Browsing by Author "Singer, M"

Now showing 1 - 5 of 5
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    A shifting bottleneck heuristic for minimizing the total weighted tardiness in a job shop
    (WILEY, 1999) Pinedo, M; Singer, M
    We present a shifting bottleneck heuristic for minimizing the total weighted tardiness in a job shop. The method decomposes the job shop into a number of single-machine subproblems that are solved one after another. Each machine is scheduled according to the solution of its corresponding subproblem. The order in which the single machine subproblems are solved has a significant impact on the quality of the overall solution and on the time required to obtain this solution. We therefore test a number of different orders for solving the subproblems. Computational results on 66 instances with ten jobs and ten machines show that our heuristic yields solutions that are close to optimal, and it clearly outperforms a well-known dispatching rule enhanced with backtracking mechanisms. (C) 1999 John Wiley & Sons, Inc.
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    An architecture for solving sequencing and resource allocation problems using approximation methods
    (STOCKTON PRESS, 1998) Nussbaum, M; Sepulveda, M; Singer, M; Laval, E
    In the search for better optimisation techniques, new methods that mix artificial intelligence and operations research have emerged. Search heuristics are integrated with optimisation algorithms. Approximation methods, like Hill Climbing, Simulated Annealing, and Tabu Search, that have been used with success in combinatorial optimisation problems, are one of such research lines. This paper presents the key elements of approximation methods and combines them in a tool appropriate for solving sequencing and resource allocation problems. The system permits a clear division between problem specification and problem solving, allowing a declarative representation and therefore minimising developing costs. The key issues discussed in this work are a model for representing this class of problems in a standard form, a set of strategies for applying the approximation methodology, and an expert system that dynamically manipulates the strategies' parameters.
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    Decision support system for conflict diagnosis in personnel selection
    (ELSEVIER SCIENCE BV, 1999) Nussbaum, M; Singer, M; Rosas, R; Castillo, M; Flies, E; Lara, R; Sommers, R
    A decision support system (DSS) is discussed here. It was developed to analyze the interpersonal behavior of members of a work team; they are tested while interacting in a set of situations simulating a daily working session. By comparing the reactions of each member, it is possible to assess the members' working style compatibility. The system can, therefore, be configured to test specific features of interpersonal behavior, such as potential conflict factors; these can then be used to assess the effect of adding a member to a team during personnel selection, Field tests have showed that the system can satisfactorily predict potential conflicts within a group. (C) 1999 Elsevier Science B.V. All rights reserved.
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    Decomposition methods for large job shops
    (PERGAMON-ELSEVIER SCIENCE LTD, 2001) Singer, M
    A rolling horizon heuristic is presented for large job shops, in which the total weighted tardiness must be minimized. The method divides a given instance into a number of subproblems, each having to correspond to a time window of the overall schedule, which are solved using a shifting bottleneck heuristic. A number of rules for defining each time window are derived. The method is tested by using instances up to 10 machines and 100 operations per machine, outperforming a shifting bottleneck heuristic that has been shown to generate close to optimal results.
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    Forecasting policies for scheduling a stochastic due date job shop
    (TAYLOR & FRANCIS LTD, 2000) Singer, M
    This work studies the problem of scheduling a production plant subject to uncertain processing times that may arise, e.g. from the variability of human labour or the possibility of machine breakdowns. The problem is modelled as a job shop with random processing times, where the expected total weighted tardiness must be minimized. A heuristic is proposed that amplifies the expected processing times by a selected factor, which are used as input for a deterministic scheduling algorithm. The quality of a particular solution is measured using a risk averse penalty function combining the expected deviation and the worst case deviation from the optimal schedule. Computational tests show that the technique improves the performance of the deterministic algorithm by similar to 25% when compared with using the unscaled expected processing times as inputs.