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  • 1
    Electronic Resource
    Electronic Resource
    Springer
    Annals of operations research 38 (1992), S. 397-419 
    ISSN: 1572-9338
    Keywords: Decision support systems ; model management ; distributed computing systems ; integer programming
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics , Economics
    Notes: Abstract Due to the growing popularity of distributed computing systems and the increased level of modelling activity in most organizations, significant benefits can be realized through the implementation of distributed model management systems (DMMS). These systems can be defined as a collection of logically related modelling resources distributed over a computer network. In several ways, functions of DMMS are isomorphic to those of distributed database systems. In general, this paper examines issues viewed as central to the development of distributed model bases (DMB). Several criteria relevant to the overall DMB design problem are discussed. Specifically, this paper focuses on the problem of distributing decision models and tools (solvers), henceforth referred to as theModel Allocation Problem (MAP), to individual computing sites in a geographically dispersed organization. In this research, a 0/1 integer programming model is formulated for the MAP, and an efficient dual ascent heuristic is proposed. Our extensive computational study shows in most instances heuristic-generated solutions which are guaranteed to be within 1.5–7% of optimality. Further, even problems with 420 integer and 160,000 continuous variables took no more than 60 seconds on an IBM 3090-600E computer.
    Type of Medium: Electronic Resource
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  • 2
    Electronic Resource
    Electronic Resource
    Springer
    Annals of operations research 20 (1989), S. 219-232 
    ISSN: 1572-9338
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics , Economics
    Notes: Abstract Network models are attractive because of their computational efficiency. Network applications can involve multiple objective analysis. Multiple objective analysis requires generating nondominated solutions in various forms. Two general methods exist to generate new solutions in continuous optimization: changing objective function weights and inserting objective bounds through constraints. In network flow problems, modifying weights is straightforward, allowing use of efficient network codes. Use of bounds on objective attainment levels can provide a more controlled generation of solutions reflecting tradeoffs among objectives. To constrain objective attainment, however, would require a side constrained network code, sacrificing some computational efficiency for greater model flexibility. We develop reoptimization procedures for the side constrained problem and use them in conjunction with simplex-based techniques. Our approach provides a useful tool for generating solutions allowing greater decision maker control over objective attainments, allowing multiobjective analysis of large-scale problems. Results are compared with solutions obtained from the computationally more attractive weighting technique. Reoptimization procedures are discussed as a means of more efficiently conducting multiple objective network analyses.
    Type of Medium: Electronic Resource
    Library Location Call Number Volume/Issue/Year Availability
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