Showing papers on "Heuristic (computer science) published in 1970"

A Heuristic Algorithm for the n Job, m Machine Sequencing Problem

Abstract: This paper describes a simple algorithm for the solution of very large sequence problems without the use of a computer. It produces approximate solutions to the n job, m machine sequencing problem where no passing is considered and the criterion is minimum total elapsed time. Up to m-1 sequences may be found.

The fixed charge problem

Abstract: The fixed charge problem is a nonlinear programming problem of practical interest in business and industry. Yet, until now no computationally feasible exact method of solution for large problems had been developed. In this paper an exact algorithm is presented which is computationally feasible for large problems. The algorithm is based upon a branch and bound approach, with the additional feature that the amount of computer storage required remains constant throughout (for a problem of any given size). Also presented are three suboptimal heuristic algorithms which are of interest because, although they do not guarantee that the true optimal solution will be found, they usually yield very good solutions and are extremely rapid techniques. Computational results are described for several of the heuristic methods and for the branch and bound algorithm.

A man-machine approach toward solving the traveling salesman problem

Abstract: The traveling salesman problem, (form a circuit through N points with no subloops in such a way as to minimize the length of the circuit), is a close kin to many board wiring problems. It has been attacked by many mathematical methods with only meager results. Only for special forms of the problem or for problems with relatively few points can it be solved exactly even using very large amounts of computer time. Heuristic procedures have been proposed and tested with only slightly better results. This paper will describe a computer-aided heuristic technique which uses only a modest amount of computer time in real time to solve large (100-200) point problems. This technique takes advantage of both the computer's and the human's problem solving abilities. The computer is not asked to solve the problem in a brute force way as is the case in many of today's heuristics but it is asked to organize the data for the human in a fashion that allows the human to solve the problem easily.The techniques employed in this paper require that the computer and the human cooperate to find the solution to the problem in reasonable amounts of both of their times. The computer initially uses a series of heuristics that produce groups of points and some partial connections of these points. The human is asked to connect the points within the groups and then connect the groups in a manner that produces a circuit and appears to the human to maximize the ratio of enclosed area to perimeter. The computer takes this solution and uses another set of heuristics to make improvements. The solution is displayed to the human and if he is satisfied the procedure stops; if not the former procedure is repeated until the human is satisfied that cost for finding a better solution exceeds his estimate of the best possible improvement that could be obtained by further work. The heuristic procedures seek to group points around information obtained from solving a series of mathematical programming problems (assignment problems) and some observed correlations between these problems and the traveling salesman problem.The results are very good. The man-machine interaction solution for all problems in the literature is within one per cent of the solution for a fraction of the computer time. The experience to date indicates that the technique can be taught to inexperienced persons and their results, after training, are similar to those of the authors'.

Heuristic Techniques for Solving Large Combinatorial Problems on a Computer

Abstract: Many problems of optimization, search, or decision-making are combinatorial in nature and basically non-numerical. The advent of the modern high-speed digital computer has opened the way for us to solve many of these problems. Although virtually all of these problems are finite, it is well-known that their size grows extremely rapidly and we can usually expect the computer to do by exhaustive search just a few cases larger than what can be done by hand. Intelligent search procedures such as banach and bound (Little, Murty, Sweeney and Karel (1963), Lawles and Wood (1964)), back-track programming (Golomb and Baument (1965), Walker (1960)), linear or dynamic programming (Gomery (1963), Bellman (1962), Held and Karp (1962)), together with isomorphic rejection (Swift, 1960) help to reduce the total number of cases to be considered but more often than not, we are interested in the solution to problems which are still too large for these techniques. Here some sort of heuristics have to be employed, whereby we hope that the solution (in the instance where a solution when found may be readily verified), or a probably solution, or a useful partial or approximate solution may be obtained in reasonable computation time.

How To Schedule A Flow Shop Plant By Agents

Abstract: The flow shop scheduling problem consists, according to a certain number of criteria, in finding the best possible allocation of n jobs on m resources, so that operations of every job must be processed on all resources in a unique order. Because of its highly combinatorial aspect, this scheduling procedure has been widely studied in the literature by exact and mostly heuristic methods. The approach, we adopt here to deal with this problem, combines a Multi-Agent system with a stochastic combinatorial optimization tool, the simulated annealing. This paper stresses the efficiency and the optimality of a distributed implementation of this tool compared to a classical one.

