Showing papers on "Heuristic published in 1993"

A technique for drawing directed graphs

Abstract: A four-pass algorithm for drawing directed graphs is presented. The fist pass finds an optimal rank assignment using a network simplex algorithm. The seconds pass sets the vertex order within ranks by an iterative heuristic, incorporating a novel weight function and local transpositions to reduce crossings. The third pass finds optimal coordinates for nodes by constructing and ranking an auxiliary graph. The fourth pass makes splines to draw edges. The algorithm creates good drawings and is fast. >

Learning by experimentation: acquiring and refining problem-solving heuristics

Abstract: This chapter concerns learning heuristic problem-solving strategies through experience. In particular, we focus on the issue of learning heuristics to guide a forward-search problem solver, and describe a computer program called lex, which acquires problem-solving heuristics in the domain of symbolic integration. lex acquires and modifies heuristics by iteratively applying the following process: (i) generate a practice problem; (ii) use available heuristics to solve this problem; (iii) analyze the search steps performed in obtaining the solution; and (iv) propose and refine new domain-specific heuristics to improve performance on subsequent problems. We describe the methods currently used by lex, analyze strengths and weaknesses of these methods, and discuss our current research toward more powerful approaches to learning heuristics.

Cyclic transfer algorithms for multivehicle routing and scheduling problems

Abstract: This paper investigates the application of a new class of neighborhood search algorithms—cyclic transfers—to multivehicle routing and scheduling problems. These algorithms exploit the two-faceted decision structure inherent to this problem class: First, assigning demands to vehicles and, second, routing each vehicle through its assigned demand stops. We describe the application of cyclic transfers to vehicle routing and scheduling problems. Then we determine the worst-case performance of these algorithms for several classes of vehicle routing and scheduling problems. Next, we develop computationally efficient methods for finding negative cost cyclic transfers. Finally, we present computational results for three diverse vehicle routing and scheduling problems, which collectively incorporate a variety of constraint and objective function structures. Our results show that cyclic transfer methods are either comparable to or better than the best published heuristic algorithms for several complex and important ...

Equality as a decision heuristic.

Abstract: Introduction In this chapter I propose to examine the concept of equality or equal division as a heuristic that is used to facilitate decision making in situations involving the allocation of goods and bads. The kinds of situations that I have in mind involve two or more people who must share resources, responsibilities, or liabilities. This is the same class of situations about which much has been written from the point of view of ethical theories of distributive justice (e.g., Nozick, 1947; Rawls, 1971) and from the psychological perspective of equity theory (Adams, 1965; Messick & Cook, 1983; Walster, Berscheid, & Walster, 1973). This chapter does not deal with ethical issues per se and it pertains to equity theory and related concepts only insofar as equity theory is viewed as the outcome or product of a certain type of social decision making. The focus of the chapter is the individual cognitive processes involved when a person must make a decision about how some resource or cost should be allocated. I propose that the idea of equality has properties that make it a useful guideline or benchmark in making allocation decisions. It will become obvious that the use of equality as a decision heuristic does not imply that such decisions are simpleminded or uninteresting. On the contrary, they can be quite intricate.

Public opinion and heuristic processing of source cues

Abstract: If the American citizen is capable of constructing reliable political judgments without engaging in extensive cognitive deliberation, then criticism that public opinion is largely vacuous in character may overstate the implications of a politically inattentive citizenry. Heuristic processing, reliance on simple rules of judgment, provides a cognitive mechanism that may enable citizens to advance informed yet efficient issue appraisals. More specifically, application of heuristic processing to source cues—references to prominent political leaders—can allow individuals to extend evaluations of those leaders to the policies and issues with which they are associated. In this paper, discussion of heuristic principles of judgment facilitates specification of the expected relationship between source cues and two component processes of individual-level public opinion: opinion holding and opinion direction. Separate quasi-experimental analyses yield evidence consistently supportive of the heuristic perspective.

A model and heuristic for solving very large item selection problems

Abstract: A model for solving very large item selection problems is presented. The model builds on previous work in binary programming applied to test con struction. Expert test construction practices are applied to situations in which all specifications for item selection cannot necessarily be met. A heuristic for selecting items that satisfy the constraints in the model also is presented. The heuristic is particu larly useful for situations in which the size of the test construction problem exceeds the limits of current implementations of linear programming algorithms. A variety of test construction problems involving real test specifications and item data from actual test assemblies were investigated using the model and the heuristic.

