Selin Soner Kara

Long term supplier selection using a combined fuzzy MCDM approach: A case study for a telecommunication company

With the globalization and the emergence of the extended enterprise of interdependent organizations, there has been a steady increase in the outsourcing of parts and services. This has led firms to give more importance to the purchasing function and its associated decisions. Since these decisions require a long term investment for the telecommunication industry especially and affect the strategic positioning of the companies in the sector, the selection of the proper supplier is one of the most important problems. Supplier selection is a multi-criteria problem which includes both tangible and intangible factors. This paper develops a supplier evaluation approach based on the analytic network process (ANP) and the technique for order performance by similarity to ideal solution (TOPSIS) methods to help a telecommunication company in the GSM sector in Turkey under the fuzzy environment where the vagueness and subjectivity are handled with linguistic terms parameterized by triangular fuzzy numbers. Contrary to conventional Fuzzy ANP (FANP) methodology in the literature, we use triangular fuzzy numbers in all pairwise comparison matrices in the FANP. Hence, criteria weights are calculated as the triangular fuzzy numbers and then these fuzzy criteria weights are inserted to the fuzzy TOPSIS methodology to rank the alternatives. This approach is demonstrated with a real world case study involving six main evaluation criteria that the company has determined to choose the most appropriate supplier. The study was followed by the sensitivity analyses of the results.

A combined fuzzy MCDM approach for selecting shopping center site: An example from Istanbul, Turkey

The aim of this study is to model shopping center site selection problem for a real world application in Istanbul which is the most populated city in Turkey. Since Turkish metropolitan cities have been attracting a large population from smaller cities and rural areas, it has caused a considerable increase in the population of the big cities of Turkey. The growth in population and the enormous shift of people from old to newly developed districts in Istanbul also create new spending demand areas. This is the most powerful motivation to generate new sites in the city for locating attractive shopping centers. A number of conflicting qualitative and quantitative criteria exist for evaluating alternative sites. Qualitative criteria are often accompanied by ambiguities and vagueness. This makes fuzzy logic a more natural approach to this kind of multi criteria decision making (MCDM) problems. The paper proposes a combined MCDM methodology. Fuzzy AHP (analytic hierarchy process) is utilized for assigning weights of the criteria for site selection and fuzzy TOPSIS (technique for order preference by similarity to ideal solution) is used to determine the most suitable alternative using these criteria weights. The study was followed by the sensitivity analysis of the results.

A hybrid fuzzy MCDM approach to machine tool selection

The selection of the appropriate machine tools for a manufacturing company is one of the important points to achieving high competitiveness in the market. Besides, an appropriate choice of machine tools is very important as it helps to realize full production quickly. Today’s market offers many more choices for machine tool alternatives. There are also many factors one should consider as part of the appropriate machine tool selection process, including productivity, flexibility, compatibility, safety, cost, etc. Consequently evaluation procedures involve several objectives and it is often necessary to compromise among possibly conflicting tangible and intangible factors. For these reasons, multiple criteria decision making (MCDM) has been found to be a useful approach to solve this kind of problem. Most of the MCDM models are basically mathematical and ignore qualitative and often subjective considerations. The use of fuzzy set theory allows incorporating qualitative and partially known information into the decision model. This paper describes a fuzzy technique for order preference by similarity to ideal solution (TOPSIS) based methodology for evaluation and selection of vertical CNC machining centers for a manufacturing company in Istanbul, Turkey. The criteria weights are calculated by using the fuzzy AHP (analytical hierarchy process).

A two-stage stochastic and robust programming approach to strategic planning of a reverse supply network: The case of paper recycling

Paper is an example of a valuable material that can be recycled and recovered. In this study, a two-stage stochastic revenue-maximization model is presented to determine a long-term strategy under uncertainty for a large-scale real-world paper recycling company. This network-design problem includes optimal recycling center locations and optimal flow amounts between the nodes in the multi-facility environment. The proposed model is formulated with two-stage stochastic mixed-integer and robust programming approaches. The models are solved by commercial software GAMS 21.6/CPLEX 9.0 and the results are compared. The study is followed by the analyses of the results.

Supplier selection with an integrated methodology in unknown environment

This paper proposes an integrated methodology and its solution for supplier selection problem. A two-stage stochastic programming model and fuzzy TOPSIS methods are consolidated in this methodology. After pre-research phase, in the second part of the methodology, fuzzy TOPSIS is used for ranking potential suppliers considering qualitative data under fuzzy environment. In the third part of the methodology a group of ranked potential suppliers are included in a two-stage stochastic programming model for evaluation. In the two-stage stochastic programming model demand is assumed as uncertain and different scenarios are generated for this parameter. With this methodology, supplier evaluation procedure can be done in unknown environment. Our methodology is interested with a problem in multi-product, multi-period and multi-sourcing environment.

