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Showing papers on "Heuristic (computer science) published in 1986"


Journal ArticleDOI
TL;DR: The simple assembly line balancing problem (SALBP) as discussed by the authors is a deterministic optimization problem where all input parameters are assumed to be known with certainty and all the algorithms discussed are exact.
Abstract: In this survey paper we discuss the development of the simple assembly line balancing problem SALBP; modifications and generalizations over time; present alternate 0-1 programming formulations and a general integer programming formulation of the problem; discuss other well-known problems related to SALBP; describe and comment on a number of exact i.e., optimum-seeking methods; and present a summary of the reported computational experiences. All models discussed here are deterministic i.e., all input parameters are assumed to be known with certainty and all the algorithms discussed are exact. The problem is termed "simple" in the sense that no "mixed-models," "subassembly lines," "zoning restrictions," etc. are considered. Due to the richness of the literature, we exclude from discussion here a the inexact i.e., heuristic/approximate algorithms for SALPB and b the algorithms for the general assembly line balancing problem including the stochastic models.

834 citations


Journal ArticleDOI
TL;DR: Both optimal and heuristic procedures are developed for this problem and are based on a dynamic programming formulation, which depends on the ability to solve the static problem efficiently.
Abstract: The problem of plant layout has generally been treated as a static one. In this paper, we deal with the dynamic nature of this problem. Both optimal and heuristic procedures are developed for this problem and are based on a dynamic programming formulation. The use of one of these approaches depends on the ability to solve the static problem efficiently. Finally, we briefly discuss the issue of extending the planning horizon, and how to resolve system nervousness when previously planned layouts need to be changed.

313 citations


Journal ArticleDOI
TL;DR: A Lagrangian relaxation heuristic algorithm is described for capacitated problems in which each customer is served by a single facility, by relaxing the capacity constraints and solving the uncapacitated facility location problem.
Abstract: Facility location models are applicable to problems in many diverse areas, such as distribution systems and communication networks. In capacitated facility location problems, a number of facilities with given capacities must be chosen from among a set of possible facility locations and then customers assigned to them. We describe a Lagrangian relaxation heuristic algorithm for capacitated problems in which each customer is served by a single facility. By relaxing the capacity constraints, the uncapacitated facility location problem is obtained as a subproblem and solved by the well-known dual ascent algorithm. The Lagrangian relaxations are complemented by an add heuristic, which is used to obtain an initial feasible solution. Further, a final adjustment heuristic is used to attempt to improve the best solution generated by the relaxations. Computational results are reported on examples generated from the Kuehn and Hamburger test problems.

183 citations


Journal ArticleDOI
TL;DR: The three genetic operators which are the core of the reproductive design are detailed, and an algorithm is presented to illustrate applications to discrete-space location problems, particularly thep-median.
Abstract: Genetic algorithms are adaptive sampling strategies based on information processing models from population genetics. Because they are able to sample a population broadly, they have the potential to out-perform existing heuristics for certain difficult classes of location problems. This paper describes reproductive plans in the context of adaptive optimization methods, and details the three genetic operators which are the core of the reproductive design. An algorithm is presented to illustrate applications to discrete-space location problems, particularly thep-median. The algorithm is unlikely to compete in terms of efficiency with existingp-median heuristics. However, it is highly general and can be fine-tuned to maximize computational efficiency for any specific problem class.

155 citations


Proceedings ArticleDOI
02 Jul 1986
TL;DR: Two algorithms for spare allocation that are based on graph-theoretic analysis are presented, which provide highly efficient and flexible reconfiguration analysis and are shown to be NP-complete.
Abstract: The issue of yield degradation due to physical failures in large memory and processor arrays is of significant importance to semiconductor manufacturers. One method of increasing the yield for iterated arrays of memory cells or processing elements is by incorporating spare rows and columns in the die or wafer which can be programmed into the array. This paper addresses the issue of computer-aided design approaches to optimal reconfiguration of such arrays. The paper presents the first formal analysis of the problem. The complexity of optimal reconfiguration is shown to be NP-complete for rectangular arrays utilizing spare rows and columns. In contrast to previously proposed exhaustive search and greedy algorithms, this paper develops a heuristic branch and bound approach based on the complexity analysis, which allows for flexible and highly efficient reconfiguration. Initial screening is performed by a bipartite graph matching algorithm.

