Fuzzy Project Research Articles

Project scheduling under uncertainty: Survey and research potentials

The vast majority of the research efforts in project scheduling assume complete information about the scheduling problem to be solved and a static deterministic environment within which the pre-computed baseline schedule will be executed. However, in the real world, project activities are subject to considerable uncertainty, which is gradually resolved during project execution. In this survey we review the fundamental approaches for scheduling under uncertainty: reactive scheduling, stochastic project scheduling, fuzzy project scheduling, robust (proactive) scheduling and sensitivity analysis. We discuss the potentials of these approaches for scheduling under uncertainty projects with deterministic network evolution structure.

Assessing renewables-to-electricity systems: a fuzzy expert system model

The assessment of Renewables-to-Electricity Systems is a complex, time-consuming task and requires skilled, experienced engineers. This paper describes the on-going research effort that takes place in the development of a new Intelligent Approach, an efficient decision making tool in this problem based on the employment of the Expert Systems and Fuzzy Logic techniques. As far as expert knowledge representation is concerned, the proposed approach is based on Expert System techniques (rule-based methodology). Moreover, trying to assess a Renewables-to-Electricity project or several alternative ones, the analysis has to face, in general, a series of uncertainties. To handle effectively these uncertainties, a new methodology is proposed (by use of Fuzzy Sets Theory and Fuzzy Logic Techniques). The proposed Fuzzy Project Priority Index for each Renewables-to-Electricity System is very useful especially in decision-makers. In order to demonstrate the proposed intelligent fuzzy analysis-based approach a simple case study is provided, supposing that a legal entity is to assess and finally select/propose an electricity production system, which uses RES (wind energy OR solar energy to photovoltaic OR small hydro). As fuzzy variables are concerned the Life Cycle Analysis (versus equipment production, plant preparation, operation and decommissioning) and the Development Cost (versus firm capabilities, spillover effects and potential downside damage).

Where Do We Stand with Fuzzy Project Scheduling?

Fuzzy project scheduling has interested several researchers in the past two decades; about 20 articles have been written on this issue. Contrary to stochastic project-scheduling approaches that are used by many project schedulers, and even if the axiomatic associated to the theory of probabilities is not always compatible with decision-making situations, fuzzy project-scheduling approaches that are most suited to these situations have been kept in the academic sphere. This paper starts by recalling the differences one can observe between uncertainty and imprecision. Then most of the published research works that have been done in this field are summarized. Finally, a framework for addressing the resource-constrained fuzzy project-scheduling problem is proposed. This framework uses temporal linguistic descriptors, which might become very interesting features to the project-scheduling practitioners.

A fuzzy project scheduling approach to minimize schedule risk for product development

The efficient management of product development projects is important to reduce the required development time and cost. However, each project is unique in nature and the duration of activities involved in a project often cannot be predicted accurately. The uncertainty of activity duration may lead to incorrect scheduling decisions. The objective of this research is to develop a fuzzy scheduling methodology to deal with this problem. Possibility theory is used to model the uncertain and flexible temporal information. The concept of schedule risk is proposed to evaluate the schedule performance. A fuzzy beam search algorithm is developed to determine a schedule with the minimum schedule risk and the start time of each activity is selected to maximize the minimum satisfaction degrees of all temporal constraints. In addition, the properties of schedule risk are also discussed. We show that the proposed methodology can assist project managers in selecting a schedule with the least possibility of being late in an uncertain scheduling environment. An example with an electronic product development project is used to illustrate the developed approach.

Fuzzy division in fuzzy relational databases: an approach

An approach to calculate the relational division in fuzzy databases is presented, starting with the GEFRED model. The tuples of the division are obtained with a possibility degree that indicates the extent to which they comply with the relational division requisites. The results obtained are those expected a priori. Furthermore, in order to formalize this method of fuzzy relational division, two new operators are defined which may be useful in other operations: the qualified fuzzy intersection and the generalized fuzzy projection with group functions. Some examples are included in order to clarify the method proposed here and the defined operators. A comparison with other fuzzy division approaches that appear in the literature is included too.

