No full-text available
To read the full-text of this research,
you can request a copy directly from the authors.
Investigation of the pharmaceutical warehouse locations under COVID-19—A case study for Duzce, Turkey
Abstract
Pharmaceutical warehouses are among the centers that play a critical role in the delivery of medicines from the producers to the consumers. Especially with the new drugs and vaccines added during the pandemic period to the supply chain, the importance of the regions they are located in has increased critically. Since the selection of pharmaceutical warehouse location is a strategic decision, it should be handled in detail and a comprehensive analysis should be made for the location selection process. Considering all these, in this study, a real-case application by taking the problem of selecting the best location for a pharmaceutical warehouse is carried out for a city that can be seen as critical in drug distribution in Turkey. For this aim, two effective multi-criteria decision-making (MCDM) methodologies, namely Analytic Hierarchy Process (AHP) and Evaluation based on Distance from Average Solution (EDAS), are integrated under spherical fuzzy environment to reflect fuzziness and indeterminacy better in the decision-making process and the pharmaceutical warehouse location selection problem is discussed by the proposed fuzzy integrated methodology for the first time. Finally, the best region is found for the pharmaceutical warehouse and the results are discussed under the determined criteria. A detailed robustness analysis is also conducted to measure the validity, sensibility and effectiveness of the proposed methodology. With this study, it can be claimed that literature has initiated to be revealed for the pharmaceutical warehouse location problem and a guide has been put forward for those who are willing to study this area.
... Erdoganand Ayyildiz [14] suggested pharmaceutical warehouses were critical for delivering medicines, particularly during a pandemic. Choosing the optimum site for a pharmaceutical warehouse requires careful consideration. ...
... The proposed solution involves the introduction of a mathematical model and algorithm that optimize the location of rescue facilities and the zoning of territories to manage evacuation traffic effectively. The study [9] investigates the best location for a pharmaceutical warehouse in one of the major Turkish cities using the analytic hierarchy process and EDAS method, which evaluate locations around an average solution within a spherical fuzzy environment. This unique approach is applied for the first time to this problem. ...
У роботі досліджується система медичної логістики під час кризових ситуацій, що спричиняють надмірне навантаження. Авторами сформульовано практичну постановку задачі для структури медичної логістики, яка включає три типи центрів: регіональні, субрегіональні та дистрибуційні. Також було запропоновано математичну модель для двоетапної задачі розміщення-активації, що мінімізує витрати на експлуатацію, доставку, розміщення та активацію відповідних центрів. Для цієї математичної моделі було представлено два підходи до розв'язання. Перший підхід розділяє процес на два етапи: спочатку визначаються місця розташування дистрибуційних центрів шляхом розв'язання неперервної задачі, а потім використовуються отримані координати для розв'язання другої (дискретної) частини. Другий підхід є комбінованою методикою, яка поєднує як неперервну, так і дискретну частини, використовуючи всю доступну інформацію під час виконання алгоритму із застосуванням еволюційної теорії. Обидва підходи можуть бути використані для розв'язання задачі: перший є простішим і має меншу обчислювальну складність, а другий використовує більше інформації, але має вищу обчислювальну складність.
... Employing diverse validation analyses is a favorable approach to evidencing the robustness of an MCDM technique. These analyses offer insights into parameter and approach variations on the solution (Erdogan and Ayyildiz, 2022). For this purpose, a twophase validation analysis is employed. ...
Purpose
Blood availability is critical for saving lives in various healthcare services. Ensuring blood availability can only be achieved through efficient management of the blood supply chain (BSC). A key component of the BSC is bloodmobiles, which are responsible for a significant portion of blood donation collections. The most crucial factor affecting the efficacy of bloodmobiles is their location selection. Therefore, detailed decision analyses are essential for the location selection of bloodmobiles. This study proposes a comprehensive approach to bloodmobile location selection for resilient BSCs.
Design/methodology/approach
This study provides a novel integration of the spherical fuzzy analytical hierarchy process (SF-AHP) and spherical fuzzy complex proportional assessment (SF-COPRAS) methodologies. In this framework, the criteria are weighted using SF-AHP. The alternatives are then evaluated using SF-COPRAS, employing criteria weights obtained from SF-AHP without defuzzification.
Findings
The results show that supply conditions and resilience are the most important criteria for a bloodmobile location selection. Additionally, the validation analyses confirm the stability of the solution.