Slack-based heuristics for constraint satisfaction scheduling

Abstract: In this paper, we define and empirically evaluate new heuristics for solving the job shop scheduling problem with non-relaxable time windows. The hypothesis underlying our approach is that by approaching the problem as one of establishing sequencing constraints between pairs of operations requiring the same resource (as opposed to a problem of assigning start times to each operation) and by exploiting previously developed analysis techniques for limiting search through the space of possible sequencing decisions, simple, localized look-ahead techniques can yield problem solving performance comparable to currently dominating techniques that rely on more sophisticated analysis of resource contention. We define a series of attention focusing heuristics based on simple analysis of the temporal flexibility associated with different sequencing decisions, and a similarly motivated heuristic for determining how to sequence a given operation pair. Performance results are reported on a suite of benchmark problems previously investigated by two advanced approaches, and our simplified look-ahead analysis techniques are shown to provide comparable problem solving leverage at reduced computational cost.

List scheduling with and without communication delays

Abstract: Empirical results have shown that the classical critical path (CP) list scheduling heuristic for task graphs is a fast and practical heuristic when communication cost is zero. In the first part of this paper we study the theoretical properties of the CP heuristic that lead to its near optimum performance in practice. In the second part we extend the CP analysis to the problem of ordering the task execution when the processor assignment is given and communication cost is nonzero. We propose two new list scheduling heuristics, the RCP and RCP∗ that use critical path information and ready list priority scheduling. We show that the performance properties for RCP and RCP∗, when communication is nonzero, are similar to CP when communication is zero. Finally, we present an extensive experimental study and optimality analysis of the heuristics which verifies our theoretical results.

Estimating the relative usability of two interfaces: heuristic, formal, and empirical methods compared

Abstract: Two alternative user interface designs were subjected to user testing to measure user performance in a database query task. User performance was also estimated heuristically in three different ways and by use of formal GOMS modelling. The estimated values for absolute user performance had very high variability, but estimates of the relative advantage of the fastest interface were less variable. Choosing the fastest of the two designs would have a net present value more than 1,000 times the cost of getting the estimates. A software manager would make the correct choice every time in our case study if decisions were based on at least three independent estimates. User testing was 4.9 times as expensive as the cheapest heuristic method but provided better performance estimates.

A new heuristic for the multi-depot vehicle routing problem that improves upon best-known solutions

Abstract: SYNOPTIC ABSTRACTIn this paper, the multi-depot vehicle routing problem is reviewed and a new heuristic is presented. The heuristic is fast and simple and improves upon previous best-known solutions. In addition, we generate a variety of new test problems. The new heuristic is shown to perform well on these problems.

Near Myopic Heuristics for the Fixed-Life Perishability Problem

Abstract: This paper details the application of a class of heuristics to the Fixed-life Perishability Problem formulated by Nahmias 1975a and Fries 1975. Various assumptions for this model include i.i.d. demand, linear ordering, holding and penalty costs. Goods have a known fixed lifetime and perished goods cause a linear outdating cost to be incurred. The approach we use, that of developing heuristics from 'near myopic' bounds, involves viewing periodic inventory problems in the framework of the classic "newsboy" model. We exploit various properties of the problem under consideration to derive tight bounds on the newsboy parameters, thus leading to efficient bounds on the order quantities. Computational studies reveal that the heuristic policies are near optimal, and are easy to compute.

A heuristic optimization method for forest planning and decision making

Abstract: The presented planning method searches for an optimal combination of treatment schedules for forest compartments by maximizing, in an iterative manner, an additive utility function. The variables of the utility function can be selected from parameters that are associated to the whole forest area, such as; amount of removal, costs, income, or volume of the growing stock. Partial utility functions are developed for each objective variable the total utility being a weighted sum of the partial utilities. The weights of the objectives and the partial utility functions are estimated using the Analytic Hierarchy Process. Development and estimation of the utility function makes decision analysis an integral part of the planning process. The method was used with a non‐industrial private forest, but the approach is also applicable to many other forest planning situations.

A Heuristic for Replenishment of Deteriorating Items with a Linear Trend in Demand

Abstract: The classical no-shortage inventory policy is examined for the case of deteriorating items having a deterministic demand pattern with a linear (positive) trend A heuristic model is developed to determine the optimal times for inventory replenishment whether or not a planning horizon exists Some numerical examples are given

A Markov decision model and decomposition heuristic for dynamic vehicle dispatching

Abstract: We describe a dynamic and stochastic vehicle dispatching problem called the delivery dispatching problem. This problem is modeled as a Markov decision process. Because exact solution of this model is impractical, we adopt a heuristic approach for handling the problem. The heuristic is based in part on a decomposition of the problem by customer, where customer subproblems generate penalty functions that are applied in a master dispatching problem. We describe how to compute bounds on the algorithm's performance, and apply it to several examples with good results.

