Gülfem Isiklar Alptekin

A hybrid MCDM methodology for ERP selection problem with interacting criteria

An enterprise resource planning (ERP) system is an information system to plan and integrate all of an enterprises subsystems including purchase, production, sales and finance. Adopting such a comprehensive framework may result in the great savings in both costs and man hours. This research explores the application of a hybrid multicriteria decision making (MCDM) procedure for the evaluation of various ERP alternatives. The proposed evaluation framework integrates three methodologies: Analytic Network Process (ANP), Choquet integral (CI) and Measuring Attractiveness by a Categorical Based Evaluation Technique (MACBETH). ANP produces the priorities of alternatives with respect to the interdependent evaluation criteria. The conjunctive or disjunctive behaviors between criteria are determined using MACBETH and CI. Numerical application of the proposed methodology is implemented on the decision making problem of a firm that faces with four ERP projects. The final ranking is compared to the one obtained by ignoring the interactions among criteria. The results demonstrate that the ignorance of the interactions may lead to erroneous decisions.

An integrated case-based reasoning and MCDM system for Web based tourism destination planning

The accelerating interaction between technology and tourism has changed radically the efficiency and effectiveness of tourism organizations, as well as how consumers interact with organizations. In this study, a Web based intelligent framework for travel agencies is proposed that offers customers a fast and reliable response service in a less costly manner. The proposed framework integrates case-based reasoning (CBR) system with a well-known multi criteria decision making (MCDM) technique, namely Analytic Hierarchy Process, to enhance the accuracy and speed in case matching in tourism destination planning. The integration of two techniques enables taking advantages of their strengths and complements each others weaknesses. A case study is performed to demonstrate how this framework can facilitate intelligent decision support by retrieving best-fitted responses for customers.

Beyond CPU: Considering memory power consumption of software

ICTs (Information and Communication Technologies) are responsible around 2% of worldwide greenhouse gas emissions (Gartner, 2007). And according to the Intergovernmental Panel on Climate Change (IPPC) recent reports, CO2 emissions due to ICTs are increasing widely. For this reason, many works tried to propose various tools to estimate the energy consumption due to software in order to reduce carbon footprint. However, these studies, in the majority of cases, takes into account only the CPU and neglects all others components. Whereas, the trend towards high-density packaging and raised memory involve a great increased of power consumption caused by memory and maybe memory can become the largest power consumer in servers. In this paper, we model and then estimate the power consumed by CPU and memory due to the execution of a software. Thus, we perform several experiments in order to observe the behavior of each component.

Product Improvement by Selecting Appropriate Suppliers: A Case Study

Supplier selection and evaluation is considered as one of the most critical issues for the success of an organization, as its correctness is directly or indirectly related to the cost, quality and the customer satisfaction. This paper presents a methodology for the supplier selection process in respect to changing customer requirements. It is based on a genetic algorithm that is a search heuristic based on biological evolution. We aim at selecting the optimum supplier set that maximizes the quality, performance of delivery time and minimizes costs at the same time. The proposed management methodology is applied on a real world case in the textile chemicals industry. The results of the algorithm show that the proposed methodology could be used as a decision support system for producers with complex product configurations.

Software Greenability: A Case Study of Cloud-Based Business Applications Provisioning

The emission of CO2 due to ICT (Information and Communication Technologies) is found as equivalent to aviation sector. Although the energy consumption in ICT is due both to hardware and software. In recent decade, the energy efficiency studies focus on the hardware part, especially with the development of cloud computing. Indeed, it is the software application that is responsible for executing tasks, that indirectly affects the hardware energy consumption. This work focus on software energy consumption. We introduce a methodology which evaluates and improves the software greenability by considering its energy consumption, performance and readability. Contrary to existing methodologies, we associate the effect of several components (CPU, memory, disk and network) when estimating software energy consumption instead of considering only the CPU as it is the case in literature. Moreover, the proposed framework can dynamically examine source code to integrate some measurement functionality. A Tool to Estimate Energy Consumption (TEEC) is implemented and its applicability is demonstrated on the case of cloud-based business application provisioning software, called MADONA. MADONA allows dynamically generating service-oriented business applications by automatically composing business services and automatically deploying the generated applications on cloud environment.