Practical implications
This study presents several managerial implications that can aid mid-level managers in the BSC during the decision-making process for bloodmobile location selection. The critical factors revealed, along with their importance in choosing bloodmobile locations, serve as a comprehensive guide. Additionally, the framework proposed in this study offers decision-makers (DMs) an effective method for ranking potential bloodmobile locations.
Originality/value
This study presents the first application of multi-criteria decision-making (MCDM) for bloodmobile location selection. In this manner, several aspects of bloodmobile location selection are considered for the first time in the existing literature. Furthermore, from the methodological aspect, this study provides a novel SF-AHP-integrated SF-COPRAS methodology.
... Moreover, other uncertainty-handling techniques, such as fuzzy programming (Mohammed et al., 2019), stochastic optimal control (Temoçin and Weber, 2014;Yılmaz et al., 2015Yılmaz et al., , 2020Bakan et al., 2018;Savku and Weber, 2018), and grey systems (Parkouhi et al., 2019;Roy et al., 2017), can be employed to study the uncertainty of the problem and be compared with the robust optimization method. Other MADM techniques, such as DEMATEL (Erdogan and Ayyildiz, 2022) and COPRAS (Schitea et al., 2019), can be compared with our suggested approach. From a management perspective, the possibility of producing national vaccines can be considered in the model, and the behavior of the proposed decision support framework can be evaluated accordingly. ...
Designing resilient supply chain networks for vaccine development and distribution requires reliable and robust infrastructure. This stud develops a novel two-stage decision support framework for configuring multi-echelon Supply Chain Networks (SCNs), resilient supplier selection, and order allocation under uncertainty. Resilient supplier selection is done using a hybrid Multi-Criteria Decision-Making (MCDM) approach based on Best-Worst Method (BWM), Weighted Aggregated Sum Product Assessment (WASPAS), and Type-2 Neutrosophic Fuzzy Numbers (T2NN). A robust multi-objective optimization model is then built for order allocation considering resiliency scores, reliability of facilities, and uncertain supply and demand. The objectives are to minimize the total cost of SCN design, maximize the resiliency score, and maximize the reliability of SC, respectively. A Non-dominated Sorting Genetic Algorithm II (NSGA-II) is developed to tackle the problem on large scales, tuned by the Taguchi design technique. The NSGA-II solution is compared to the ε-constraint and Multi-objective Particle Swarm Optimization (MOPSO) solutions using test problems. We demonstrate the superiority of the suggested NSGA-II method over the two competing methods according to five performance metrics. A case study is then investigated to illustrate the applicability and effectiveness of the offered methodology for COVID-19 vaccine distribution in a developing country. It is revealed that the models and algorithms can treat the problem optimally, such that Germany is the main source (approximately 25.61%) while India does not contribute to the supply of vaccines.
... Following the appearance of soft expert sets, different researchers have been attracted by this powerful idea and introduced many new hybrid models for the purpose of group decision-making (see Al-Qudah and Hassan, 2017;Arockiarani and Lancy, 2013;Hassan and Alhazaymeh, 2013 as a short sample). For other notions not discussed in the paper, the readers can consult (Bi et al., 2022;Feng et al., 2010;Liang et al., 2022;Erdogan and Ayyildiz, 2022;Akram et al., 2021Akram et al., , 2022bHazarika and Gupta, 2020;Juszczuk and Kruś, 2020;Kahraman et al., 2014Kahraman et al., , 2018Liu et al., 2019;Pegoraro et al., 2020;Peng et al., 2017;Xu et al., 2022;Qayyum and Shaheen, 2020;Zhang et al., 2019a,b;Zou et al., 2021). To better understand the research gaps and developments in the literature, a summary is provided in Table 1. ...
Many scholars have been challenged by multi-attribute group decision-making problems that have stimulated the appearance of increasingly general models. Pythagorean fuzzy sets were a reaction by Yager who in 2013, suggested this model to improve the performance of intuitionistic fuzzy sets. Another hybrid model-soft expert sets-deals with uncertain parameterized information. It considers opinions of different experts, improving the single-agent experience of soft sets.-soft expert sets and their fuzzy version, namely, fuzzy-soft expert sets, consider the ratings given to objects by more than one expert with respect to relevant parameters. The arguments supporting the need for independent allocation of membership and non-membership degrees apply to the fuzzy expressions imposed on top of the benefits of the-soft expert environment. These challenges converge on the formulation of a new hybrid model called Pythagorean fuzzy N-soft expert sets that improves upon Pythagorean fuzzy sets with the benefits of-soft expert sets. We study their scope of application with practical examples. Afterwards we discuss certain basic operators (subsethood, complement, union and intersection), prove some of their remarkable properties, and provide the concepts of equal, agree, and disagree-Pythagorean fuzzy-soft expert sets. We present an algorithm for group decision-making problems in this framework and we explore three applications of this methodology, namely, to the analysis of wheat varieties, employee selection, and recovery order of patients suffering COVID-19. In the end, we provide a sensitivity analysis comparing the proposed model with some existing models to guarantee its cogency and feasibility.