Light traffic heuristic for an M/G/ 1 queue with limited inventory

Abstract: Motivated by solving a stylized location problem, we develop a light traffic heuristic for anM/G/1 queue with limited inventory that gives rise to a closed form expression for average delay in terms of basic system parameters. Simulation experiments show that the heuristic works well. The inventory operates as follows: the inventory level drops by one unit after each service completion and whenever it drops to a pre-specified levelu, an order is placed with replenishment time ∼ exp(γ). Upon replenishment the inventory is restocked to a pre-specified levels and any arrivals when there is no inventory are placed in queue. Suggestions are given to cover the more general case of a New Better than Used (NBU) replenishment time distribution. Applications to inventory management problems are also discussed.

Scheduling medical residents to rotations: solving the large-scale multiperiod staff assignment problem

Abstract: The resident scheduling problem is a specific case of the multiperiod staff assignment problem where individuals are assigned to a variety of tasks over multiple time periods. As in many staffing and training situations, numerous limitations and requirements may be placed on those assignments. This paper presents a procedure for addressing two major problems inherent in the determination of a solution to this type of problem: infeasibilities that naturally occur in the scheduling environment but are obscured by complexity; and the intractable nature of large-scale models with this structure. The procedure developed describes a systematic approach that allows decision makers to resolve system-inherent infeasibilities, and a heuristic based on rounding to develop good feasible solutions to the model. The procedure is illustrated via a case example of resident assignments for teaching and training modules in a university affiliated teaching hospital.

Massively parallel tabu search for the quadratic assignment problem

Abstract: A new heuristic algorithm to perform tabu search on the Quadratic Assignment Problem (QAP) is developed. A massively parallel implementation of the algorithm on the Connection Machine CM-2 is provided. The implementation usesn2 processors, wheren is the size of the problem. The elements of the algorithm, calledPar_tabu, include dynamically changing tabu list sizes, aspiration criterion and long term memory. A new intensification strategy based on intermediate term memory is proposed and shown to be promising especially while solving large QAPs. The combination of all these elements gives a very efficient heuristic for the QAP: the best known or improved solutions are obtained in a significantly smaller number of iterations than in other comparative studies. Combined with the implementation on CM-2, this approach provides suboptimal solutions to QAPs of bigger dimensions in reasonable time.

Prediction-based dynamic load-sharing heuristics

Abstract: Presents dynamic load-sharing heuristics that use predicted resource requirements of processes to manage workloads in a distributed system. A previously developed statistical pattern-recognition method is employed for resource prediction. While nonprediction-based heuristics depend on a rapidly changing system status, the new heuristics depend on slowly changing program resource usage patterns. Furthermore, prediction-based heuristics can be more effective since they use future requirements rather than just the current system state. Four prediction-based heuristics, two centralized and two distributed, are presented. Using trace driven simulations, they are compared against random scheduling and two effective nonprediction based heuristics. Results show that the prediction-based centralized heuristics achieve up to 30% better response times than the nonprediction centralized heuristic, and that the prediction-based distributed heuristics achieve up to 50% improvements relative to their nonpredictive counterpart. >

A dual ascent and column generation heuristic for the discrete lotsizing and scheduling problem with setup times

Abstract: In this paper the Discrete Lotsizing and Scheduling Problem (DLSP) with setup times is considered. DLSP is the problem of determining the sequence and size of production batches for multiple items on a single machine. The objective is to find a minimal cost production schedule such that dynamic demand is fulfilled without backlogging. DLSP is formulated as a Set Partitioning Problem (SPP). We present a dual ascent and column generation heuristic to solve SPP. The quality of the solutions can be measured, since the heuristic generates lower and upper bounds. Computational results on a personal computer show that the heuristic is rather effective, both in terms of quality of the solutions as well as in terms of required memory and computation time.

Shortest paths, single origin‐destination network design, and associated polyhedra

Abstract: We study a specialized version of network design problems that arise in telecommunications, transportation, and other industries. The problem, a generalization of the shortest path problem, is defined on an undirected network consisting of a set of arcs on which we can install (load), at a cost, a choice of up to three types of capacitated facilities. Our objective is to determine the configuration of facilities to load on each arc that will satisfy the demand of a single commodity at the lowest possible cost. Our results (i) demonstrate that the single-facility loading problem and certain “common break-even point” versions of the two-facility and three-facility loading problems are polynomially solvable as a shortest path problem; (ii) show that versions of the two-facility loading problem are strongly NP-hard, but that a shortest path solution provides an asymptotically “good” heuristic; and (iii) characterize the optimal solution (i.e., specify a linear programming formulation with integer solutions) of the common break-even point versions of the two-facility and three-facility loading problems. In this development, we introduce two new families of facets, give geometric interpretations of our results, and demonstrate the usefulness of partitioning the space of the problem parameters to establish polyhedral integrality properties. Generalizations of our results apply to (i) multicommodity applications and (ii) situations with more than three facilities. © 1993 by John Wiley & Sons, Inc.

