Tuli Bakshi

MCA BASED PERFORMANCE EVALUATION OF PROJECT SELECTION

Multi-criteria decision support systems are used in various fields of human activities. In every alternative multi-criteria decision making problem can be represented by a set of properties or constraints. The properties can be qualitative & quantitative. For measurement of these properties, there are different unit, as well as there are different optimization techniques. Depending upon the desired goal, the normalization aims for obtaining reference scales of values of these properties. This paper deals with a new additive ratio assessment method. In order to make the appropriate decision and to make a proper comparison among the available alternatives Analytic Hierarchy Process (AHP) and ARAS have been used. The uses of AHP is for analysis the structure of the project selection problem and to assign the weights of the properties and the ARAS method is used to obtain the final ranking and select the best one among the projects. To illustrate the above mention methods survey data on the expansion of optical fibre for a telecommunication sector is used. The decision maker can also used different weight combination in the decision making process according to the demand of the system .

A soft-computing approach for software project selection

Every software manufacturing company wants to gain higher market share with the development of their products. Evolving high quality software products will decrease the cost of R&D and increase productivity. In this aspect, proper project selection in consideration with multiple factors is very vital step. There are many mutually inclusive and exclusive factors-in combinations with vagueness. The authors have tried to depict one of such situation from analytical point of view. The main objective of this paper is to explain how quality function deployment (QFD) model is used in combining with AHP under fuzziness. The authors have proposed an integrated model determines project selection system from a economic point of view also. The proposed integrated approach identifies technical requirements followed by customer requirements.

A Novel Integrated AHP-QFD Model for Software Project Selection under Fuzziness

Now-a-days software manufacturing companies usually try to capture market share with rapid software development. Actually the development of high quality software products can decrease cost by reducing rework and increasing productivity. For that, reason selection of software projects is an important task. Several factors come into consideration while selection of proper project for a specified job. To deal with this typical situation the current researchers combined the much known AHP(Analytic Hierarchy Process) with QFD (Quality Function Deployment). Here the AHP method redefined under fuzziness. Then QFD process is applied to identifying and ranking customer’s need and translating them into product / service specification. At the last sensitivity analysis has been done to make the process more robust from the characterization of integration of the fuzzy AHP & QFD. General Terms Software project selection; fuzziness; sensitivity analysis.

A New Meta-Heuristic PSO Algorithm for Resource Constraint Project Scheduling Problem

In this paper a meta heuristic Particle Swarm Optimization (PSO)-based approach for the solution of the resource-constrained project scheduling problem with the purpose of minimizing project time has been developed. In order to evaluate the performance of the PSO based approach for the resource-constrained project scheduling problem, computational analyses are given. As per the results the application of PSO to project scheduling is achievable.

A new soft-computing based framework for project management using game theory

Software project success or failure depends on the ineffective software project management. Success or failure of any project can be attributed incorrect handling of one or more project variables: people, proper technology, proper project scheduling and selection. Among these attributes proper project selection is one of the most vital part of software project management. There exist many uncertainties in project management and current software engineering techniques are unable to eliminate them. So there is huge scope for developing. The current researchers have developed a unique model which is capable to take decision on the field of software project selection. This model has two embedded sub models namely fuzzy AHP(Analytic Hierarchy Process) and strategic game model. Here in the first case experts opinions are considered under fuzzy environment and in the second case, different decisions makers act as players in the game module. Different criteria are taken into consideration for choosing optimal strategy of the players. An elaborated case study is also analyzed for testing the output of the system.

An introduction towards automated parameterization reduction of soft set

In this paper, we introduce seven correlated algorithms for reduction of decision making parameters. This reduction framework is heuristic function based. The reduction of soft set decision parameters, those are collectively sufficient and individually necessary for keeping any special characteristic of a given information system. There has been very few works done on the algorithmic reduction techniques of decision parameter of soft set To the best of our knowledge, the computing complexities of available methods are of exponential order. We claim that our method is the first such kind of algorithms having polynomial time of computation. Illustrative example demonstrates the process elaborately.

Introduction to soft-set theoretic solution of project selection problem

Purpose The purpose of this paper is to introduce a model of decision-making problem in multi-criteria optimization domain for project selection. The model is built by combining the soft set theory and analytic hierarchical model under fuzziness. Soft set model gives us the opportunity to use parameterization properties. Here, the authors have proved that multiple alternatives can be reduced to make the selection process computationally efficient. Here, the authors illustrate the hybrid method by means of an application of the new mathematical model of soft set theory. Design/methodology/approach This paper is designed to excel a decision support system with multiple criteria analysis tool, analytic hierarchy process combined with soft set theory under fuzziness. Findings In this paper, the authors have taken four projects P1, P2, P3 and P4. As per chosen parameters of softest theory the result of the illustrative example reveals that P2 is the best project. The ranking the authors get is in the order of P2, P3, P4 and P1. The algorithm leads the authors to maximize the proper choice in the environment of imprecise information. The main advantage of this method compare to others is that this hybrid method is very simple in terms of calculation and the computational complexity of the proposed algorithm is low. Originality/value This proposed decision support strategy for an intended project manager helped to take decision in the perspective environment.

A computational geometry based cell migration technique for VLSI placement problem

The movement of preplaced cells among a prescribed existing placement to solve a set of worst designed placement related facts - for example, routing problems, timings, integrity of signals and distribution of heat. To solve this design related problems one has to prefer to translate the design. The translation should be as small as possible while retaining the originality of the placement integrity. The current paper proposes a new algorithmic treatment of the above problem through computational geometry. Our theoretical proposals established through experimental proofs.

A Novel Soft Theoretic AHP Model for Project Management in Multi-criteria Decision Making Problem

The present paper introduces a model of decision-making problem in multicriteria optimization domain for project management. The model is built by combining the fuzzy soft set theory and analytical hierarchical model. The later is the well-known method of ranking the alternatives for multicriteria decision-making problem. The ultimate project selection is the best of many decisions associated with project management. Here we illustrate the hybrid method by means of an application of the new mathematical model of soft set theory.

An ε- Optimal Strategy of Algorithmic Game Theory for Project Selection under Fuzziness

Software project success or failure depends on the ineffective software project management. Success or failure of any project can be attributed by incorrect handling of one or more project variables such as people, proper technology, proper project scheduling and selection. Among these attributes proper project selection is one of the most vital part of software project management. There exist many uncertainties in project management and current software engineering techniques are unable to eliminate them. So there is huge scope for developing. The current researchers have developed a unique model which is capable to take decision on the field of software project selection. This model has two embedded sub models namely fuzzy AHP (Analytic Hierarchy Process) and strategic game model. Here in the first case experts opinions are considered under fuzzy environment and in the second case, different decisions makers act as players in the game module. Different criteria are taken into consideration for choosing optimal strategy of the players. An elaborated case study is also analyzed for testing the output of the system.