Fethullah Gocer

An Intuitionistic Fuzzy MCDM Approach for Effective Hazardous Waste Management

Improperly handled hazardous waste can pose significant threat to human health and the environment. Safety of waste transportation is an important aspect of hazardous waste management . This makes the evaluation and selection of the most suitable hazardous waste transportation firm (HWTF) a crucial problem for hazardous waste generators. A number of factors affect the selection of the proper HWTF, which need to be addressed with usually vague and imprecise data provided by a team of experts from different backgrounds. This chapter proposes a systematic and integrated multi criteria decision making (MCDM) approach to support the HWTF selection process. This approach is based on the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method in an intuitionistic fuzzy (IF) environment. A Group Decision Making (GDM) approach is utilized by the means of IF to minimize the bias stemming from experts’ fuzzy evaluations and to better manage the associated uncertainties and partiality. A real case application from Turkey is presented to illustrate the use of the proposed methodology.

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.

Cloud computing technology selection based on interval-valued intuitionistic fuzzy MCDM methods

Cloud computing technology (CCT) can bring considerable advantages to businesses and people by taking over many time-wasting technicalities, such as establishing communications infrastructure, thus adding value. CCT is revolutionizing every industry by allowing all types of organizations to buy a service instead of owning with flexible rates. An objective of this study is to identify a set of relevant decision criteria and their subcriteria needed in the evaluation of the CCT provider selection (PS) problem. Another objective is to provide a powerful integrated framework that can be used for the evaluation and selection of the most appropriate CCT provider and then to apply this proposed approach in a real case study. CCT PS is a complex multi-criteria decision problem that involves a number of qualitative and quantitative parameters that might be conflicting, or even ambiguous. Interval-valued intuitionistic fuzzy (IVIF) set is an effective concept that is used to cope with uncertainty by taking both the degree of membership and non-membership functions in an interval. Therefore, this paper proposes a state-of-the-art novel methodology consisting of IVIF analytic hierarchy process, IVIF complex proportional assessment, IVIF multi-objective optimization by ratio analysis plus the full multiplicative form, IVIF technique for order of preference by similarity to ideal solution and IVIF višekriterijumsko kompromisno rangiranje methods. This combination aims to rank available CCT PS alternatives in the presence of imprecise and vague information.

Digital Supply Chain: Literature review and a proposed framework for future research

Abstract Suppliers, partners, companies and dealers in supply chains do use, generate and share information with others. These associations lead to a multitude of challenges and opportunities within the supply chains. A Digital Supply Chain (DSC) is a smart, value-driven, efficient process to generate new forms of revenue and business value for organizations and to leverage new approaches with novel technological and analytical methods DSC is not about whether goods and services are digital or physical, it is about the way how supply chain processes are managed with a wide variety of innovative technologies, e.g. unmanned aerial vehicles, cloud computing, and internet of things, among others. Recent literature highlights the importance of DSC and many industrial researchers discuss its applications. This article reviews the state-of-the-art of existing DSC literature in detail from both academic and industrial points of view. It identifies key limitations and prospects in DSC, summarizes prior research and identifies knowledge gaps by providing advantages, weaknesses and limitations of individual methods The article also aims at providing a development framework as a roadmap for future research and practice.

An extension of ARAS methodology under Interval Valued Intuitionistic Fuzzy environment for Digital Supply Chain

Abstract Technology, such as personal computers and mobile devices, alters the foundation of how people communicate and interact with their surrounding environment. Emerging technologies affect every industry and management of supply chain and logistics are no exception. Given that Digital Supply Chain (DSC) provides several opportunities to companies the topic has received increasing attention from both academia and industry in the recent years. Today, an emerging worldwide trend in supply chain management is a shift of the focus from classical supply chains towards DSC, which leverages new solutions to generate new sources of revenue and additional business value for organizations. Recognizing these potential benefits, today’s modern organizations now interact with their dealers through DSC processes for improved efficiency and effectiveness in their production and delivery operations. Supplier selection is one of the crucial processes that has been affected by the digitalization of supply chain management. Addressing multi-criteria nature of the supplier selection process, this article introduces a new DSC approach for the first time to support the supplier selection process. This approach develops a group decision making (GDM) method in an uncertain environment. This methodology also considers decision makers when they are under pressure and/or lack expertise during the evaluation process. The proposed framework integrates for the first time the Interval Valued Intuitionistic Fuzzy (IVIF) Sets, Analytic Hierarchy Process (AHP) and Additive Ratio Assessment (ARAS) under a GDM environment. IVIF AHP is used to evaluate criteria weights and IVIF ARAS methodology is used for the alternative assessment procedure. The paper also included the analyses for the selection of a suitable supplier in a real case study from Turkey to illustrate the validity of the proposed novel approach.