On the efficiency of parallel backtracking

Abstract: Analytical models and experimental results concerning the average case behavior of parallel backtracking are presented. Two types of backtrack search algorithms are considered: simple backtracking, which does not use heuristics to order and prune search, and heuristic backtracking, which does. Analytical models are used to compare the average number of nodes visited in sequential and parallel search for each case. For simple backtracking, it is shown that the average speedup obtained is linear when the distribution of solutions is uniform and superlinear when the distribution of solutions is nonuniform. For heuristic backtracking, the average speedup obtained is at least linear, and the speedup obtained on a subset of instances is superlinear. Experimental results for many synthetic and practical problems run on various parallel machines that validate the theoretical analysis are presented. >

A simulated annealing approach to the cyclic staff-scheduling problem

Abstract: This article presents the application of a simulated annealing heuristic to an NP-complete cyclic staff-scheduling problem. The new heuristic is compared to branch-and-bound integer programming algorithms, as well as construction and linear programming-based heuristics. It is designed for use in a continuously operating scheduling environment with the objective of minimizing the number of employees necessary to satisfy forecast demand. The results indicate that the simulated annealing-based method tends to dominate the branch-and-bound algorithms and the other heuristics in terms of solution quality. Moreover, the annealing algorithm exhibited rapid convergence to a low-cost solution. The simulated annealing heuristic is executed in a single program and does not require mathematical programming software. © 1993 John Wiley & Sons, Inc.

Integrating target analysis and tabu search for improved scheduling systems

Abstract: This paper explores the integration of the Artificial Intelligence/Operations Research approach known as target analysis with tabu search to create a more effective system for machine scheduling. Target analysis is designed to give heuristic and optimization procedures the ability to learn what rules are best for solving particular classes of problems. The authors focus on the development of rules that depend on memory functions to incorporate diversifying elements in a tabu search method which is tailored to find optimal or near optimal solutions for a class of single machine scheduling problems with delay penalties and setup costs.

Asynchronous organizations for multi-algorithm problems

Abstract: A Multi-Algorithm Problem (MAP) is a problem with many approximate algorithms available to solve it. Examples of MAPs are most of the combinatorial optimization problems and the multi-criterion problems with conflicting objectives. Since a MAP has no single algorithm able to solve it optimally in reasonable time, and each available algorithm for a MAP has its own approach to solving a problem, there exists the possibility of combining these algorithms in order to take advantage of their strengths. This thesis demonstrates that by using an Asynchronous Team, a software organization characterized by cyclic, iterative data flow and autonomous agents which communicate asynchronously through shared memories, one can synergistically combine many algorithms in order to reach better results than each algorithm can do by itself. As an example of a MAP, we chose the Euclidean version of the Traveling Salesman Problem (TSP), which is a difficult combinatorial problem and has many heuristic algorithms that only provide approximate solutions for it. Asynchronous Teams with a few algorithms were able to reach optimal solutions for all the TSP instances tackled whereas, individual algorithms could not. Moreover, parallel execution of Asynchronous Teams on a computer network presented linear speed up. We also observed that Asynchronous Teams for solving TSP are scale efficient; that is, the more algorithms an Asynchronous Team uses, the better it performs. We tested and analyzed several design parameters for Asynchronous Teams such as selection and destruction policies (how agents select and destroy data from shared memories,) initialization policies (how to initiate shared memories,) memory sizes, and the influence of data flows on the performance of Asynchronous Teams. We also present some specially developed algorithms to be exclusively executed in Asynchronous Teams. Finally, we present a Markov-based model that not only explains the behavior, but also helps in designing Asynchronous Teams by giving insights about expected value of final solutions, optimal relative execution frequencies of algorithms, and convergence of Asynchronous Teams.

An Iterative Approach to Language Containment

Abstract: We propose an iterative approach to formal verification by language containment. We start with some initial abstraction and then iteratively refine it, guided by the failure report from the verification tool. We show that the procedure will terminate, propose a series of heuristic aimed at reducing the size of BDD's used in the computation, and formulate several open problems that could improve efficiency of the procedure. Finally, we present and discuss some initial experimental results.

Algorithms for the vehicle routing problems with time deadlines

Abstract: SYNOPTIC ABSTRACTThe vehicle routing problem with time deadlines (VRPTD) is an extension of the classical vehicle routing problem (VRP) with constraints on the latest allowable time (deadline) for servicing each customer. The objective is to minimize the number of vehicles and the distance travelled without exceeding the capacity of the vehicles or violating the customer deadlines. VRPTD belongs to the class of NP-complete problems. As the computational time taken to solve such problems using exact methods is prohibitive, heuristic methods are used instead to obtain near optimal solutions for large-size problems. We develop three heuristics to solve the VRPTD: deadline sweep, push-forward insertion and genetic sectoring. The solutions obtained by these heuristics are improved using a local post-optimization procedure. Computational analysis of the three heuristics are reported on 25 problems consisting of 200 customers each with different geographical and temporal characteristics.