142 citations


Journal ArticleDOI
TL;DR: Heuristic and optimal solution procedures are developed and computational experience with these procedures is reported and Implications of the model for designing distributed systems are discussed.
Abstract: Design of distributed computer systems is a complex task requiring solutions for several difficult problems. Location of computing resources and databases in a wide-area network is one of these problems which has not yet been solved satisfactorily. Solution of this problem involves determining number and size of computer facilities and their locations, configuring databases and allocating these databases among computer facilities. An integer programming formulation of the problem is presented. Heuristic and optimal solution procedures are developed and computational experience with these procedures is reported. Implications of the model for designing distributed systems are discussed.

129 citations


Journal ArticleDOI
TL;DR: In this paper, a heuristic method based on Lagrangian relaxation is proposed for multilevel lot sizing when there is a single bottleneck facility, where the objective is to find a production schedule that fits within available capacity at minimum cost.
Abstract: In this paper we present a heuristic method, based on Lagrangian relaxation, for multilevel lot-sizing when there is a single bottleneck facility. A series of Lagrangian relaxations one for each item in the product structure is imbedded in a branch and bound procedure. The objective is to find a production schedule that fits within available capacity at minimum cost. The method has two solution phases, dual and primal. In the dual phase of the procedure, implied costs of setups and production are determined based on a tentative schedule. The primal phase is repeated with these new prices and we iterate to reach a good solution. The solution procedure is first tested on two special cases: uncapacitated multilevel lot-sizing and the capacitated, single-level multi-item lot sizing problem. The results show that the solution procedure can provide better solutions than some heuristics designed especially for those problems. Test results on the bottleneck problem indicate that good feasible solutions are found for problems too difficult to solve with exact methods.

123 citations


Journal ArticleDOI
TL;DR: In this article, the authors introduce the hierarchical network design problem (HNDP) and present a heuristic which employs a K shortest path algorithm, and a minimum spanning tree algorithm.

119 citations


Journal ArticleDOI
TL;DR: Three heuristic route improvement schemes based on the concept of node interchange between different routes using a modified Clark and Wright algorithm are presented together with their computational performance when applied to an inventory routing problem for 12 consecutive weekly periods.

107 citations


Journal ArticleDOI
TL;DR: Two linear expected time complexity greedy algorithms are proposed for the determination of a lower bound on the optimal value by using a cascade of surrogate relaxations of the original problem whose sizes are decreasing step by step.

83 citations


Journal ArticleDOI
TL;DR: A fast heuristic for this important class of problems is presented and its worst-case performance is analyzed: the ratio of the heuristic value to the optimum does not exceed the maximum row sum of the matrix A.

Journal ArticleDOI
TL;DR: In this article, the authors consider the problem of determining a schedule of capacity expansions for m producing regions and a schedule from the regions to n markets so as to meet market demands over a T-period planning horizon at minimum discounted capacity expansion and shipment costs.
Abstract: We consider the problem of determining a schedule of capacity expansions for m producing regions and a schedule of shipments from the regions to n markets so as to meet market demands over a T-period planning horizon at minimum discounted capacity expansion and shipment costs. The proposed algorithm permits capacity expansion costs to be arbitrary nonnegative increasing functions of the expansion amounts, but the shipment (and production) costs are restricted to be proportional to the amounts shipped. The algorithm does not require market demands to be increasing over time. The cost functions are allowed to be nonstationary and the possibility of imports is considered. The proposed heuristic algorithm improves on feasible solutions by simultaneously reassigning several capacity expansions to different regions and/or time periods. A look-ahead feature prevents the algorithm from becoming myopic and a self-learning feature dynamically updates computational parameters. The heuristic algorithm was tested on b...

