Hayri Baraçlı

An integrated decision support system dealing with qualitative and quantitative objectives for enterprise software selection

Previous methods for enterprise software selection generally take into account the attributes that are restricted to some financial factors, such as costs and benefits. However, the literature lacks studies on considering the evaluation of both functional and non-functional suitability of software alternatives versus various requirements. This study presents a new decision support system for combining these two kinds of evaluation to select suitable enterprise software. A hierarchical objective structure that contains both qualitative and quantitative objectives is proposed to evaluate software products systematically. This approach uses a heuristic algorithm, a fuzzy multi-criteria decision making procedure and a multiobjective programming model to make final selection decision. All the phases of presented method are applied in an electronic companys ERP software selection project to validate it with a real application. The satisfactory results are obtained during this project. The company can select the right software to fit its business processes instead of adapting its business processes to fit the software.

A framework for defining both qualitative and quantitative supplier selection criteria considering the buyer–supplier integration strategies

Previous research has reported that the decision criteria used to evaluate suppliers differ by product category. This paper presents a framework for defining the supplier selection criteria by investigating possible quantitative and qualitative criteria reported by earlier studies according to the levels of buyer–supplier relationship, the companys competitive situation and its corporate strategies, rather than investigating these criteria in respect of product category. The solution proposed in this study not only assists decision makers to define selection criteria, but also supports determining the buyer–supplier integration level. An actual case in Audio Electronics, a Turkish electronics business, demonstrates the feasibility of applying the proposed framework in practice.

Fuzzy quality function deployment based methodology for acquiring enterprise software selection requirements

In many software acquisition methods, functional software requirements are resolved, but non-functional requirements are more or less deliberately put aside. A large body of research exists on the necessity of handling specific non-functional requirements as major drivers in the software development process. However, prior research does not provide adequate support for managing non-functional requirements in the software selection process, and suggests a unique technique and methodology for identifying the selection criteria. This paper presents a fuzzy quality function deployment approach for determining which of the non-functional requirements reported by earlier studies are important to a companys software selection decision based on and integrated with its functional requirements. The solution provided in this study not only assists decision makers in acquiring software requirements and defining selection criteria, but also supports determining the relative importance of these criteria. An actual case in Audio Electronics of Turkeys electronic industry demonstrates the feasibility of applying the proposed framework in practice.

A weakness determination and analysis model for business process improvement

Purpose – Modeling of the methods for providing improvements in business processes by value adding is researched with an integrated approach. The main purpose in this approach is improving the processes by determining and analyzing the weak points and reducing the weakness degrees.Design/methodology/approach – The designed model determines weak points that need to be improved, analyzes them to find privileged processes in improvement by considering improvement costs and obtains the improvement degrees for defining the improvement strategy by four‐phase business process improvement framework: start‐up, self analysis, defining improvement strategy for making changes, feedback, and continuous improvement.Findings – The key factor in keeping up with changes in market conditions is systematic application of improvement efforts and providing planned and controlled value addition with these improvements. Decision problems in process improvement can be structured to provide input data suitable for multi‐criteria ...

A fuzzy multi-criteria decision making methodology based upon the interval Type-2 fuzzy sets for evaluating renewable energy alternatives in Turkey

AbstractEnergy is substantial for social development and has an essential role for the survival and development of human as an environmental factor. Due to the serious increase ofthe worlds primary energy price and rapid development of Turkeys economy, energy consumption of Turkey has become a major problem. For that purpose, opinions of the decision makers upon the relative importance of selection criteria are determined by using a fuzzy AHP procedure based upon type-2 fuzzy sets, and fuzzy multi-criteria decision-making based upon the interval type-2 TOPSIS method is used to rank the best renewable alternative energy. In this paper, a fuzzy multi criteria decision making (MCDM) methodology was proposed to determine the best renewable energy alternatives for Turkey, because type-2 fuzzy sets provide additional degrees of independence to represent the uncertainty and the fuzziness of real world applications. The results showed that wind was the best alternative for Turkeys energy investments, as being ...

A hybrid fuzzy methodology to evaluate customer satisfaction in a public transportation system for Istanbul

Service quality (SERVQUAL) is one of the most important factors that increase the usage of public transportation. SERVQUAL improvements can solve many problems such as traffic congestion, air and noise pollution, and energy consumption in any public transportation system (PTS). In this paper, a hybrid fuzzy approach consisting of SERVQUAL, the Delphi method, fuzzy analytic hierarchy process (AHP) and fuzzy technique for order preference by similarity to ideal solution (TOPSIS) methodologies for the evaluation of SERVQUAL of PTS is presented. The SERVQUAL method is used for classifying the public transportation SERVQUAL evaluation criteria, the Delphi method is used to obtain the evaluations of experts, fuzzy AHP is used to determine the weights of the criteria and fuzzy TOPSIS is used to rank the alternatives. A new linguistic scale is taken into consideration while converting the linguistic evaluations of survey respondents to fuzzy numbers. The proposed approach is applied for the PTS in Istanbul, and the public transportation company that provides the highest customer satisfaction is determined. With this aim, four alternative companies named Istanbul Electric Tramway and Tunnel (IETT), Metrobus, Private Public Transportation Buses and Otobus Inc. are evaluated.

Garage Location Selection for Public Transportation System in Istanbul: An Integrated Fuzzy AHP and Fuzzy Axiomatic Design Based Approach

We try to determine the best location for a bus garage, in which maintenance and repair activities are operated, for public transportation system in Istanbul. An integrated multicriteria decision making technique (MCDM) is used to obtain reliable results. Firstly, various criteria related to garage location selection are specified and weighted by fuzzy AHP (analytical hierarchy process). Then, these weights are used in fuzzy axiomatic design (AD) technique to determine the precedencies of the alternative garage locations.

Modeling Potential Future Energy Demand for Turkey in 2034 by Using an Integrated Fuzzy Methodology

Since the decisions made regarding the future include uncertainty for operations, alternative predictions are needed to be developed in such decision-making processes. Accurate forecasting is a great help for companies in making the best decisions in terms of unit commitment, production, and maintenance planning. It is necessary for the companies to have prior foresight of future demand with adequate accuracy. Some data mining algorithms play the greatest role in predicting the demand forecasting. As a regular data-driven method, artificial neural networks (ANNs) are popular in energy forecasting. This paper investigated the application of the adaptive network based fuzzy inference system (ANFIS) as a forecasting tool for predicting the energy demand in Turkey. The benefit of the proposed model is forecasting energy needs through the evaluation of ANFIS application using data sets processed with principle component analysis (PCA) and collected from the energy forecast shootout (EFS) and Ministry of Natural Resources Turkey. The results showed that the hybrid ANFIS model based upon fuzzy logic (FL) and ANN performed efficiently in term of forecast accuracy. Thus, it could be regarded as an alternative method in energy forecasting. Finally, the application of ANFIS in a long term energy forecasting was provided, and the results were interpreted.