A Fuzzy Quality Function Deployment Approach for Differentiating Cloud Products

Cloud computing is defined as an on-demand large-scale distributed network to provide and realize computational resources. This flexibility expectation of cloud products forces cloud service providers (CSPs) to tailor their products to the needs of their customers. The framework in this paper proposes a fuzzy logic-based decision support tool for CSPs guiding them in differentiating their cloud offerings for different customer profiles. The tool aims to replicate human reasoning process by making use of fuzzy logic as a computational intelligence framework. The quality function deployment (QFD) approach in the tool systematically handle the product design process. The applicability of the proposed framework is shown using a real life cloud product design scenario with three customer profiles.

Resource Usage Analysis of a Sensor-based Mobile Augmented Reality Application☆

Abstract In this work, design of an Android-based augmented reality application is presented and particularly its performance is analyzed in terms of resource usage in comparison to similar applications. The application displays merchant, branch information of one of the Turkish banks, as well as related sales campaigns of the merchants on the screen that are within the proximity of the users location. The developed application uses GPS, compass, gyroscope, accelerometer sensors and it utilizes an accurate tagging algorithm. We examine the resource and battery consumption of the application. Accordingly, we propose methods for improving the resource usage. The proposed improvements reduce the resource consumptions up to 35% and the application performs considerably well compared to the state of the art commercial applications. We believe that the suggested improvements can be useful for other sensor-based mobile augmented reality applications.

Design of sensor-based augmented reality software (SARAS)

In this study, the design of an augmented reality application running on mobile devices is presented. Given the coordinates of various places of interest, the application places these points and the information associated with them on the device screen, utilizing the sensors available on the mobile devices. In particular, since the project is implemented together with Yapi Kredi Bank, the points presented on the screen are the YKB merchant points. In order to have the application working indoors, such as shopping malls, where the GPS is not operating, an RFID reader add-on is also supported. In this paper, the design of the application and the algorithms and the tools for placing the points on the screen are emphasized.

Strategic pricing model based on genetic algorithm: The case of electronic publishing market

The electronic-book (e-book) is one of the new technological changes that has significantly influenced the publishing industry in the last century. This has forced publishers to reconsider their distribution channels, since the Internet has provided a new means with which to serve readers. In this paper, a strategic market analysis is proposed from the perspective of a traditional publisher that needs to decide whether to switch to e-publishing business. The analysis model determines the publishing market equilibrium in three different market scenarios. Besides, it shows the impact of readers’ choices and price sensitivities on the publishers’ profits. The proposed decision support model has its basis on game theory and it is built in an oligopoly setting to reflect the severe market competition. The readers’ utilities and demands are modeled using the multinomial logit (MNL) model. Although the first scenario possesses a global optimum solution, in the remaining two market scenarios genetic algorithms (GAs) are used in order to find the solutions of the oligopolies. Numerical applications reveal the industry equilibrium point, where the sum of the profits of all publishers in the market is maximum.

An Integrated Approach for Defining Pricing Strategies in Competitive Markets

Quality function deployment (QFD) is a customer-oriented decision support tool with cross-functional team members reaching a consensus in developing a new or improved product to increase customer satisfaction. In this paper, we concentrate on showing the strong effect of QFD methodology on customer satisfaction and on the price determination of a product. The end-users demand to the newly developed or existing products depends not only on the quality level and the price of that producer, but also upon those of its competitors. Our proposed pricing model applies game theory as its mathematical base. The equilibrium point of the game gives the best pricing strategy for the producers. We use the QFD methodology to obtain end-user preferences and quality levels of the competitors. Besides, QFD enables the producer to determine on which product technical requirements to concentrate in order to get more market share.