As unidades armazenadoras desempenham um papel estratégico na cadeia de suprimentos de indústrias, atacado, varejo e produção agropecuária, tanto sob viés qualitativo quanto no quantitativo, sendo um dos maiores representantes dos custos logísticos totais. A escolha baseada em critérios de sua localização pode gerar redução de custos, aumento de renda e participação de mercado. O objetivo desta pesquisa foi fazer uma revisão sistemática de literatura (RSL) sobre tomada de decisão de localização de armazéns utilizando o método AHP com o objetivo de levantar o estado da arte, as tendências e lacunas de pesquisa existentes sobre esse tema. Os resultados foram analisados no VosViewer e R/Bibliometrix, classificados e codificados, resultando em uma estrutura analítica de dados, com recomendações, visando orientar pesquisas futuras. Dentre os resultados principais destacam-se a utilização de Delphi para identificação de critérios, as tendências de utilização de metodologias integradas ao AHP, como GP (Goal Programming) e SIG, e a necessidade de aumento de estudos desse tema para a armazenagem agropecuária.
Earth observation (EO) provides dynamic scientific methods for tracking and defining ecological parameters in mountainous regions. Open-source platforms are frequently utilized in this context to efficiently collect and evaluate spatial data. In this study, we used Collect Earth (CE), an open-source land monitoring platform, to reveal and assess land cover, land cover change, and relevant ecological parameters such as drought risk. Mountain ecosystems were subject to an evaluation for the first time by combining remote sensing with a hybridization of Decision-Making Trial and Evaluation Laboratory (DEMATEL), analytic hierarchy process (AHP), and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) for neutrosophic sets in risk assessment problems of several connected criteria. The high and dispersed high alpine environment of Türkiye accommodates land with relatively less human influence, making it suitable to observe climate change impacts. In the framework of the study, we evaluated more than two decades (2000–2022) of land use and land cover (LULC) changes in the mountain regions of the country. Using nine identified ecological parameters, we also evaluated drought risk. The parameters included were the LULC classes and their change, elevation, slope, aspect, precipitation, temperature, normalized difference vegetation index (NDVI), water deficit, and evapotranspiration (ET). The risk map we produced revealed a high to very high drought risk for almost throughout the Türkiye’s mountainous areas. We concluded that integrating geospatial techniques with hybridization is promising for mapping drought risk, helping policymakers prepare effective drought mitigation measures to reasonably adapt to climate change impacts.
Earth observation (EO) provides dynamic scientific methods for tracking and defining ecological parameters in mountainous regions. Open-source platforms are frequently utilized in this context to efficiently collect and evaluate spatial data. In this study, we used Collect Earth (CE), an open-source land monitoring platform, to reveal and assess land cover, land cover change, and relevant ecological parameters such as drought risk. Mountain ecosystems were subject to an evaluation for the first-time by combining remote sensing with an hybridization of Decision-Making Trial and Evaluation Laboratory (DEMATEL), Analytic Hierarchy Process (AHP), and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) for Neutrosophic sets in risk assessment problems of several connected criteria. The high and dispersed high alpine environment of Türkiye accommodates land with relatively less human influence, making it suitable to observe climate change impacts. In the framework of the study, we evaluated more than two decades (2000–2022) of Land Use and Land Cover (LULC) changes at the mountains regions of the country. Using nine identified ecological parameters, we also evaluated drought risk. The parameters included were the LULC classes and their change, elevation, slope, aspect, precipitation, temperature, normalized difference vegetation index (NDVI), water deficit, and evapotranspiration (ET). The risk map we produced revealed a high to very high drought risk for almost throughout the Türkiye’s mountainous areas. We concluded that integrating geospatial techniques with hybridization is promising for mapping drought risk, helping policymakers prepare effective drought mitigation measures to reasonably adapt to climate change impacts.