Proceedings ArticleDOI
02 Jul 1986
TL;DR: An algorithm is given for generating tests from chip-level functional descriptions, and uses the hardware description language (HDL) definition to solve for the test vector.
Abstract: An algorithm is given for generating tests from chip-level functional descriptions. The algorithm uses a chip-level fault model to define faults and fault sensitization requirements, and uses the hardware description language (HDL) definition to solve for the test vector. Artificial intelligence techniques of goal trees and rule databases are used to implement the algorithm in ProLog. The goal types and solving strategies are outlined. The current, partial ProLog implementation is discussed.

Journal ArticleDOI
TL;DR: The program Priam presented hereafter is an interactive program for chosing a best issue from a multiple attribute alternative set using the artificial intelligence methods and relies on a heuristic exploration of the alternative set.

Journal ArticleDOI
TL;DR: From the existing algorithms for group scheduling, a heuristic algorithm has been developed and programmed for computer/microcomputer applications to determine the optimal group and the optimal job sequence for a batch type production process with functional layout.

Journal ArticleDOI
TL;DR: In this article, the authors considered a generalization of the classic uncapacitated facility location problem (UFLP) in which customers require multiple products, and obtained lower bounds by solving a UFLP subproblem for each product using a dual ascent routine.

Journal ArticleDOI
TL;DR: A formal representation is developed for the process of problem structuring to enable decomposition and recomposition of architectural problems and consists of a heuristic method for manipulating the parameters of a general purpose generate-and-test mechanism which is capable of solving well-defined problems.
Abstract: It is no longer unusual to find automated systems as partners in the architectural design process. Most, if not all, of these systems are limited to solving well-structured problems efficiently and accurately. The use of these systems relies on manual decomposition of complex problems into more limited, well-defined ones and the subsequent recomposition of the solutions into comprehensive ones. In this paper a formal representation is developed for the process of problem structuring to enable decomposition and recomposition of architectural problems. This consists of a heuristic method for manipulating the parameters of a general purpose generate-and-test mechanism which is capable of solving well-defined problems.

Journal ArticleDOI
TL;DR: It is shown that the real problems are easier to solve than the simulated ones; and the Natural Order heuristic is more effective on real problems than simulated ones.
Abstract: This paper compares and contrasts the use of simulated versus real data in testing the efficiency of scheduling algorithms. Five hypotheses, formulated to evaluate two important measures of algorithm efficiency, viz., CPU time and the number of iterations, are tested on over one hundred problems drawn from different sources. On the basis of these empirical results, it is shown that: (1) the real problems are easier to solve than the simulated ones; and (2) the Natural Order heuristic is more effective on real problems than simulated ones. Implications of these results for testing the efficiency of algorithms are discussed.

Journal ArticleDOI
01 May 1986
TL;DR: A new method developed for solving the thermal unit commitment problem in large-scale electrical power systems semi-rigorously, by using a hybrid form of the discrete decision linear programming (DDLP) and heuristic techniques, capable of incorporating all the operational constraints of the system and obtaining fully feasible schedules.
Abstract: The paper describes a new method developed for solving the thermal unit commitment problem in large-scale electrical power systems semi-rigorously, by using a hybrid form of the discrete decision linear programming (DDLP) [13] and heuristic techniques. It is capable of incorporating all the operational constraints of the system and obtaining fully feasible schedules. The developed algorithm has been tested on a CDC7600 computer to solve the thermal unit commitment problem in the South-Western Region subsystem of the CEGB. Simulation results have shown that various solutions with different characteristics can be obtained for the same problem, requiring modest computer time and memory. Encouraging financial savings have also been obtained due to employing an optimisation (DDLP) technique within the method.

