İhsan Kaya

A fuzzy multicriteria methodology for selection among energy alternatives

Since the correct energy policy affects economic development and environment, the most appropriate energy policy selection is excessively important. Recently some studies have concentrated on selecting the best energy policy and determining the best energy alternatives. In most of these studies, multicriteria and fuzzy approaches to energy policy making are frequently used. The fuzzy set theory is a powerful tool to treat the uncertainty in case of incomplete or vague information. In this paper, a fuzzy multicriteria decision-making methodology is suggested for the selection among energy policies. The methodology is based on the analytic hierarchy process (AHP) under fuzziness. It allows the evaluation scores from experts to be linguistic expressions, crisp or fuzzy numbers. In the application of the proposed methodology, the best energy policy is determined for Turkey.

Information systems outsourcing decisions using a group decision-making approach

Outsourcing refers to a company that contracts with another company to provide services that might otherwise be performed by in-house employees. Information system (IS) outsourcing policies define the criteria that organizations utilize to decide upon the scope and degree of reliance of their IS capabilities upon external sources. IS outsourcing is an innovative organizational tool for IS management in both private and public sector organizations. In this paper, an interactive group decision-making methodology is proposed to select/rank IS providers under multiple criteria. A measure for the consensus level of the group preferences is developed to satisfy an acceptable level of group agreement and reliability. The Spearman coefficients for both the aggregated rank order and each DMs rank order have also been calculated. The group and the individual evaluations are gathered through a fuzzy TOPSIS approach. The proposed methodology is applied in the largest office furniture manufacturer in Konya-Turkey. Eight alternative IS providers are evaluated based on seven criteria by five decision makers. Sensitivity analyses are also provided to see the effects of parameter changes on the final decision.

Selection among ERP outsourcing alternatives using a fuzzy multi-criteria decision making methodology

Outsourcing can be seen as a strategic way to align technology initiatives and business goals, as a strategy for managing technology operations in todays difficult business environment, and as a way to reduce operating costs. Often, companies begin the process by outsourcing non-core business operations, which may include applications, assets, people and other resources. The outsourcing decision is important since the correct selection can dramatically increase a firms performance. When companies outsource a significant part of their business and become more dependent on outsourcers, the consequences of poor decision making becomes more severe. Since enterprise resource planning (ERP) is one of the vital systems, if not the one, that integrates all functions including planning, manufacturing, distribution, and accounting into a single system, its outsourcing is a very important multi-attribute decision problem for the firms. In the literature, outsourcing decisions are often based on multi-criteria approaches. In this paper, a fuzzy multi-criteria decision making methodology is suggested for the selection among ERP outsourcing alternatives. The methodology is based on the analytic hierarchy process (AHP) under fuzziness. It allows decision-makers to express their evaluations in linguistic expressions, crisp or fuzzy numbers. In the application of the proposed methodology, an automotive firm selects the best alternative among three ERP outsourcing firms.

AN APPLICATION OF EFFECTIVE GENETIC ALGORITHMS FOR SOLVING HYBRID FLOW SHOP SCHEDULING PROBLEMS

This paper addresses the Hybrid Flow Shop (HFS) scheduling problems to minimize the makespan value. In recent years, much attention is given to heuristic and search techniques. Genetic algorithms (GAs) are also known as efficient heuristic and search techniques. This paper proposes an efficient genetic algorithm for hybrid flow shop scheduling problems. The proposed algorithm is tested by Carlier and Nerons (2000) benchmark problem from the literature. The computational results indicate that the proposed efficient genetic algorithm approach is effective in terms of reduced total completion time or makespan (Cmax) for HFS problems.

A genetic algorithm approach to determine the sample size for attribute control charts

Determining the sample size for control charts (CCs) is generally an important problem in the literature. In this paper, Kaya and Engins [I. Kaya, O. Engin, A new approach to define sample size at attributes control chart in multistage processes: an application in engine piston manufacturing process, Journal of Materials Processing Technology 183 (2007) 38-48] model based on minimum cost and maximum acceptance probability to determine the sample size for attribute control charts (ACCs), and solved by genetic algorithms (GAs) with linear binary representation structure, is handled to solve it by a linear real-valued representation. A new chromosome structure is also suggested to increase the efficiency of GAs. The performance of GAs depends on mutation and crossover operators, and their ratios. To determine the most appropriate operators, five different mutation and crossover operators are used and they are compared with each other. An application in a motor engine factory is illustrated. u-Control charts are constructed with respect to the sample size determined by GA in the model. The piston production stages in this factory are monitorized using the obtained control charts.

A fuzzy approach to define sample size for attributes control chart in multistage processes: An application in engine valve manufacturing process

Control charts are a basic means for monitoring the quality characteristics of processes to ensure the required quality level. Determine the sample size is a problem for attribute control charts (ACC). Kaya and Engin [I. Kaya, O. Engin, A new approach to define sample size at attributes control chart in multistage processes: an application in engine piston manufacturing process, J. Mater. Process. Technol. 183 (2007) 38-48] developed a model to determine sample size in multistage process and it was solved by Genetic Algorithms (GAs). In their model, the parameters such as defective item rates for raw materials and benches were assumed to be known exactly. But in many real world applications, these parameters may be changed very dynamically due to material, human factors or operating faults. In this study a fuzzy approach for ACC in multistage process is presented and it is solved by GAs. Formulations of this model are calculated based on acceptance sampling approach and, two main parameters are determined for every stage by GAs. These are: sample size, n, and acceptance number, c. The sample size, n, is suggested for ACC. The main contributions of this paper are to develop a fuzzy model for ACC in multistage processes. The proposed approach is applied in an engine valve manufacturing firm and the model is solved by GAs.

