Gül Tekin Temur

A novel multi attribute decision making approach for location decision under high uncertainty

A novel soft computing approach for location decision under uncertainty is presented.A new notion named as cloud based design optimization (CBDO) which tackles high uncertainty is utilized.It is revealed out that location decision is very sensitive to the consideration of uncertainty.This study contributes to by handling high uncertainties in a location selection by utilizing CBDO as a first time.CBDO can be a helpful supportive tool for decision makers in providing solution under high uncertainty. Location selection is a multi dimensional issue which requires consideration of quantitative and qualitative evaluation criteria. Some of these criteria may have imprecise and uncertain data which make the location selection decision hard to progress. Although many multi attribute decision making (MADM) techniques are utilized in location decision study field, there is a lack of studies which provide solutions by considering high number of supply chain uncertainties. In this study, against the drawbacks of traditional MADM techniques, a novel MADM approach is applied for location decision under high uncertainty as a first time. In the proposed model, a new notion named as cloud based design optimization (CBDO) is utilized because CBDO can take into consideration certain and uncertain factors simultaneously. Furthermore, it provides robust solution within worst case scenario to existing approaches by mediating between aspects of fuzzy set theory and probability distributions. Robustness enables decision makers have managerial and operational foresights about possible unexpected situations, and take necessary actions against risk. An illustrative example is conducted in warehouse location selection problem area to indicate the performance of the proposed approach. It is revealed out that location decision is very sensitive to the consideration of uncertainty and CBDO can be a helpful supportive tool for decision makers in providing solution under high uncertainty.

A fuzzy integrated approach for project selection

Purpose – The purpose of this paper is to develop a systematic and comprehensive project selection model utilizing fuzzy multi-objective linear programming (FMOLP) that deals with the imprecise data in IS projects and uncertain judgment of decision makers. Design/methodology/approach – First, projects are prioritized by considering both quantitative and qualitative factors. A fuzzy analytical hierarchical process (FAHP) is used in order to obtain weights of each project that indicates their priorities. At the second step, project selection decision is completed by using FMOLP. Then, the sensitivity analysis is performed to evaluate the robustness of the proposed integrated model. Findings – The result of this study indicates that an integrated approach utilizing FAHP and FMOLP can be used as a supportive tool for project selection in IS context. It decreases the uncertainty caused from uncertain judgment of decision makers. Research limitations/implications – Future studies are suggested to design models ...

Facility Location Selection in Reverse Logistics Using a Type-2 Fuzzy Decision Aid Method

Location selection for the process of moving goods from their final destination to ensure proper value creation is a multi-faceted issue which requires consideration of social, economic, environmental and technical factors. The fuzzy sets theory is a good tool for dealing with complex and subjective problems which make use of implicit human judgments. Type-2 fuzzy sets provide more degrees of freedom to reflect the uncertainty and the ambiguity of real cases. The aim of this study is to suggest a multi criteria approach for the selection of the most appropriate reverse logistics facility location using a type-2 fuzzy TOPSIS methodology. Using proposed methodology, a case study from an e-waste recycling industry is conducted. In the evaluations, criteria like social acceptability, environmental risks, biodiversity conservation, operation and investment costs, energy and transportation infrastructure, legal/political environment, and growth potentials of the region are considered.

A Study on Performance Measurement of a Plastic Packaging Organization's Manufacturing System by AHP Modeling

By the effect of globalization, products, services, capital, technology, and people began to circulate more freely in the world. As a conclusion, in order to achieve and gain an advantage against competitors, manufacturing firms had to adopt themselves to changing conditions and evaluate their critical performance criteria. In this study, the aim is to determine general performance criteria and their characteristics and classifications from previous studies and evaluate performance criteria for a plastic packaging organization by utilizing analytic hierarchy process (AHP) modeling. A specific manufacturing organization, operating in the Turkish plastic packaging sector has been selected and the manufacturing performance criteria have been determined for that specific organization. Finally, the selected criteria have been assessed according to their relative importance by utilizing AHP approach and expert choice (EC) software program. As a result of this study, operating managers chose cost, quality, customer satisfaction and time factors as criteria for this organization. As the findings of the study indicate, the manufacturing organization operating in the plastic packaging sector, overviews its operations and measures its manufacturing performance basically on those four criteria and their sub criteria. Finally, relative importance of those main measures and their sub criteria are determined in consideration to plastic packaging sector.

A fuzzy expert system design for forecasting return quantity in reverse logistics network

Purpose – The purpose of this study is to develop a fuzzy expert system to design robust forecast of return quantity in order to handle uncertainties from the return process in reverse logistic network. Design/methodology/approach – The most important factors which have impact on return of products are defined. Then the factors which have collinearity with others are eliminated by using dimension redundancy analysis. By training data of selected factors with fuzzy expert system, the return amounts of alternative cities are forecasted. Findings – The performance metrics of the proposed model are found as satisfactory. That means the result of this study indicates that fuzzy expert systems can be used as a supportive tool for forecasting return quantity of alternative areas. Research limitations/implications – In the future, the proposed model can be used for forecasting other uncertain parameters such as return quality and return time. Other fuzzy systems such as type-2 fuzzy sets can be used, or other exp...