Journal ArticleDOI
TL;DR: This paper presents two algorithms that generate parallel suffix solutions with minimum cost for a given length, time delay, availability of initial values, and fanout and reduces the size of the solutions generated.
Abstract: The suffix problem has appeared in solutions of recurrence systems for parallel and pipelined machines and more recently in the design of gate and silicon compilers. In this paper we present two algorithms. The first algorithm generates parallel suffix solutions with minimum cost for a given length, time delay, availability of initial values, and fanout. This algorithm generates a minimal solution for any length n and depth range from log2 n to n. The second algorithm reduces the size of the solutions generated by the first algorithm.

Journal ArticleDOI
TL;DR: A heuristic approach of the construction type proposed for solving a multiple objectives facilities layout problem, based on the concept of similarity coefficient, aims at forming cells of highly interrelated facilities.

Journal ArticleDOI
TL;DR: The motivation here is to develop a polynomial time heuristic which is effective with respect to the quality of solutions obtained, while at the same time not being computationally very expensive.

01 Jun 1986
TL;DR: A heuristic algorithm to schedule hard real-time tasks, i.e. tasks that have deadlines, in a distributed system, is described, an attempt to overcome the exponential problem of scheduling.
Abstract: In the design of real-time computer systems, the scheduling problem is considered to be an important one and has been addressed by many researchers. However, most approaches have not dealt with tasks' resource requirements. In this dissertation, we will describe a heuristic algorithm to schedule hard real-time tasks, i.e. tasks that have deadlines, in a distributed system. Salient features of our algorithm are that it takes tasks' resource requirements into account, is dynamic, and is distributed. When a task arrives at a node, the scheduler component local to that node attempts to schedule the task on that node. If the attempt fails, the scheduling components on individual nodes cooperate to determine which node has sufficient resource surplus to finish the task before its deadline. This cooperation occurs through exchange of state information among nodes. Determination of a good destination node for a task is based on a technique that combines bidding and focused addressing. In the former, a good node is selected based on the bids that nodes send for the task; in the latter, a node that is estimated to have more than sufficient surplus to guarantee the task is said to be good. By properly combining these two schemes, we make use of their positive features while overcoming their shortcomings. Our heuristic algorithm is an attempt to overcome the exponential problem of scheduling. The algorithm for the local scheduler incorporates various factors that affect real-time scheduling to actively direct the scheduling process. The scheme for cooperation among nodes functions in spite of imprecise and incomplete global state information. Simulation studies show that our heuristic scheduling algorithm functions well in a wide range of application environments. The performance of our heuristic algorithm measured under various metrics is very close to that obtained via the "optimal" algorithm though the optimal algorithm has been proved to be NP-hard.

Journal ArticleDOI
TL;DR: The sequencing problem is solved using a branch and bound procedure with Lagrangean relaxation providing bounds and a particularly effective heuristic is also developed.
Abstract: We consider a problem of sequencing capacity expansion projects with a continuous demand function specified over a given time horizon. Each type of expansion project has a specified integer capacity and an associated cost which is nonincreasing with respect to the time at which the project is brought on stream. The problem is to determine the sequence of expansions to provide sufficient capacity to meet demand at minimum cost. A formulation is presented and its relaxation leads to a shortest route problem. The sequencing problem is solved using a branch and bound procedure with Lagrangean relaxation providing bounds. A particularly effective heuristic is also developed. Computational results are given.

Journal ArticleDOI
TL;DR: Barcelo and Casanovas as discussed by the authors showed that the condition as shown by the authors is only a necessary condition and not a sufficient condition using an example, and they proposed a heuristic Lagrangean relaxation algorithm for the capacitated plant location problem.

Journal ArticleDOI
TL;DR: In this paper, the authors present an efficient algorithm for the multiconstraint general knapsack problem, which considers multiple resource constraints and permits multiple (rather than single) units of each item to be placed in the knapsacks to maximize the total value of items chosen.
Abstract: This paper presents efficient algorithms for the multiconstraint general knapsack problem. This version of the knapsack problem considers multiple resource constraints and permits multiple (rather than single) units of each item to be placed in the knapsack to maximize the total value of items chosen. Various relaxations of this problem are suggested and the bounds derived from these relaxations are compared. Heuristic procedures for obtaining good feasible solutions are described and computational results with these procedures are reported. Rules for reducing problem size are suggested. An efficient branch and bound code is developed, tested and compared with a state of the art commercial integer programming package. Solution times with this new code are found to be significantly lower than solution times with the commercial code.

