for specifying the functions of designs, which are used to index the known designs stored in a case-based memory Second, it identifies elementary types of behavior transformations and elementary types of structure modifications Third, it provides knowledge for decomposition of the adaptation search space into smaller spaces so that the search for the needed structure modifications is localized Fourth, it leads to a novel method for simulating the behavioral effects of structure modifications The output and causal behaviors of the modified design, in this method, are derived by revising the output and causal behaviors of the known design
This integrative approach unifies case-based methods, associative methods, heuristic search methods, decomposition methods, and model-based methods into one architecture for adaptive design problem solving Core portions of this approach have been implemented in an experimental design system called KRITIK
TL;DR: The problem of maximizing the share of a new product introduced in a competitive market is shown to be NP-hard, and a directed graph representation of the problem is used to construct shortest-path and dynamic-programming heuristics.
TL;DR: An extensive search of journal publications on heuristic methods and applications produced 442 articles published in 37 journals during the last sixteen years, with some interesting historical patterns and directions for future work revealed.
TL;DR: A new heuristic rule which compares favourably with the widely-used heuristic rules is developed, and the influence of network/resource characteristics on the performance of different heuristic Rules is investigated.
Abstract: The problem considered in this study is that of non-pre-emptive scheduling of the activities in a project network to minimize project duration under limited resource availabilities Various heuristic rules and optimization techniques have been applied to this problem, and comparisons of their effectiveness have been made in the literature However, no thorough investigation of the types of network and resource characteristics which play an underlying role in determining heuristic performance and which account for the variability of results has been made previously In this study, a new heuristic rule which compares favourably with the widely-used heuristic rules is developed, and the influence of network/resource characteristics on the performance of different heuristic rules is investigated
TL;DR: The problem of finding plausible lines of separation between touching groups of dark objects is investigated and it is shown that the problem can be largely solved by a robust, adaptive procedure based on an analysis of concavities in relation to expected chromosome shape, and on a search for minimum density paths.
TL;DR: The solution approach includes several heuristic procedures to handle the subproblems which are formulated as assignment type problems in which entities are assigned to resources by using penalty terms for conflicts and excessive use of classrooms.
TL;DR: A heuristic decision rule is derived for the replenishment of items with a linearly increasing demand rate over a finite planning horizon during which shortages are allowed.
Abstract: A heuristic decision rule is derived for the replenishment of items with a linearly increasing demand rate over a finite planning horizon during which shortages are allowed. When compared with the exact decision rule, the heuristic is found to incur negligible cost penalty for the numerical example which is given to illustrate the use of the heuristic.
IBM1
TL;DR: The FAP solution has been implemented in a PL/I program known as the Placement Optimization Program (POP), which consists of three major components — two heuristic optimization models and a queueing network model.
Abstract: In this paper we describe a practical mathematical formulation and solution of the so-called “File Assignment Problem” (FAP) for computer disks. Our FAP solution has been implemented in a PL/I program known as the Placement Optimization Program (POP). The algorithm consists of three major components — two heuristic optimization models and a queueing network model. POP has been used in validation studies to assign files to disks in two IBM MVS complexes. The resulting savings in I/O response times were 22% and 25%, respectively. Throughout the paper we shall emphasize the real-world nature of our approach to the disk FAP, which we believe sets it apart from previous attempts.
16 Mar 1989
TL;DR: A new parallel heuristic, SAGA, for the quadratic assignment problem is described, a cascaded hybrid of a genetic algorithm and simulated annealing that is superior to the most commonly employed heuristic in solution quality and in solution time.
Abstract: The quadratic assignment problem represents an important class of problems with applications as diverse as facility layout and data analysis. The importance of these applications coupled with the fact that the quadratic assignment problem is NP-hard has encouraged the development of heuristics because optimal seeking procedures have been restricted to very small versions of the problem. This paper describes a new parallel heuristic, SAGA, for the quadratic assignment problem. SAGA is a cascaded hybrid of a genetic algorithm and simulated annealing. In addition to details regarding SAGA and its implementation, this paper also describes the performance of SAGA on two standard problems taken from the literature. The results from these problems show SAGA to be superior to the most commonly employed heuristic in solution quality, and for large problems it is also superior in solution time.
TL;DR: The proposed heuristic is based on duality theory and is shown to have a close connection to the well known Greedy Heuristic, formulated to allow applications in much broader spare part logistics contexts.
Abstract: Consider a facility that stocks various parts in support of repairs for a set of products in some customer region. The problem considered is to determine base stock policies for each part to minimize expected inventory costs across all parts while satisfying some service constraint on total customer repair services completed. A heuristic procedure is developed for several variants of this problem. The heuristic is based on duality theory and is shown to have a close connection to the well known Greedy Heuristic. Encouraging experimental results on the performance of the proposed heuristic are reported. The basic problem structure considered is similar to the tool-kit problem, but it is formulated to allow applications in much broader spare part logistics contexts.
TL;DR: The convergence of the algorithm and its various modifications are the main topic of this paper, based on the simple heuristic idea: to delete, at every step of the iterative procedure, ‘bad’ sets of the supporting points and to include ‘good’ ones in the design.
TL;DR: In this article, a heuristic method for scheduling a television network's programs in order to maximize the network's share of audience, or any other criterion related to viewership is proposed.
Abstract: A heuristic method for scheduling a television network's programs in order to maximize the network's share of audience, or any other criterion related to viewership is proposed. Using an extension of the audience flow model of individual viewing choice, combined with a heuristic search procedure, it is indicated that network audience shares may be unexpectedly sensitive to scheduling. The proposed method may be used either to evaluate candidate program schedules or to select the schedule which maximizes the heuristic's objective function. Managerial constraints, such as requiring childrens' programs to be shown before 8 p.m. may be easily incorporated.