A distribution network optimization problem for third party logistics service providers

Third party logistics service providers (3PLs) have an important role in supply chain management. Increasing cooperation with 3PLs is expanding in todays business environment. Hence, 3PLs need to have an efficient distribution network to meet customer demands. Nevertheless, few researches have tried to propose a solution for distribution network problems of 3PLs. The optimization problem which is discussing in our study is solved in two stages. At the first stage, the assignment problem which includes assigning the order of the vehicles is solved with mixed integer programming by using GAMS 21.6/CPLEX. The output of the first stage is used as an input in the second stage. In this stage routes are determined for vehicles by developing a genetic algorithm by using C#.

An Extensive Group Decision Methodology for Alliance Partner Selection Problem in Collaborative Networked Organisations

More and more companies address collaborations and cooperation as strategic topics to build competitive networks and broaden the global competences provided by the community thus created. These companies can cooperate with partners to share resources, competences, risks, or costs. Besides, penetrating new markets can be easier when associating new partners. The selection process of an adequate partner considered for a specific objective is a key success factor. This paper proposes an evaluation methodology for selecting an alliance partner in the case of a network of enterprises, manufacturing high precision mechanical components. An extensive group decision methodology is developed using both quantitative data and qualitative judgments in the evaluation of criteria. Since some performances cannot be represented with crisp numbers, the proposed methodology allows experts to use linguistic variables to express their judgments for the assessment of qualitative criteria. Two main phases are integrated in this methodology. In the first phase, criteria and experts weightings are calculated to determine the criteria importance using fuzzy the analytical hierarchy process. In the second phase, a technique for order preference by similarity to ideal solution is proposed to rank the different alternatives, corresponding to the alternative partnerships.

Passenger Flow Prediction Based on Newly Adopted Algorithms

ABSTRACT Passenger flow forecasting is an essential part of transportation systems. Neural networks in the transportation field have been applied to passenger demand prediction. In this paper, we developed two hybrid methods, known as parlimentary optimization algorithm-artificial neural network (POA-ANN), and intelligent water drops algorithm-ANN (IWD algorithm-ANN). In addition, we applied the proposed algorithms to illustrate the effect of precise prediction for passenger queues. We mainly focus on predicting passenger demand by comparing the genetic algorithm-ANN (GA-ANN) with POA-ANN and IWD-ANN. The results of prediction methods suggest that both POA-ANN and IWD-ANN provide a better forecasting performance, which is obtained via mean square error (MSE), than GA-ANN in the field of passenger flow prediction. This study illustrates that the newly adopted algorithms exhibit good performance for passenger prediction.

Determination of Effective Parameters for Coal Moisture Content Determination Using a ‘Design of Experiment’ Method

ABSTRACT Drying of low rank coal is a very significant topic due to its high economic value. One of the most important parameter in the designing of a dryer is the exiting moisture content of the material at the specified conditions and time. Aim of this study was to utilize the design of experiment methodology to determine the most effective parameters on the moisture content of the dried coal. The investigated parameters were dryer bed height, coal particle size, inlet drying air velocity, inlet drying air temperature, inlet drying air relative humidity, exit drying air relative humidity, and time. The results showed that bed height and time were the most effective parameters, and inlet air relative humidity and inlet drying air velocity were less effective parameters. Additionally, particle size and inlet drying air temperature parameters were also significant. To attain more accurate results in the prediction of the exiting coal moisture content, only more influential parameters may be used. Therefore, the design of experiment methodology was a significant way of determining the effective parameters in the coal drying process.

Simulation-based fleet scheduling in the Metrobus

A successful fleet scheduling in any public transport system has a prominence. Metrobus is a bus rapid transit (BRT) system in Istanbul and has a high usage percentage among the public transport. The aim of this study is to establish a successful fleet scheduling by using simulation in the Metrobus. In addition, it aims to meet the satisfaction of passengers. Metrobus system is regarded as a discrete system in the study. The distributions of the passengers, which are boarding and disembarking the system, are determined with regard to stochastic process and a discrete-event model is constructed to determine at which bus-stop one boards and disembarks from the Metrobus. A simulation experiment is presented with the results by applying several different scenarios. At least 12.7% decrease in queue for Zincirlikuyu direction and at least 9.9% decrease in queue for Avclar direction is achieved in the developed simulation model. The results prove that simulation-based fleet scheduling can be applied for the Metrobus successfully.