Evaluating efforts to build sustainable WEEE reverse logistics network design: comparison of regulatory and non-regulatory approaches

Abstract Many developing countries such as Turkey are still making an effort on building an infrastructure for waste of electrical and electronic equipment (WEEE) reverse logistic network design (RLND) processes. It is obvious that policies/laws/regulations related to WEEE management provide a sustainable framework for implementation in the RLND. The question is here: Does the implementation of WEEE directives make sense in terms of reducing the total cost of the network in the long term? This study aims to compare regulatory and non-regulatory situations of WEEE RLND in developing countries by formulating two models named as ‘regulatory’ and ‘non-regulatory’. Model 1 is considered as sustainable with economic, environmental and social goals, and the quotas imposed by the environmental directive are taken into consideration as the data of product return amount. In Model 2, only economic goal is considered, and product return amount is forecasted using Artificial Neural Network (ANN). A case study is conducted in a recycling company in order to evaluate performance of the proposed models. This study contributes to the relevant literature by (1) comparing the regulatory and non-regulatory situations RL models explicitly and (2) proposing ANN model to forecast EEE product return or WEEE quantity for non-regulatory situation.

Determination of performance criteria for ERP software technology

In this paper, the main goal is to determine the performance criteria for ERP software technology with respect to their sources. Today, the companies are in interaction with many internal and external partners and have to face many situations in various environments. Especially, many small and medium sized firms utilize ERP software technology to plan their business interactions in the chain. In this study, firstly a literature search is accomplished. Then, in order to reveal the performance criteria, a large-scale automotive company has been selected to conduct interviews with managers. Also, the meeting reports of a few small and medium-sized companies have been analyzed. The similar and common agreements on ERP software technology performance criteria were listed and grouped. As a result of the analysis on findings, the common views of the firms about performance criteria are classified into four groups according to their sources such as people, technical, managerial, and economical. The subfactors of these main criteria are also taken into account in order to point out the origins of the criteria. The results of the study will provide insight to researchers and as well as to professionals in the practice.

A Novel Approach for Multi-Period Reverse Logistics Network Design under High Uncertainty

In this paper, a multi-period multi-echelon reverse logistics network design problem under high extent of uncertainty is addressed. We first formulate and then solve the multi-period network design model using the cloudbased design optimization framework which ensures to: (1) handle high number of uncertain factors; (2) propose alternative solution to traditional approaches; (3) provide a robust solution which strengthens decision makers against unexpected situations. Finally, applicability of the presented approach is tested through a dataset of e-waste reverse logistics network.

Energy-related CO2 emission forecast for Turkey and Europe and Eurasia: A discrete grey model approach

Purpose The global warming, caused by the anthropogenic greenhouse gases, has been one of the major worldwide issues over the last decades. Among them, carbon dioxide (CO2) is the most important one and is responsible for more than the two-third of the greenhouse effect. Currently, greenhouse gas emissions and CO2 emissions – the root cause of the global warming – in particular are being examined closely in the fields of science and they also have been put on the agenda of the political leaders. The purpose of this paper is to predict the energy-related CO2 emissions through using different discrete grey models (DGMs) in Turkey and total Europe and Eurasia region. Design/methodology/approach The proposed DGMs will be applied to predict CO2 emissions in Turkey and total Europe and Eurasia region from 2015 to 2030 using data set between 1965 and 2014. In the first stage of the study, DGMs without rolling mechanism (RM) will be used. In the second stage, DGMs with RM are constructed where the length of the rolling horizons of the respected models is optimised. Findings In the first stage, estimated values show that non-homogeneous DGM is the best method to predict Turkey’s energy-related CO2 emissions whereas DGM is the best method to predict the energy-related CO2 emissions for total Europe and Eurasia region. According to the results in the second stage, NDGM with RM (k=26) is the best method for Turkey while optimised DGM with RM (k=4) delivers most reliable estimates for total Europe and Eurasia region. Originality/value This study illustrates the effect of different DGM approaches on the estimation performance for the Turkish energy-related CO2 emission data.

A comparative study for differences on Information System success perceptions: a study of retailing industry

In today’s business environment, the implementation process of Information System (IS) has deep effect on supply chain position o f an organisation. IS projects have multi-dimensional impacts, and they are evaluated regarding implementation success criteria that managers want to achieve. This paper aims to investigate the distinguishing impact of different functional categories on managers’ perceptions on IS Project Implementation Success criteria by comparing two classification models. Within the scope of this study, related literature was examined and then a questionnaire survey was conducted to managers in an organisation from retailing sector to reveal out the importance of the success criteria. Artificial Neural Network (ANN) and Multiple Discriminant Analysis (MDA) have been utilised to seek the effects of different functional department perceptions.