Orhan Feyzioğlu

Group decision making to better respond customer needs in software development

Quality Function Deployment (QFD) is a well-known planning methodology for translating customer needs into relevant design and production requirements. The intent of applying QFD is to incorporate the voice of the customer into the various phases of the product development cycle for a new product, or a new version of an existing product. The traditional QFD structure requires individuals to express their preferences in a restricted scale without exceptions. In practice, people contributing to the process tend generally to give information about their personal preferences in many different ways, numerically or linguistically, depending on their background. Moreover, collaborative decision-making is not an emphasized issue in QFD even though it requires several peoples involvement. In this study, we extend the QFD methodology by introducing a new group decision making approach that takes into account multiple preference formats and fusing different expressions into one uniform group decision by means of fuzzy set theory. An application on software development is supplied to illustrate the approach.

Optimum component test plans for phased-mission systems

We consider the component testing problem of a system that has to perform a mission consisting of a sequence of stages. Once a stage is over, all failed components of the system are replaced before the next stage starts in order to improve its reliability. The components have exponential life distributions where the failure rates depend on the stage of the mission. We formulate the optimal component testing problem as a semi-infinite linear program. We present an algorithmic procedure to compute optimal test times based on the column generation technique and illustrate with numerical examples.

Modeling collaboration formation with a game theory approach

The revenue of two partners from a collaborative product development (CPD) project is modeled.Nash Bargaining solution is applied to define optimum strategies.The effects of different parameters on the optimum solution are analyzed.Trust is a major concern in CPD projects, and it indicates that no collaboration is preferable with the lack of trust.Learning is a value adding dimension in the model. While collaborative product development (CPD) is adopted by more and more firms as a business strategy, there is still lack of thorough research on the conditions under which the collaboration is formed. This paper proposes a mathematical model integrating trust, coordination, co-learning, and co-innovation dimensions of CPD. These dimensions, as well as additional parameters such as knowledge investment, absorption capability, efficacy, and complementarity enable the observation of the collaboration behavior under various scenarios. An analysis is conducted with Nash Bargaining approach to investigate the effect of various parameters on the collaboration formation as well as the revenue sharing. The analysis summary presents the optimum strategies for each scenario.

Fuzzy Multi-Criteria Evaluation of Knowledge Management Tools

In the knowledge economy, a key source of sustainable competitive advantage relies on the way to create, share, and utilize knowledge. Knowledge Management (KM) tools assumed an important role in supporting KM activities. The objective of this paper is to aid decision makers to identify the most appropriate KM tool to improve the effectiveness of their organization. In order to rate competing systems of different vendors, we propose an enhanced multi-criteria method, namely fuzzy VIKOR, that takes advantages of fuzzy logic and group decision making to deal with the vagueness and granularity in the linguistic assessments. The method aims to isolate compromise solutions, by providing a maximum group utility and a minimum of an individual regret. A case study is also given to demonstrate the potential of the methodology.

Component testing of a series system in a random mission

Abstract Components of a series system are tested in order to assure desired levels of system reliability during the mission. The components are nonidentical but they all fail exponentially with failure rates that depend on the mission performed. There is a given set of missions that the device can be assigned randomly with respect to a given probability distribution. This directly implies that the failure rates of the components depend on the specific mission that the device performs. The objective is to find an optimal component test plan. We will show that, with some extra effort, this rather complicated but realistic model can be handled using available results in semi-infinite linear programming and d.c. (difference of convex functions) programming.

Mission-Based Component Testing for Series Systems

We consider the component testing problem of a device that is designed to perform a mission consisting of a random sequence of phases with random durations. Testing is done at the component level to attain desired levels of mission reliability at minimum cost. The components fail exponentially where the failure rate depends on the phase of the mission. The reliability structure of the device involves a series connection of nonidentical components with different failure characteristics. The optimal component testing problem is formulated as a semi-infinite linear program. We present an algorithmic procedure to compute optimal test times based on the column generation technique, and illustrate it with numerical examples.

Supply chain risk analysis with fuzzy cognitive maps

To achieve continuous customer satisfaction and sustain competency, a company must identify, evaluate, rank, and manage its supply chain risks. These risk factors and system components are linked in a complicated manner via direct and indirect relationships. This study suggests a systematic way of analyzing supply chain risks using a cognitive map (CM) approach. CMs have proven particularly useful for solving problems in which a number of decision variables and uncontrollable variables are causally interrelated.

The design of optimum component test plans for system reliability

It is often necessary to conduct individual component tests for the prediction and verification of system reliability. Moreover, system testing can be economically infeasible or even impossible. The question then arises as to how component test plans should be designed so as to minimize test costs. Acceptance procedures based on the sum of failures during component tests have been previously suggested in the literature only for a series system, parallel system, and a serial connection of redundant subsystems. This line of research is extended by considering serial connection of standby redundant and k-out-of-n subsystems. It is shown that the serial connection of the mixture of all these systems can also be modelled. The optimal component testing problem is formulated as a semi-infinite linear program, and a procedure to compute optimum component test times is developed. The solution procedure is based on the well known cutting plane idea and column generation technique. Numerical examples are also provided.

A global optimization method for solving fuzzy relation equations

A system of fuzzy relation equations can be reformulated as a global optimization problem. The optimum solution of this new model corresponds to a solution of the system of fuzzy relation equations whenever the solution set of the system is nonempty. Moreover, even if the solution set of the fuzzy relation equations is empty, a solution to the global optimization problem provides a point such that the difference between the right and the left hand side of the fuzzy relation equations is minimized. The new global optimization problem has a nonconvex and nondifferentiable objective function. Therefore, a recent stochastic search approach is applied to solve this new model. The performance of the approach is tested on a set of problems with different dimensions.

Evaluation of hospital web services using intuitionistic fuzzy AHP and intuitionistic fuzzy VIKOR

Professionals in the healthcare industry are constantly pressured to ensure that their provided services are patient-focused. Considering that the healthcare industry seeks continuous performance improvement, well-designed web services can benefit healthcare institutions by improving their reputation and recognition. This paper provides a new perspective for web service performance of healthcare institutions with different quality evaluation criteria for ranking their web services. The proposed framework is based on an integrated multiple criteria decision making (MCDM) methodology that makes use of the intuitionistic fuzzy analytic hierarchy process (IF AHP) and intuitionistic fuzzy Višekriterijumsko kompromisno rangiranje Resenje (IF VIKOR). The methodology presented in this study displays a framework which can be used for better explaining the complicated aspects of the execution of healthcare web services. The proposed approach is applied on a case study to measure the performance of the web based services of ten different healthcare institutions in Turkey.