Investment project evaluation by a decision making methodology based on type-2 fuzzy sets

An evaluation model is required to transfer public resources to projects.It is necessary to consider many criteria for evaluation of an investment project.The fuzzy sets provide huge facilities to decision makers in project evaluation.To address ambiguities and relativities conveniently, type-2 fuzzy sets are used. Although investment projects supported by the state are extremely important in terms of national policy the projects to be transferred from the common public funds brings with it many problems. Highly transparent and comprehensive evaluation model are required to transfer the public resources to the right investment projects. It is necessary to consider many criteria for the evaluation of an investment project. These criteria are generally subjective and extremely difficult to express in numbers. However, using the fuzzy sets provide huge facilities to decision makers in project evaluation process with linguistic variables and measurement challenges. In this study, a new evaluation model for investment projects have been proposed for development agencies operating in Turkey. To address ambiguities and relativities in real world scenarios more conveniently, type-2 fuzzy sets and crisp sets have been simultaneously used. The proposed model for the investment project evaluation problem composed of type-2 fuzzy AHP and type-2 fuzzy TOPSIS methods. The proposed fuzzy MCDM method consists of three phases: (1) identify the criteria to be used in the model, (2) type-2 fuzzy AHP computations, (3) evaluation of investment projects with type-2 fuzzy TOPSIS and determination of the final rank. To perceive proposed model better, an application with real case data have been performed in Middle Black Sea Development Agency in Turkey. As a consequence of this application, it has been observed that the proposed model have proved effective in evaluation of alternatives in multi-criteria group decision making problems in a broader perspective and flexible fashion.

Evaluation of renewable energy alternatives using MACBETH and fuzzy AHP multicriteria methods: the case of Turkey

Energy is a critical foundation for economic growth and social progress. It is estimated that 70% of the world energy consumption could be provided from renewable resources by the year 2050. Renewable energy is the inevitable choice for sustainable economic growth, for the harmonious coexistence of human and environment as well as for the sustainable development. The aim of this paper is to evaluate the renewable energy alternatives as a key way for resolving the Turkeys energy-related challenges because of the fact that Turkeys energy consumption has risen dramatically over the past three decades as a consequence of economic and social development. In order to realize this aim, we comparatively use MACBETH and AHP-based multicriteria methods for the evaluation of renewable energy alternatives under fuzziness. We use 4 main attributes and 15 sub-attributes in the evaluation. The potential renewable energy alternatives in Turkey are determined as Solar, Wind, Hydropower, and Geothermal.

Fuzzy process accuracy index to evaluate risk assessment of drought effects in Turkey.

ABSTRACT Risk assessment provides a systematic procedure for predicting potential risks to human health or the environment. In the literature, many techniques have been used for risk assessment. In this article, process capability indices are used for this aim. The fullness rates of the dams in Istanbul are analyzed for the harmful effects of the drought by the help of process capability indices. Additionally, the process accuracy index (C a ), which measures the degree of the process centering and gives alerts when the process mean departures from the target value, is used for risk assessment. Its distinctive feature is used to determine the mean of the fullness rates departures from the target value. The results are analyzed to improve precautions. The C a index is also analyzed when the critical parameters are defined as linguistic terms. The specification limits and mean are defined by triangular and trapezoidal fuzzy numbers, then the fuzzy set theory is applied to obtain fuzzy process capability indices. The proposed methodology is applied to measure droughts effects by analyzing the fullness rates of the dams in Istanbul.

RETRACTED: A genetic algorithm approach to determine the sample size for control charts with variables and attributes

Generally today’s production systems consist of multistage processes. Control of these processes is a very important to meet customer’s and engineering’s specifications. Generally control charts which are used to monitor the process and process capability analysis (PCA) which is a summary statistic to show the process performance are used to determine whether or not the process is in statistical control and meet specifications. Although control charts have been applied in very large area of process control, determining the sample size for control charts is generally a problem. In the literature many techniques to solve this problem have been executed. Kaya and Engin [Kaya, _ I. & Engin, O. 2007. A new approach to define sample size at attributes control chart in multistage processes: An application in engine piston manufacturing process. Journal of Materials Processing Technology, 183, 38–48] also proposed a model based on minimum cost and maximum acceptance probability to determine the sample size in Attribute Control Charts (ACCs). In this paper, this model is solved by Genetic Algorithms (GAs) with linear real-valued representation and a new chromosome structure is suggested to increase the efficiency of GAs. The performance of GAs is affected by mutation and crossover operators and their ratios. To determine the most appropriate operators, five different mutation and crossover operators are used and they are compared with each other. For this purpose a computer program is coded by MS Visual Basic and it has been run to determine the suitable operators and ratios, respectively. One of the results of the model is sample size, n, and it is suggested to set up ACCs and Variable Control Charts (VCCs). Also the sample size, n, is used to PCA. To show the usage of the proposed model an application from a motor engine factory is illustrated. For quality characteristics cannot be easily represented in numerical form, ‘‘u-control charts” and for characteristics measurable on numerical scales, ‘‘ x R control charts” are constructed for every stage by taking into account the sample size, n, determined by GAs from the proposed model. These control charts are used to determine whether or not the every stage is in statistical control. Then PCA has been executed for every stage and capability ratios are determined. 2008 Elsevier Ltd. All rights reserved.