Browsing by Author "Sepulveda, M"

Now showing 1 - 6 of 6
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    A fuel distribution knowledge-based decision support system
    (PERGAMON-ELSEVIER SCIENCE LTD, 1997) Nussbaum, M; Sepulveda, M; Cobian, A; Gaete, J; Parra, E; Cruz, J
    This paper reports the experience of solving the distribution problem for the biggest fuel company in Chile, A planning, execution and control system for fuel distribution was developed, It employs a knowledge-based approach that utilizes a graphical user interface which mimics the mental model of the user, An automatic scheduler solves the Capacity Vehicle Routing Problem, The solution ran be manually modified so verifying system consistency. The system is running throughout the country, 13 sites, having a positive impact on the three types of player, Plant managers have increased fleet efficiency and customers receive a higher quality service, Finally, the central administration obtains a report once a month from each of the plants that allows the strong and weak aspects of each site to be analyzed. (C) 1997 Elsevier Science Ltd.
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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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    Bitwise implementation of a two-dimensional cellular automata biofilm model
    (ASCE-AMER SOC CIVIL ENGINEERS, 2005) Pizarro, GE; Teixeira, J; Sepulveda, M; Noguera, DR
    Mathematical modeling using the cellular automata (CA) approach is an attractive alternative to models based on partial differential equations when the domains to be simulated have complex boundary conditions. The computational efficiency of CA models is readily observed when using parallel processors but implementations in personal computers are, although feasible, not quite efficient. In an effort to improve the computational efficiency of CA implementations in personal computers, we introduce in this paper a bitwise implementation, based on the use,of each bit as a: different CA cell. Thus, in a 32-bit processor, each computer word stores information about 32 different CA cells. We illustrate the bitwise implementation with a biofilm model that simulates substrate diffusion and microbial growth of a single-species, single-substrate, structurally heterogeneous biofilm. The efficiency of the bitwise implementation was evaluated by comparing the computational time-of equivalent CA biofilm models that used more common low-level implementations, namely, if-then operators and look-up-tables. The processing speed of the bitwise implementation was over an order of magnitude higher than the processing speed of the other two implementations. Regarding the biofilm simulations, the. CA model exhibited self-organization of the biofilm morphology as a function of kinetic and physical parameters.
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    Strategic and cognitive criteria for the selection of startups
    (ELSEVIER SCIENCE BV, 2006) Csaszar, F; Nussbaum, M; Sepulveda, M
    A decision aid is proposed that would enable venture capitalists to improve the accuracy of their decision-making processes. The methodology complements strategic criteria with cognitive ones. When questions on technical know-how and business expertise do not give enough clues, they are supplemented with a different viewpoint given by a cognitive model. The methodology is demonstrated using an e-Business case. (c) 2005 Elsevier Ltd. All rights reserved.
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    Using global search heuristics for the capacity vehicle routing problem
    (PERGAMON-ELSEVIER SCIENCE LTD, 1998) Rodriguez, P; Nussbaum, M; Baeza, R; Leon, G; Sepulveda, M; Cobian, A
    In this work, a Frame is posed which allows to define global search heuristics in an efficient and declarative way, which interacts with a specific computational implementation of a problem. An overview of different ways to solve problems by the use of global search is presented, followed by the specification of the language proposed. A real decision support system was developed through the use of the language. The problem faced was an extension of the capacity vehicle routing problem. The followed approach minimizes the development cost of a decision support system for logistic and productive environments, since the performance of different heuristics can be tested using the language in a straightforward way. Besides, when new requirements or additional knowledge about the problem appear, the solving engine can be easily modified through the heuristic language. (C) 1998 Elsevier Science Ltd All rights reserved.
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    Visiplan: A knowledge-based modelling tool
    (IEE-INST ELEC ENG, 1996) Sepulveda, M; Nussbaum, M; Levys, P
    The paper presents Visiplan, a knowledge-based modelling language that is applied to resource allocation and sequencing problems. Expert knowledge is encapsulated throughout the whole modelling and problem solving process. This enables the user's effort to be targetted at defining what he or she wishes to solve (i.e. the problem) but not how to solve it (i.e. not the implementation details behind the optimisation technique). Approximation methods that mix artificial intelligence and operation research techniques are successfully used for solving combinatorial problems. The key elements of this approach are combined in a tool appropriate for solving resource allocation and sequencing problems.