ISSN:1246-0125    

DOI:10.1080/12460125.1993.10511567

出版商:Taylor & Francis Group    

年代:1993

数据来源: Taylor

ISSN:1246-0125    

DOI:10.1080/12460125.1993.10511568

出版商:Taylor & Francis Group    

年代:1993

数据来源: Taylor

摘要:

This paper presents a decision support system, called FPS (Fuzzy Project Scheduling), for solving a problem of an allocation of renewable resources among nonpreemtable and dependent activities, talking into account three criteria: project completion time, maximum lateness and mean flow time, under uncertain time parameters. By time parameters we understand: durations, ready times and due dates of particular activities. Uncertainty of these parameters is modelled by means of L—R fuzzy numbers. A general procedure for transforming the fuzzy scheduling problem into a set of associate are generated for given α-cuts. Optimistic and pessimistic schedules are generated for given α-levels. The comparison of obtained fuzzy result is based on the compensation of areas determined by membership function. The FPS system is described and illustrated using a real, agricultural example.

ISSN:1246-0125    

DOI:10.1080/12460125.1993.10511569

出版商:Taylor & Francis Group    

年代:1993

数据来源: Taylor

摘要:

Modern organisations increasingly rely on management science techniques and decision support systems (DSS) to enhance problem solving and decision making. Simulation modelling is one of the most widely applied management science techniques. However, constructing a valid model of a complex real—life system can be a tedious, lengthy and error—prone task, even when powerful model specification techniques, based on computer graphics or natural language processing (i. e. on graphical or textual descriptions of models), are incorporated. This is particulary true when attempting to model systems consisting of vast numbers of interacting elements. The paper proposes an alternative methodology for developing simulation—based decision support tools, wich is based on a DSS combining computer graphics with automatic model generation techniques. A case—study from the railway transportation domain is presented to demonstrate the main features of the system and its impact on decision making and organisational performance.

ISSN:1246-0125    

DOI:10.1080/12460125.1993.10511570

出版商:Taylor & Francis Group    

年代:1993

数据来源: Taylor

摘要:

This paper investigates the potentialities of the new technology based on neural networks to realize a DSS that might help the marketing and product planning management of a factory in conceiving strategic decisions regarding prices and product performance levels in an oligopolistic market. A possible oligopolistic market scenario assumes a multiplicity of customers whose choices on a given product set have to be explained; the individual choices of these subjects contribute to defining some market shares to be estimated—with their possible evolution—by a multicriteria model (management's problem formulation). Very few computer based systems to support such a complex problem are found in literature; developed essentially with “classic” methodologies and techniques, they suffer from considering either one single decisional subject (the market/consumer) or a multiplicity of monocriterion choices under uncertainty conditions, without taking into account any integrated representation wich might permit a multiactorial interpretation of “the market” to be given. An approach based on neural networks seems to allow such limits to be overcome, as it has been applied successfully to design and develop a prototype of DSS (MUTANT) for the problem formulated. Some first computational experiments allow some positive judgements to be expressed about the system's validity and the promising potentialities of a new “modelling and problem solving” approach that might be usefully applied so as to enlarge some potentialities of other multicriteria decision aid (MCDA) methods.

ISSN:1246-0125    

DOI:10.1080/12460125.1993.10511571

出版商:Taylor & Francis Group    

年代:1993

数据来源: Taylor

摘要:

Frequently, several heuristic strategies are relevant to a given production scheduling problem. A choice must be made among them whenever these heuristics have different performances, and when none of them is globally better than the other ones. A neural network approach for selecting the most suited heuristic is discussed. The configuration of the shop floor, the characteristics of the manufacturing program to be carried out, and the performance criteria to optimize are presented as inputs to the first layer. The most suitable heuristic is given as output. Such a neural network is trained using a large sample of simulation results as training examples. This technique is illustrated through the dynamic scheduling problem of a simplified flow shop. A back propagation neural network finds the most appropriate dispatching rules in ninety four percent of the cases. The benefits of the neural network approach over other possible methods are discussed.

ISSN:1246-0125    

DOI:10.1080/12460125.1993.10511572

出版商:Taylor & Francis Group    

年代:1993

数据来源: Taylor

ISSN:1246-0125    

DOI:10.1080/12460125.1993.10511573

出版商:Taylor & Francis Group    

年代:1993

数据来源: Taylor

ISSN:1246-0125    

DOI:10.1080/12460125.1993.10511574

出版商:Taylor & Francis Group    

年代:1993

数据来源: Taylor

ISSN:1246-0125    

DOI:10.1080/12460125.1993.10511575

出版商:Taylor & Francis Group    

年代:1993

数据来源: Taylor

ISSN:1246-0125    

DOI:10.1080/12460125.1993.10511576

出版商:Taylor & Francis Group    

年代:1993

数据来源: Taylor