Showing papers on "Heuristic published in 1997"

Heuristics based on unit propagation for satisfiability problems

Abstract: The paper studies new unit propagation based heuristics for Davis-Putnam-Loveland (DPL) procedure. These are the novel combinations of unit propagation and the usual "Maximum Occurrences in clauses of Minimum Size" heuristics. Based on the experimental evaluations of different alternatives a new simple unit propagation based heuristic is put forward. This compares favorably with the heuristics employed in the current state-of-the-art DPL implementations (C-SAT, Tableau, POSIT).

Capacitor placement in distribution systems using heuristic search strategies

Abstract: Heuristic search strategies are used to determine the optimum capacitor placement and ratings for distribution systems. In the heuristic approach proposed a small number of critical nodes, named sensitive nodes, are selected for installing capacitors that optimise the net savings while achieving a large overall loss reduction. This method insures that voltage constraints are met. This heuristic approach is suitable for large distribution systems and can be useful in online implementation. The proposed approach is applied to a test system and the results are compared with other published techniques.

New Benchmark Results for the Resource-Constrained Project Scheduling Problem

Abstract: This paper reports on new insights derived from computational results obtained with an updated version of the branch-and-bound procedure previously developed by Demeulemeester and Herroelen Demeulemeester, E., W. Herroelen. 1992. A branch-and-bound procedure for the multiple resource-constrained project scheduling problem. Management Sci.38 1803-1818. for solving the resource-constrained project scheduling problem RCPSP. The new code fully exploits the advantages of 32-bit programming provided by recent compilers running on platforms such as Windows NT® and OS/2®: flat memory, increased addressable memory, and fast program execution. We study the impact of three important variables on the computation time for the RCPSP: addressable computer memory, the search strategy depth-first, best-first, or hybrid, and the introduction of a stronger lower bound. We compare the results obtained by a truncated branch-and-bound procedure with the results generated by the minimum slack time heuristic and report on the dependency of its solution quality on the allotted CPU time.

Heuristic Walkthroughs: Finding the Problems Without the Noise

Abstract: Inspection-based evaluation techniques are popular because they require less formal training, are quick, can be used throughout the development process, do not require test users, and can result in finding numerous usability problems. A new technique is described that combines the benefits of heuristic evaluations, cognitive walkthroughs, and usability walkthroughs. This technique, a heuristic walkthrough, provide more structure than heuristic evaluations but less than cognitive walkthroughs. The result is an effective task-oriented evaluation technique that is easy to learn and apply. Methods are proposed for comparing the validity, thoroughness, and reliability of evaluation techniques. Finally, heuristic walkthroughs are compared to heuristic evaluations and cognitive walkthroughs in a controlled study. The results indicate that heuristic walkthroughs are more thorough than cognitive walkthroughs and more valid than heuristic evaluations. In other words, heuristic walkthroughs resulted in finding more ...

Improved Fashion Buying with Bayesian Updates

Abstract: We focus on the problem of buying fashion goods for the “big book” of a catalogue merchandiser. This company also owns outlet stores and thus has the opportunity, as the season evolves, to divert inventory originally purchased for the big book to the outlet store. The obvious questions are: (1) how much to order originally, and (2) how much to divert to the outlet store as actual demand is observed. We develop a model of demand for an individual item. The model is motivated by data from the women's designer fashion department and uses both historical data and buyer judgement. We build a stochastic dynamic programming (DP) model of the fashion buying problem that incorporates the model of demand. The DP model is used to derive the structure of the optimal inventory control policy. We then develop an updated Newsboy heuristic that is intuitively appealing and easily implemented. When this heuristic is compared to the optimal solution for a wide variety of scenarios, we observe that it performs very well. Si...

Simple assembly line balancing—Heuristic approaches

Abstract: In this paper heuristics for Type 1 and Type 2 of the Simple Assembly Line Balancing Problem (SALBP) are described. Type 1 of SALBP (SALBP-1) consists of assigning tasks to work stations such that the number of stations is minimized for a given production rate whereas Type 2 (SALBP-2) is to maximize the production rate, or equivalently, to minimize the sum of idle times for a given number of stations. In both problem types, precedence constraints between the tasks have to be considered.