A Novel technique for Reference Node Placement in Wireless Indoor Positioning Systems based on Fingerprint Technique

Abstract: Accuracy of location determination is one of the keys to success for any indoor positioning system. This performance metric is influenced by how reference nodes (RNs) are installed. However, most of existing research studies ignored the problem of optimal reference node placement and efficient system design for indoor positioning systems. In this paper, we propose a novel technique using heuristic approach that can design suitable location to install the reference nodes and improve the location determination performance for a single-floor area and the multi-floor building. A mathematical formulation of reference node placement is developed as a Binary Integer Linear Programming (BILP) problem. The proposed formulation aims to minimize the number of reference nodes and derive their suitable locations for the indoor positioning systems. We developed an efficient solution technique based on Simulated Annealing algorithm (SA), called Maximizing Summation of the Maximum RSS for Multi-floor building (MSMR-M). The results from performance study show that by using the proposed RN placement technique, the indoor positioning systems can gain up to five meters of accuracy at 90% precision for single-floor building. Moreover, in the case of the multi-floor building, the proposed technique can improve the error distances up to 20% which is better than those of the other techniques. The proposed technique can provide an average error distance within 1.42 meters where the grid spacing of fingerprint is 2m2m.

The main principles for optimal allocation of capacities in network planning

Abstract: The problem of allocating limited capacities among activities of the set of networks is studied. This allocation must be optimal in a certain sense (e.g. minimizing duration of projects involved) and subject to the usual constraints. Different approaches to the problem are considered: from the point of view of mathematical programming and that of dynamic programming. But the problem in its general case has no way to exact solution and is to be dealt with heuristic methods. Five main principles are formulated and explained which form the basis for these methods. Different algorithms are analysed in their relations to those principles. The author proposes to build a hierarchy of algorithms on the common basis of one system of standard procedures adjusted to the problem in question. The hierarchy is to be designed to solve more common cases of the problem with simpler means and to add to those moans more complicated rules for treating less common cases containing more rare elements.

Letter to the Editor—Comments on “The Multiple Terminal Delivery Problem with Probabilistic Demands”

Abstract: In a recent paper (Tillman, F. A. 1969. The multiple terminal delivery problem with probabilistic demands. Trans. Sci. 3 192–204.) Tillman proposes a heuristic for the problem of supplying demand points P1, P2, …, Pn from m terminals T1, T2, …, Tm. Since no restrictions are imposed on the terminals, the problem is to select the cycles that join the demand points and each terminal from which these demand points receive their supplies.

A Mathematical Programming Model To Determine A Set Of Operation Lines At Minimal Costs

Abstract: A mathematical program is developed in order to determine an optimal train allocation. This allocation optimizes: # which trains to assign to long distance routes and which trains to assign to local routes. • the frequencies to operate at, and • the length of trains. The optimization model leads to a non-linear, partially quadratic mathematical program for which no standard solution technique is yet available. A heuristic approach is developed and numerically illustrated. This illustration supports a practical usefulness and further investigation of the model.

Letter to the Editor—Comments on a Heuristic Algorithm for the Multiple Terminal Delivery Problem

Abstract: The limitations of a heuristic vehicle routing procedure proposed by Tillman for the multiterminal delivery problem are investigated Because of limitations in the assignment of demand points to terminals, the procedure does not guarantee the optimality of its solution even when the solution consists entirely of single demand point-terminal linkings

Power-Aware Logical Topology Optimization for IP-over-WDM Networks Based on Per-Lightpath Power Consumption Model

Abstract: This paper considers the problem of logical topology optimization in an IP-over-WDM network, with the objective to minimize the total power consumption used to support the given traffic demands. While there are several power consuming devices in a backbone WDM network, it is assumed that major power consumption occurs at IP router ports, which are termination points of optical connections or lightpaths. The logical topology optimization problem is first formulated as an integer linear programming (ILP) problem that is computationally difficult to solve. To focus on minimizing the power consumption, it is assumed that traffic routing is not limited by link capacities. Two systematic approaches to obtain a lower bound on the power consumption are considered, namely linear programming (LP) relaxation and Lagrangian relaxation. It is shown that both methods result in the same lower bound that corresponds to shortest path routing of traffic. A heuristic based on shortest path routing and pruning of unnecessary lightpaths is then evaluated using simulation results obtained from the heuristic in comparison to the baseline case in which the logical topology is taken to be the same as the physical topology. Finally, WDM networks that support mixed line rates are considered in order to further optimize the power consumption of IP-over-WDM networks in which transmission rates over different wavelength channels are not necessarily equal.