Semih Önüt

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 fuzzy analytic network process based approach to transportation-mode selection between Turkey and Germany: A case study

A case study examining the different modes for transportation of freight by a Turkish logistics-service provider company is presented herein. A number of conflicting qualitative and quantitative criteria exist for evaluating alternative modes of transport. Qualitative criteria are often accompanied by ambiguity and vagueness. To cope with ambiguity and vagueness problem, the fuzzy analytic network process (ANP) method has been used. A large number of detailed criteria that interact with each other have been evaluated and synthesized to obtain the most suitable transportation mode. This evaluation has been carried out by a group of decision makers coming from different management levels and functional areas in the sector of logistics and from the service company with intent to provide a more accurate and mutually acceptable solution. Furthermore, the model used here has been validated by comparing the results obtained with the current preferences of the company.

A decision support system for demand forecasting with artificial neural networks and neuro-fuzzy models: A comparative analysis

An organization has to make the right decisions in time depending on demand information to enhance the commercial competitive advantage in a constantly fluctuating business environment. Therefore, estimating the demand quantity for the next period most likely appears to be crucial. This work presents a comparative forecasting methodology regarding to uncertain customer demands in a multi-level supply chain (SC) structure via neural techniques. The objective of the paper is to propose a new forecasting mechanism which is modeled by artificial intelligence approaches including the comparison of both artificial neural networks and adaptive network-based fuzzy inference system techniques to manage the fuzzy demand with incomplete information. The effectiveness of the proposed approach to the demand forecasting issue is demonstrated using real-world data from a company which is active in durable consumer goods industry in Istanbul, Turkey.

A particle swarm optimization algorithm for the multiple-level warehouse layout design problem

Warehouse operation and management is one of the essential parts of manufacturing and service operations. The warehouse layout problem is a key to warehouse operations. Generally, warehouse layout design models attempt to optimize different objectives such as the orientation of storage racks, the allocation of space among competing uses, the number of cranes, the overall configuration of the facility, etc. The warehousing strategies can be classified as distribution-type, production-type and contract-type warehouse strategies. In this study, a distribution-type warehouse considered that various type products are collected from different suppliers for storing in the warehouse for a determined period and for delivery to different customers. The aim of the study is to design a multiple-level warehouse shelf configuration which minimizes the annual carrying costs. The turnover rates of the products are classified and they are considered while putting/picking them to/from shelves regarding the distances between the shelves and docks. Since proposed mathematical model was shown to be NP-hard, a particle swarm optimization algorithm (PSO) as a novel heuristic was developed for determining the optimal layout.

A two-phase possibilistic linear programming methodology for multi-objective supplier evaluation and order allocation problems

In this study, an integration of the analytic hierarchy process (AHP) and a multi-objective possibilistic linear programming (MOPLP) technique is developed to account for all tangible, intangible, quantitative, and qualitative factors which are used to evaluate and select suppliers and to define the optimum order quantities assigned to each. A multi-objective linear programming technique is first employed to solve the problem. To model the uncertainties encountered in the integrated supplier evaluation and order allocation methodology, fuzzy theory is adopted. Hence, possibilistic linear programming (PLP) is proposed for solving the problem, as it is believed to be the best approach for absorbing the imprecise nature of the real world. In the supplier evaluation phase, environmental criteria are also considered.

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.

A stochastic optimization approach for paper recycling reverse logistics network design under uncertainty

One of the most important objectives of a manufacturing firm is the efficient design and operation of its supply chain to maximize profit. Paper is an example of a valuable material that can be recycled and recovered. Uncertainty is one of the characteristics of the real world. The methods that cope with uncertainty help researchers get realistic results. In this study, a two-stage stochastic programing model is proposed to determine a long term strategy including optimal facility locations and optimal flow amounts for large scale reverse supply chain network design problem under uncertainty. This network design problem includes optimal recycling and collection center locations and optimal flow amounts between the nodes in the multi-facility environment. Proposed model is suitable for recycling/ manufacturing type of systems in reverse supply chain. All deterministic, stochastic models are mixed-integer programing models and are solved by commercial software GAMS 21.6/CPLEX 9.0.

A theorical model design for ERP software selection process under the constraints of cost and quality: A fuzzy approach

Enterprise Resource Planning (ERP) software selection is one of the most important decision making issues covering both qualitative and quantitative factors for organizations. Multiple criteria decision making (MCDM) has been found to be a useful approach to analyze these conflicting factors. Qualitative criteria are often accompanied by ambiguities and vagueness. This makes fuzzy logic a more natural approach to this kind of problems. This study presents a beneficial structure to the managers for use in ERP software vendor selection process. In order to evaluate ERP vendors methodologically, a hierarchical framework is also proposed. As a MCDM tool, we used analytic hierarchy process (AHP) and its fuzzy extension to obtain more decisive judgments by prioritizing criteria and assigning weights to the alternatives. The objective of this paper is to select the most appropriate alternative that meets the customer’s requirements with respect to cost and quality constraints. In the end of this study, a real-world case study from Turkey is also presented to illustrate efficiency of the methodology and its applicability inpractice.