Finding multiple possible critical paths using fuzzy PERT

Program evaluation and review techniques (PERT) is an efficient tool for large project management. In actual project control decisions, PERT has successfully been applied to business management, industry production, project scheduling control, logistics support, etc. However, classical PERT requires a crisp duration time representation for each activity. This requirement is often difficult for the decision-makers due to the fact that they usually can not estimate these values precisely. In recent years, some fuzzy PERT methods have been proposed based on fuzzy set theory for project management. However, there is a drawback in the existing fuzzy PERT methods, i.e., sometimes they maybe cannot find a critical path in a fuzzy project network. In this paper, we propose a fuzzy PERT algorithm to find multiple possible critical paths in a fuzzy project network, where the duration time of each activity in a fuzzy project network is represented by a fuzzy number. The proposed algorithm can overcome the drawback of the existing fuzzy PERT methods.

ファジィプロジェクトネットワーク問題におけるクリティカルパスの解析

ファジィプロジェクトネットワーク問題におけるクリティカルパスの解析

Fuzzy Optimal Model for Resource-Constrained Construction Scheduling

Activity duration is uncertain due to the variation in the outside environment, such as weather, site congestion, productivity level, etc. Furthermore, resources for construction activities are limited in the real construction world so that scheduling must include resource allocation. A new optimal resource-constrained construction scheduling model is proposed in this paper, in which the effects of both uncertain activity duration and resource constraints are taken into account. Fuzzy set theory is used to model the uncertainties of activity duration. A genetic algorithm-based searching technique is adopted to search for the fuzzy optimal project duration under resource constraints. The model can effectively provide the optimal fuzzy profiles of project duration and resource amounts under the constraint of limited resources.

Fuzzy projection pursuit density estimation by eigenvalue method

This paper focuses on the use of kernel method and projection pursuit regression for non-parametric probability density estimation. Direct application of the kernel method is not able to pick up characteristic features of multidimensional density function. We propose a fuzzy projection pursuit density estimation based on the membership function and the eigenvector of the covariance matrix. Marginal densities along the subspace spanned by the projection vector are estimated. The proposed projection pursuit is one of the methods which are able to bypass the ‘curse of dimensionality’ in multidimensional density estimation. An application to experimental design for machining accuracy of end milling with the tool in small diameter is presented to demonstrate its usefulness.

A Fuzzy Chip Controller for Nonlinear Vibrations

The fuzzy logic ability to manage nonlinear vibration control problems has one major drawback: the slow reaction time offered by a software-implemented controller. This justifies the adoption of integrated fuzzy chip controllers. Nevertheless, designing a fuzzy controller for structural control purposes requires some fine tuning work. This can be conveniently carried out over an equivalent electronic circuit which emulates a civil structure, rather than by an expensive laboratory environment for testing frame specimens or by using computer simulations which would not allow a real-time study. Conceiving and implementing an electronic circuit equivalent to a single-degree-of-freedom system is the topic of this paper. A case study is finally presented. It discusses the major features of the adopted 'fuzzy chip controller'. A brief overview of the results of the laboratory tests is also given. They were performed in order to properly tune the fuzzy project with civil structure applications.

Fuzzy loss less decompositions in databases

In this paper we deal with the problem of defining projection and join operators in the framework of a fuzzy relational database. We study the properties of such operators relating to the concept of fuzzy functional dependency. There are two main properties to achieve: On the one hand, if a relation r satisfies a dependency between two sets of attributes, X and Y, then we want to preserve this dependency in the fuzzy projection over XY of r. On the other hand, we want to test a fuzzy dependency just by looking at this projection. Therefore, such a projection operator must allow us to store the information conveyed in r into a separate relation with fewer tuples. In order to define the criterion for fuzzy dependency, we shall need to extend a resemblance relation defined for crisp data to the fuzzy case. Finally, we shall introduce a fuzzy join operator which will allow us to recover the information given in a relation, from its projections.

Remarks on products of L-fuzzy topological spaces

Two essential theorems about fuzzy projection are improved, and a negative answer to Wang's question (in 1987) on products of L-fuzzy topological spaces is given.

Capital Budgeting Decisions With Fuzzy Projects

ABSTRACT In this paper, we propose a capital budgeting model under uncertainty in which cash flow information is specified as a special type of fuzzy number—triangular fuzzy numbers. We then estimate the present worth of each fuzzy project cash flow. Using the present worth criterion, we propose a new project dominance method to determine the preference of fuzzy projects. To select fuzzy projects under limited capital budget, a branch and bound procedure is suggested. To illustrate the procedure, we give a numerical example and compare and analyze the results of the capital rationing problem using the proposed dominance method and two other conventional dominance methods.