Vehicle Routing with Time Windows using Genetic Algorithms

Abstract: In vehicle routing problems with time windows (VRPTW), a set of vehicles with limits on capacity and travel time are available to service a set of customers with demands and earliest and latest time for servicing. The objective is to minimize the cost of servicing the set of customers without being tardy or exceeding the capacity or travel time of the vehicles. As finding a feasible solution to the problem is NP-complete, search methods based upon heuristics are most promising for problems of practical size. In this paper we describe GIDEON, a genetic algorithm heuristic for solving the VRPTW. GIDEON consists of a global customer clustering method and a local post-optimization method. The global customer clustering method uses an adaptive search strategy based upon population genetics, to assign vehicles to customers. The best solution obtained from the clustering method is improved by a local post-optimization method. The synergy a between global adaptive clustering method and a local route optimization method produce better results than those obtained by competing heuristic search methods. On a standard set of 56 VRPTW problems obtained from the literature the GIDEON system obtained 41 new best known solutions.

Multilevel circuit partitioning

Abstract: Recent work has illustrated the promise ofmultilevel approaches for partitioning large circuits. Multilevel partitioningrecursively clusters the instance until its size is smallerthan a given threshold, then unclusters the instance while applyinga partitioning refinement algorithm. Our multilevel partitioner usesa new technique to control the number of levels in the matching-basedclustering phase and also exploits recent innovations in classiciterative partitioning. Our heuristic outperforms numerousexisting bipartitioning heuristics, with improvements rangingfrom 6.9 to 27.9% for 100 runs and 3.0 to 20.6% for just 10 runs(while also using less CPU time).

Decentralized motion planning for multiple mobile robots: the cocktail party model

Abstract: This paper presents an approach for decentralized real-time motion planning for multiple mobile robots operating in a common 2-dimensional environment with unknown stationary obstacles. In our model, a robot can see (sense) the surrounding objects. It knows its current and its target‘s position, is able to distinguish a robot from an obstacle, and can assess the instantaneous motion of another robot. Other than this, a robot has no knowledge about the scene or of the paths and objectives of other robots. There is no mutual communication among the robots; no constraints are imposed on the paths or shapes of robots and obstacles. Each robot plans its path toward its target dynamically, based on its current position and the sensory feedback; only the translation component is considered for the planning purposes. With this model, it is clear that no provable motion planning strategy can be designed (a simple example with a dead-lock is discussed); this naturally points to heuristic algorithms. The suggested strategy is based on maze-searching techniques. Computer simulation results are provided that demonstrate good performance and a remarkable robustness of the algorithm (meaning by this a virtual impossibility to create a dead-lock in a “random” scene).

Evaluating persuasion-enhancing techniques from a resource-matching perspective

Abstract: This article examines how two ad execution characteristics intended to heighten persuasion can influence the resources required to process an ad under high and low motivation conditions. These ad execution characteristics include (1) whether the ad copy is narrative or factual and (2) whether the ad layout either physically integrates or separates the ad picture and ad claims. Results reveal that under low motivation, persuasion is unaffected by these two execution characteristics but instead is affected by heuristic aspects of the ad photo. Under high motivation, whether persuasion is heightened or undermined appears to depend on the extent to which the ad execution characteristics render the resources needed to process the ad equal to, in excess of, or inadequate compared with those that motivated viewers have available for processing the ad.

Application of Threshold-Accepting to the Evaluation of the Discrepancy of a Set of Points

Abstract: Efficient routines for multidimensional numerical integration are provided by quasi--Monte Carlo methods. These methods are based on evaluating the integrand at a set of representative points of the integration area. A set may be called representative if it shows a low discrepancy. However, in dimensions higher than two and for a large number of points the evaluation of discrepancy becomes infeasible. The use of the efficient multiple-purpose heuristic threshold-accepting offers the possibility to obtain at least good approximations to the discrepancy of a given set of points. This paper presents an implementation of the threshold-accepting heuristic, an assessment of its performance for some small examples, and results for larger sets of points with unknown discrepancy.