01 Nov 1986
TL;DR: It is demonstrated that the switching algorithm can be applied to synthesis problems with the objective of minimizing the largest deviation between a prescribed location and the corresponding desired location.
Abstract: The following satellite synthesis problem is addressed: communication satellites are to be allotted positions on the geostationary arc so that interference does not exceed a given acceptable level by enforcing conservative pairwise satellite separation. A desired location is specified for each satellite, and the objective is to minimize the sum of the deviations between the satellites' prescribed and desired locations. Two mixed integer programming models for the satellite synthesis problem are presented. Four solution strategies, branch-and-bound, Benders' decomposition, linear programming with restricted basis entry, and a switching heuristic, are used to find solutions to example synthesis problems. Computational results indicate the switching algorithm yields solutions of good quality in reasonable execution times when compared to the other solution methods. It is demonstrated that the switching algorithm can be applied to synthesis problems with the objective of minimizing the largest deviation between a prescribed location and the corresponding desired location. Furthermore, it is shown that the switching heuristic can use no conservative, location-dependent satellite separations in order to satisfy interference criteria.

Journal ArticleDOI
TL;DR: This paper considers good strategies for search when the total search time is fixed and the criterion for optimality considered in this paper is maximum probability of detection for specified false alarm probability.
Abstract: The search for weak signals with unknown center frequency using energy detectors is a resource allocation problem. This paper considers good strategies for search when the total search time is fixed. The criterion for optimality considered in this paper is maximum probability of detection for specified false alarm probability. The problem is shown to be one in nonlinear programming which has a solution via dynamic programming methods. Several computer simulations are presented for representative cases of interest. A heuristic discussion of these strategies is presented together with suboptimal strategies that require a lower computational burden.

Posted Content
01 Jan 1986
TL;DR: In this article, a formal representation is developed for the process of problem structuring to enable decomposition and recomposition of architectural problems, which consists of a heuristic method for manipulating the parameters of a general purpose generate-and-test mechanism which is capable of solving well defined problems.
Abstract: It is no longer unusual to find automated systems as partners in the architectural design process Most, if not all, of these systems are limited to solving well-structured problems efficiently and accurately The use of these systems relies on manual decomposition of complex problems into more limited, well-defined ones and the subsequent recomposition of the solutions into comprehensive ones In this paper a formal representation is developed for the process of problem structuring to enable decomposition and recomposition of architectural problems This consists of a heuristic method for manipulating the parameters of a general purpose generate-and-test mechanism which is capable of solving well-defined problems

Journal ArticleDOI
TL;DR: This work deals with some performance aspects of the implementation of an algorithm to simulate MOS electronic circuits, modeled at the transistor level, and shows the optimal way to deal with acyclic circuits and some heuristic criteria to handle cyclic circuits.
Abstract: This work deals with some performance aspects of the implementation of an algorithm to simulate MOS electronic circuits, modeled at the transistor level. The target architecture is a special purpose logic simulation machine, the Yorktown Simulation Engine (YSE), which has no direct support for loop mechanisms or conditional flow control. Since the simulation algorithm requires such mechanisms to determine whether and when a circuit reaches a steady state, we must calculate prior to simulation time, the number of iterations required for the algorithm to converge. We show how to arrange the order in which transistors are processed, aiming at a reduced number of such iterations, and therefore, an improved simulation performance. The results presented here show the optimal way to deal with acyclic circuits and some heuristic criteria to handle cyclic circuits. Also, we show a method to calculate the number of iterations required for the convergence of the simulation algorithm. These methods, originally developed for the YSE, have been also incorporated in a switch level simulator running on an IBM/370 architecture.