FUZZY PROJECT SCHEDULING MODELS UNDER INFLATION CONDITION

The paper defines four fuzzy project scheduling models under inflation condition without or with budget limit. The activity time is described by fuzzy relation or fuzzy variables. The models can be solved in the forms of crisp LP or NLP after using the concept of α-cut.

Regression and Classification by Neuro Fuzzy Projection Pursuit

Regression and Classification by Neuro Fuzzy Projection Pursuit

An efficient approach for large scale project planning based on fuzzy Delphi method

The goal of this paper is to replace probabilistic or deterministic considerations in the project network analysis by possibilistic ones and to reduce the difficulty arising from the inexact and insufficient information of activity times. The activity times are considered as fuzzy numbers (fuzzy intervals or time intervals) and the fuzzy Delphi method is used to estimate a reliable time interval of each activity. Based on these time estimates, we then propose an efficient methodology for calculating the fuzzy project completion time and the degree of criticality for each path in a project.

Ranking multicritena fuzzy alternatives in the decision-maker's preference subspace

This paper offers a new procedure for ranking multicritena fuzzy alternatives when the decision-maker subscribes to the notion of ‘the larger, the better’. For each alternative a joint membership function captures possible interactions among ratings for each criterion. The ranking procedure first orthogonally projects the joint membership functions from the multicritena decision space to the one-dimensional preference subspace, and then the fuzzy projections are ranked in that subspace. A method for generating joint membership functions is introduced, and a numerical example is presented.

Fuzzy project scheduling system for software development

This paper presents an FPS (Fuzzy Project Scheduling) decision support system applied to software project scheduling. The purpose of the FPS system is to allocate resources (software engineers) among dependent activities (system design, user interface design, modules implementation, modules integration and tests), taking into account one of two criteria: project completion time and maximum lateness, under uncertain time parameters of activities. 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 deterministic problems is based on the use of α-cuts. Optimistic and pessimistic schedules are heuristically generated for given α-levels. Aggregation of optimistic and pessimitic values of a minimized criterion for all α-levels gives a fuzzy result. Comparison of fuzzy result is based on a compensation of areas determined by the membership functions. The FPS system is described and its application to software project management is presented on a real example.

Morphology of Muerto Canyon virus, causative agent of hantavirus pulmonary syndrome

Hantavirus pulmonary syndrome (HPS) is a newly described illness which was first recognized in May, 1993, in the southwestern US. The disease is characterized by fever, myalgias, headache, and cough,with rapid development of fatal respiratory distress. The causative agent, Muerto Canyon virus (MCV), was recently isolated at CDC from a deer mouse, Peromyscus maniculatus, the most common vector for this virus. In order to fully understand this newly discovered virus, we have characterized the isolate by thin section and negative stain TEM, by SEM, and by pre- and post-embedded immunolabeling of thin sections.MCV is a hantavirus, a member of the family Bunyaviridae. In thin sections of infected Vero E6 culture cells, the extracellular particles are spherical to oval, with fuzzy surface projections along the envelope (figs. 1,2). The interior of the virus contains strands of nucleocapsid. Particles are similar in morphology to Hantann virus, the causative agent of Korean hemorrhagic fever. The negative staining of purified particles (fig. 3) also revealed typical hantavirus morphology of a spherical particle with grid-like surface. Examination by SEM showed scattered particles along the cell surface and among the filopodia.

FUZZY CASH FLOW ANALYSIS USING PRESENT WORTH CRITERION

ABSTRACT In practice, engineering economic analysis involves uncertainty about future cash flows. To deal quantitatively with imprecision or uncertainty, fuzzy set theory is primarily concerned with vagueness in human thoughts and perceptions. As an alternative to conventional cash flow models where cash flows are defined as either crisp numbers or risky probability distributions, we propose an engineering economic decision model in which the uncertain cash flows and discount rates are specified as triangular fuzzy numbers. The present worth formulation of this fuzzy cash flow model is derived. The result of the present worth is also a fuzzy number with nonlinear membership function. The present worth can be approximated by a triangular fuzzy number. Deviation between exact present worth and its approximate form is examined. Finally, the fuzzy project selection is performed by applying different dominance rules. To demonstrate the application of the fuzzy present worth function, a comprehensive numerical ...