Static and dynamic channel assignment using neural networks

Abstract: We examine the problem of assigning calls in a cellular mobile network to channels in the frequency domain. Such assignments must be made so that interference between calls is minimized, while demands for channels are satisfied. A new nonlinear integer programming representation of the static channel assignment (SCA) problem is formulated. We then propose two different neural networks for solving this problem. The first is an improved Hopfield (1982) neural network which resolves the issues of infeasibility and poor solution quality which have plagued the reputation of the Hopfield network. The second approach is a new self-organizing neural network which is able to solve the SCA problem and many other practical optimization problems due to its generalizing ability. A variety of test problems are used to compare the performance of the neural techniques against more traditional heuristic approaches. Finally, extensions to the dynamic channel assignment problem are considered.

Scheduling Semiconductor Burn-In Operations to Minimize Total Flowtime

Abstract: This paper addresses a problem of batch scheduling which arises in the burn-in stage of semiconductor manufacturing. Burn-in ovens are modeled as batch-processing machines which can handle up to B jobs simultaneously. The processing time of a batch is equal to the longest processing time among the jobs in the batch. The scheduling problem involves assigning jobs to batches and determining the batch sequence so as to minimize the total flowtime. In practice, there is a small number m of distinct job types. Previously, the only solution techniques known for the single-machine version of this problem were an O(m23Bm+1) pseudopolynomial algorithm, and a branch-and-bound procedure. We present an algorithm with a running time of O(m23m), which is independent of B, the maximum batch size. We also present a polynomial heuristic for the general problem (when m is not fixed), which is a two-approximation algorithm. For any problem instance, this heuristic provides a solution at least as good as that given by previo...

Bidirectional heuristic search reconsidered

Abstract: The assessment of bidirectional heuristic search has been incorrect since it was first published more than a quarter of a century ago. For quite a long time, this search strategy did not achieve the expected results, and there was a major misunderstanding about the reasons behind it. Although there is still wide-spread belief that bidirectional heuristic search is afflicted by the problem of search frontiers passing each other, we demonstrate that this conjecture is wrong. Based on this finding, we present both a new generic approach to bidirectional heuristic search and a new approach to dynamically improving heuristic values that is feasible in bidirectional search only. These approaches are put into perspective with both the traditional and more recently proposed approaches in order to facilitate a better overall understanding. Empirical results of experiments with our new approaches show that bidirectional heuristic search can be performed very efficiently and also with limited memory. These results suggest that bidirectional heuristic search appears to be better for solving certain difficult problems than corresponding unidirectional search. This provides some evidence for the usefulness of a search strategy that was long neglected. In summary, we show that bidirectional heuristic search is viable and consequently propose that it be reconsidered.

A heuristic variable grid solution method for POMDPs

Abstract: Partially observable Markov decision processes (POMDPs) are an appealing tool for modeling planning problems under uncertainty. They incorporate stochastic action and sensor descriptions and easily capture goal oriented and process onented tasks. Unfortunately, POMDPs are very difficult to solve. Exact methods cannot handle problems with much more than 10 states, so approximate methods must be used. In this paper, we describe a simple variable-grid solution method which yields good results on relatively large problems with modest computational effort.

An effective truncation heuristic for bias reduction in simulation output

Abstract: The start-up or warm-up problem arises in steady-state, discrete-event simulation, where the arbitrary selection of initial conditions introduces bias in simulated output sequences. In this paper, we develop and test a new truncation heuristic or resolving the start-up problem. Given a finite sequence, the truncation rule deletes initial observations until the width of the marginal confidence interval about the truncated sample mean is minimized. This rule is easy to implement, has strong intuitive appeal, and is remarkably effective in mitigating initialization bias. We illustrate the performance of the heuristic by comparison with enhanced implementations of alternative truncation rules proposed in the literature. All rules are applied to output sequences generated by ten runs each of four representative queuing simulations. Results confirm the significance of the start-up problem and demonstrate that simple truncation heuristics can solve this problem. All of the rules tested are shown to provide impro...

Improving the product mix heuristic in the theory of constraints

Abstract: The product mix heuristic is the component in the theory of constraints (TOC) which develops a master production schedule (MPS) to maximize system throughput. Prior research identified certain conditions where the TOC product mix heuristic does not identify the optimal solution. This paper revises the TOC product mix heuristic to identify the optimal product mix under conditions where the original TOC heuristic failed. The revised heuristic continues to be relatively easy for managers to understand and use when developing a MPS.

