Özgür Kabak

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.

Possibilistic linear-programming approach for supply chain networking decisions

Supply chain networking decisions are very important for the medium- and long-term planning success of manufacturing companies. The inputs to supply chain planning models are subject to environmental and system uncertainties. In this paper, a fuzzy set theory-based model is proposed to deal with those uncertainties. For this purpose, a possibilistic linear programming (PLP) model is used to make strategic resource-planning decisions using fuzzy demand forecasts and fuzzy yield rates as well as other inputs such as costs and capacities. The objective of the proposed PLP is to maximize the total profit of the enterprise. The model is applied to Mercedes-Benz Turk, one of the largest bus-manufacturing companies in the world, and conclusions and suggestions for further research are provided.

Efficient shift scheduling in the retail sector through two-stage optimization

Efficient workforce scheduling has an important impact on store profit and customer service. Standard scheduling problems do not recognize the effect of staff availability on customer sales, however, even though the latter is an important factor in the retail sector. In this paper a two-stage model is proposed for this purpose. In the first stage a sales response model is used to specify hourly staff requirements. The output of the sales response model is then used as the input of a mixed integer optimization model, which finds an optimum assignment of the staff to daily shifts. Simulations are used to validate the sales response function, and to revise the model for more accurate results. In the simulations, customer arrivals and sales response error values are generated using appropriate distribution functions. As a case study the proposed model is applied to a Turkish retailer in the apparel sector.

Applications of Fuzzy Sets in Industrial Engineering: A Topical Classification

Summary: A rational approach toward decision-making should take into account human subjectivity, rather than employing only objective probability measures. This attitude towards the uncertainty of human behavior led to the study of a relatively new decision analysis field: Fuzzy decision-making. Fuzzy systems are suitable for uncertain or approximate reasoning, especially for the system with a mathematical model that is difficult to derive. Fuzzy logic allows decision-making with estimated values under incomplete or uncertain information. A major contribution of fuzzy set theory is its capability of representing vague data. Fuzzy set theory has been used to model systems that are hard to define precisely. As a methodology, fuzzy set theory incorporates imprecision and subjectivity into the model formulation and solution process. Fuzzy set theory represents an attractive tool to aid research in industrial engineering (IE) when the dynamics of the decision environment limit the specification of model objectives, constraints and the precise measurement of model parameters. This chapter provides a survey of the applications of fuzzy set theory in IE.

A Cumulative Belief Degree-Based Approach for Missing Values in Nuclear Safeguards Evaluation

Nuclear safeguards are a set of activities to verify that a State is living up to its international undertakings not to use nuclear programs for nuclear weapons purposes. Nuclear safeguards experts of International Atomic Energy Agency (IAEA) evaluate indicators by benefitting from several sources such as State declarations, on-site inspections, the IAEA databases, and other open sources. The IAEA expert evaluations are aggregated to make a final decision, which usually incomplete because of over 900 indicators, lack of expertise, and unavailability of information sources. In this study, a cumulative belief degree approach is introduced based on the belief structure. It is used to aggregate the incomplete expert evaluations that are represented with fuzzy linguistic terms. Moreover, a reliability index is employed to find the trustworthiness of the final result depending on the available evaluations. A numerical example illustrates the applicability of the proposed methodology.

A comparison study of fuzzy MADM methods in nuclear safeguards evaluation

Nuclear safeguards evaluation (NSE) is to verify that a State is living up to its international undertakings not to use nuclear programs for nuclear weapons purposes. The main issue in NSE is on the aggregation of expert evaluations for numerous indicators to make a final decision about the State’s nuclear activity. Fuzzy multiple attribute decision making (FMADM) methods are capable of dealing with such an issue. In this study, we propose a new FMADM methodology to solve the NSE problem. To this end, we investigate the applicability of four basic FMADM methods, namely a simple additive weighting method, a TOPSIS method, a linguistic method, and a non-compensatory method, to the NSE issue. As a result of the assessment of the basic methods, we propose a new FMADM methodology based on a new aggregation operator in which a cumulative belief structure is used to represent the expert evaluations. The basic methods and the proposed method as well are applied to an example from the literature for illustration purposes.

A fuzzy approach to value and culture assessment and an application

This paper presents a fuzzy approach to the identification of organizational values and culture. The proposed approach has been developed from crisp assessment methods in the literature and has been applied to the Industrial Engineering Department (IED) at a state university in Turkey. Highly subjective judgments and ambiguity regarding the presence of values and the culture type of the organization resulting from these values suggest the necessity of using a fuzzy approach. Where the uncertainty arises from the inability to perform adequate measurements, fuzzy sets provide a mathematical method of representing such uncertainties. Applying the fuzzy approach, organizational values which are common, and should be common in the IED, are identified and these values are organized into four generic culture types - adhocracy culture, market culture, clan culture and hierarchy culture - stating in which culture type the IED belongs. Finally the uncertainties of the culture sets are quantified by the measure of fuzzy entropy.

A decision support methodology to enhance the competitiveness of the Turkish automotive industry

Three levels of competitiveness affect the success of business enterprises in a globally competitive environment: the competitiveness of the company, the competitiveness of the industry in which the company operates and the competitiveness of the country where the business is located. This study analyses the competitiveness of the automotive industry in association with the national competitiveness perspective using a methodology based on Bayesian Causal Networks. First, we structure the competitiveness problem of the automotive industry through a synthesis of expert knowledge in the light of the World Economic Forum’s competitiveness indicators. Second, we model the relationships among the variables identified in the problem structuring stage and analyse these relationships using a Bayesian Causal Network. Third, we develop policy suggestions under various scenarios to enhance the national competitive advantages of the automotive industry. We present an analysis of the Turkish automotive industry as a case study. It is possible to generalise the policy suggestions developed for the case of Turkish automotive industry to the automotive industries in other developing countries where country and industry competitiveness levels are similar to those of Turkey.

Fuzzy Inference Systems for Disaster Response

Disaster management is extremely important in today’s world, which is defined as the organization and management of resources and responsibilities for dealing with all humanitarian aspects of emergencies, in particular preparedness, response and recovery in order to lessen the impact of disasters. Disaster response is one of the critical stages of disaster management, which necessitates spontaneous decision making when a disaster occurs. Fuzzy inference systems are very suitable for such decision making environments since the inputs and outputs of disaster events cannot be sharply defined. This chapter describes potential applications of fuzzy inference systems in disaster response.

An ordered weighted averaging operator-based cumulative belief degree approach for energy policy evaluation

In the context of a long-term for sustainable energy policy development, a set of interlinked decisions produces a process as a strategy. An ordered weighted averaging operator-based cumulative belief degree approach is proposed for a case-study of energy policy evaluation. The approach is realised in a developed software tool called advanced uncertain information processing tool (AdUnIT) for the case study. The tool can be flexibly used to solve other related policy evaluation problems.