An extention of ARAS methodology based on interval valued intuitionistic fuzzy group decision making for digital supply chain

Everyone knows that technology altered profoundly the way we communicate and interact with the world, from personal computers to mobile devices. The impact of emerging new technologies affects every industry, and supply chain or logistics are no exception. Digital Supply Chain (DSC) create added various benefits to companies, financial or otherwise. Today, an emerging trend in supply chain management worldwide is a movement of the focus from that of classical supply chain to that of DSC. Modern organizations therefore shall interact with their dealers through DSC processes for production and delivery operations of goods and services. Due to its multi criteria nature, this study proposes a novel approach to evaluate supplier selection process under DSC environment for group decision making in an uncertain environment. The proposed framework combines for the first time the Interval Valued Intuitionistic Fuzzy (IVIF) Analytic Hierarchy Process (AHP) to evaluate criteria weights and IVIF Additive Ratio Assessment (ARAS) methodology for alternative assessment procedure. The paper also analyzes the selection of a suitable supplier in a real case study from Turkey to demonstrate the validity of the proposed approach.

Cloud Computing Technology Selection Based on Interval Valued Intuitionistic Fuzzy COPRAS

Cloud computing technology provides virtual services based on subscriptions with an associated cost that is accessible to its users from anywhere, wherever they are. Technology brings many different benefits to companies as well as to the public by reducing the time and resources for them which would be needed for establishing and operating their own Information Technology infrastructure. The main aim in this study is to identify significant decision criteria that are relevant to the cloud computing technology selection problem among ‘Infrastructure as a Service’ cloud providers, to provide an effective framework to evaluate and select the most appropriate ‘Infrastructure as a Service’ providers and also to apply the proposed approach through an empirical study. Technology selection essentially is a difficult multi-criteria problem that deals with both quantitative and qualitative parameters, which are usually conflicting and uncertain. Interval valued intuitionistic fuzzy set is a powerful method to cope with uncertainty by taking both degree of membership and non-membership function in an interval. A multi-criteria approach based on the combination of interval valued intuitionistic fuzzy set theory and complex proportional assessment is proposed to deal with cloud computing technology selection problem in uncertain and ambiguous environment. Finally, in order to illustrate the procedure thoroughly, an application of the proposed approach is considered.

Smart Medical Device Selection Based on Interval Valued Intuitionistic Fuzzy VIKOR

Advances in wireless communication technologies and the internet of things are leading to new developments in the domain of wearable, smart medical devices (SMDs) as a major disruptive trend for the medical industry. Wearable smart sensor technology with non-invasive or invasive implantable materials has a great potential for interfacing with the human body, thanks to low-power silicon-based electronics that are very efficient in data processing and transmission. Novel SMDs are designed for monitoring living being’s vital signs, such as blood pressure, cardiac monitoring, respiration rate, body temperature, etc. in either medical diagnostic or health monitoring. Considering various smart devices in the medical industry, a key decision is which device to choose and apply on the patient. The decision on the evaluation of SMDs is a complicated problem that needs to be assessed from different perspectives. This study guides decision makers on the selection of SMDs of wearable vital sign sensors under different evaluation criteria. A multi criteria decision making approach is proposed to support the SMD selection process under group decision making (GDM) in an uncertain environment. A significant feature of this analysis is the complexity of the selected decision criteria for the SMD evaluation. To simulate these processes, a methodology that combines interval valued intuitionistic fuzzy (IVIF) with Visekriterijumsko kompromisno rangiranje (VIKOR) under GDM is proposed. This methodology is then used to measure the assessment of four SMDs using five evaluation criteria. To validate the proposed approach, the selection methodology for wearable vital sign monitoring devices is applied on a case study.

Heuristic inventory policies for a hybrid manufacturing/remanufacturing system with product substitution

Abstract We consider inventory control for an infinite-horizon hybrid manufacturing/remanufacturing system with product substitution under stochastic demand and returns. Remanufactured and manufactured products are considered as different products having different costs and selling prices as well as separate demand streams. Remanufactured products have a higher stock out risk than manufactured products because the remanufacturing capacity is dependent on available returns for remanufacture. One way to cope with this stock-out risk is to use a downward substitution strategy, under which a manufactured product (i.e. higher value item) substitutes for a remanufactured product (i.e. lower value item) in case the latter runs out of stock. This problem can be formulated as a Markov Decision Process in order to determine the optimal manufacturing and remanufacturing decisions under product substitution. However, the optimal policy has a complicated structure. Based on characterization of the optimal policies, we propose intuitive heuristic policies that are easy to implement in practice. Then, we develop a heuristic search technique to determine the parameter values for these policies in an efficient way. We evaluate the performance of the proposed heuristic policies compared to the optimal inventory policy through a real case study involving a spare part manufacturer.

Analysis of Downward Product Substitution in a Recoverable System

We consider the inventory control problem for an infinite-horizon stochastic hybrid manufacturing /remanufacturing system with product substitution under stochastic demand and returns. Remanufactured and manufactured products are considered as two different products, having different costs and selling prices as well as separate demand streams. Remanufactured products have a higher stock out risk because the remanufacturing capacity is mainly dependent on the amount of returns available for remanufacture. One way to cope with the stock-out issue for remanufactured products is to use a downward substitution strategy, which allows a manufactured product (i.e. higher value item) to be substituted for a remanufactured product (i.e. lower value item) in case the latter runs out of stock. We formulate this problem as Markov Decision Process in order to determine the optimal manufacturing and remanufacturing decisions under product substitution, and through numerical experimentation, we investigate the effects of stochastic demand/return distributions on the profitability of the substitution strategy.