Task Assignment in a Distributed System: Improving Performance by Unbalancing Load

Abstract: We consider the problem of task assignment in a distributed system (such as a distributed Web server) in which task sizes are drawn from a heavy-tailed distribution. Many task assignment algorithms are based on the heuristic that balancing the load at the server hosts will result in optimal performance. We show this conventional wisdom is less true when the task size distribution is heavy-tailed (as is the case for Web file sizes). We introduce a new task assignment policy, called Size Interval Task Assignment with Variable Load (SITA-V). SITA-V purposely operates the server hosts at different loads, and directs smaller tasks to the lighter-loaded hosts. The result is that SITA-V provably decreases the mean task slowdown by significant factors (up to 1000 or more) where the more heavy-tailed the workload, the greater the improvement factor. We evaluate the tradeoff between improvement in slowdown and increase in waiting time in a system using SITA-V, and show conditions under which SITA-V represents a particularly appealing policy. We conclude with a discussion of the use of SITA-V in a distributed Web server, and show that it is attractive because it has a simple implementation which requires no communication from the server hosts back to the task router.

A heuristic technique for multi-agent planning

Abstract: The subject of multidagent planning has been of continuing concern in Distributed Artificial Intelligence (DAI). In this paper, we suggest an approach to multidagent planning that contains heuristic elements. Our method makes use of subgoals, and derived subdplans, to construct a global plan. Agents solve their individual subdplans, which are then merged into a global plan. The suggested approach reduces overall planning time and derives a plan that approximates the optimal global plan that would have been derived by a central planner, given those original subgoals. We explore three different scenarios. The first involves a group of agents with a common goal. The second considers how agents can interleave planning and execution when planning towards a common, though dynamic, goal. The third examines the case where agents, each with their own goal, can plan together to reach a state in consensus for the group. Finally, we consider how these approaches can be adapted to handle rational, manipulative agents.

A parametric rule for planning and management of multiple-reservoir systems

Abstract: A parametric rule for multireservoir system operation is formulated and tested. It is a generalization of the well-known space rule of simultaneously accounting for various system operating goals, in addition to the standard goal of avoiding unnecessary spills, including avoiding leakage losses, avoiding conveyance problems, taking into account the impacts of the reservoir system topology, and assuring satisfaction of secondary uses. Theoretical values of the rule's parameters for each one of these isolated goals are derived. In practice, parameters are evaluated to optimize one or more objective functions selected by the user. The rule is embedded in a simulation model so that optimization requires repeated simulations of the system operation with specific values of the parameters each time. The rule is tested on the case of the multireservoir water supply system of the city of Athens, Greece, which is driven by all of the operating goals listed above. Two problems at the system design level are tackled. First, the total release from the system is maximized for a selected level of failure probability. Second, the annual operating cost is minimized for given levels of water demand and failure probability. A detailed simulation model is used in the case study. Sensitivity analysis of the rule's parameters revealed a subset of insensitive parameters that allowed for rule simplification. Finally, the rule is validated through comparison with a number of heuristic rules also applied to the test case. Appendices are available on microfiche. Order from AmericanGeophysical Union, 2000 Florida Avenue, N.W., Washington, DC 20009. Document 97WR01034M; $2.50. Payment must accompany order.

Tractable induction and classification in first order logic via stochastic matching

Abstract: Learning in first-order logic (FOL) languages suffers from a specific difficulty: both induction and classification are potentially exponential in the size of hypotheses. This difficulty is usually dealt with by limiting the size of hypotheses, via either syntactic restrictions or search strategies. This paper is concerned with polynomial induction and use of FOL hypotheses with no size restrictions. This is done via stochastic matching: instead of exhaustively exploring the set of matchings between any example and any short candidate hypothesis, one stochastically explores the set of matchings between any example and any candidate hypothesis. The user sets the number of matching samples to consider and thereby controls the cost of induction and classification. One advantage of this heuristic is to allow for resource-bounded learning, without any a priori knowledge about the problem domain. Experiments on a real-world problem pertaining to organic chemistry fully demonstrate the potentialities of the approach regarding both predictive accuracy and computational cost.