In this study, the overall aerodynamic performance of the airfoils with variable amplitude geometric structure for lift-type vertical axis wind turbine has been analyzed and optimized using Fuzzy Analytic Hierarchy Process (AHP)-Fuzzy COmbinative Distance-based Assessment (CODAS) based on Taguchi Method. In the design of the experiment, leading edge (LE) tubercle airfoil models, Reynolds number, and angle of attack are utilized as the control factors. Drag, lift, and lift to drag ratio performances by comparing the airfoils according to the baseline model are calculated and the combination of these performances is considered as the overall aerodynamic performance response. The experiments are carried out according to the orthogonal matrix L18. Since the importance of each response is different on lift-type vertical axis wind turbines, the importance weights of these responses were obtained by Fuzzy AHP according to the evaluation of the decision maker. Multiple responses are converted into a single response (the overall aerodynamic performance) via Fuzzy CODAS which enables to model uncertainty in order to overcome the physical effects that may arise in experiments. Thereby, a multi objective Taguchi methodology was applied for the overall aerodynamic performance with a novel Fuzzy logic based multi criteria decision analysis approach. As a result, Model-4, having tubercle design parameters of amplitude of 0.025c and 0.0125c and wavelength of 0.5c and 0.125c, was found to have the best performance with the novel Taguchi approach, which saves time and cost. In addition, it has been determined that the airfoils, Reynolds number and angle of attack are significant and what level of contribution to the overall aerodynamic performance. At this point, the most important control factor has emerged as the angle of attack.
Purpose
This study aims to investigate and classify the hospital logistics key performance indicators (KPIs) using the context of public hospitals in remote areas.
Design/methodology/approach
The public hospitals in northern Thailand were selected for the case study. The questionnaire was developed and used to collect data from hospital logistics experts. Then, the analytic hierarchy process (AHP) method was used to evaluate the hospital logistics KPIs in each dimension.
Findings
This research found that the procurement management dimension is ranked highest. Information and technology management is the last rank in the hospital logistics KPIs used for public hospitals in remote areas.
Research limitations/implications
The public hospitals located in northern Thailand were selected for the case study. Fuzzy multi-criteria decision-making methods can be used to reduce the vagueness of the values.
Practical implications
The results from this study can be a guideline for hospitals to improve the efficiency of their logistics operations.
Social implications
The decision-makers in the hospital can use these results to improve the hospital’s logistics performance in the future, which could help increase the service level and the safety of the patients.
Originality/value
The hospital logistics KPIs were revised, and the crucial KPIs were prioritized for improving the hospital logistics using the AHP method.
To mitigate losses from public health emergencies, promptly choosing an emergency medical supply distribution center is essential. The success of rescues hinges on the center’s location. Location decision for such centers is fraught with uncertainty and risk, complicating the task for decision makers (DMs). Often, DMs operate under bounded rationality in urgent situations. In response, this paper introduces a novel fuzzy multicriteria decision-making approach, integrating interval-valued intuitionistic fuzzy numbers, interval intuitionistic trapezoidal fuzzy numbers, not-in-my-backyard (NIMBY) effect, and cumulative prospect theory, to address the uncertainty and risk in decision-making. The method begins by introducing the NIMBY effect to construct the evaluation criteria system of emergency medical supply distribution centers. Next, the alternative under the evaluation criteria is evaluated by fuzzy linguistic terms and interval intuitionistic trapezoidal fuzzy numbers. The interval-valued intuitionistic fuzzy numbers are used to determine the weight of each DM by calculating the relative scores of advantage and disadvantage. The criteria weight is determined via a combination of fuzzy network analysis and the entropy weight method. Cumulative prospect theory is then employed to rank site selections, factoring in DM risk preferences. The approach is tested on an emergency medical supply distribution center location for sensitivity and comparative analysis, proving its efficacy and stability. This research presents an innovative fuzzy model for evaluating emergency medical supply distribution center site selection in uncertain conditions, suggesting the need to incorporate multiple dimensions such as realistic criteria systems, uncertain decision information, and variable environments in such decision-making processes.
The amount of information in the Pythagorean fuzzy set is determined by its entropy. Entropy offers a new, versatile tool that is especially helpful in intricate multi-criteria problems where ambiguous data and erroneous information are taken into account. One of the main problems in the research of logistics networks is the optimization of the location of sustainable logistics centres in order to reduce the operational expenses of sustainable logistics distribution centres. A real-world case study that includes a multi-criterion sustainable logistics centre location selection problem serves as an illustration of the performance of the hybrid entropy-TOPSIS approach. The proposed method is defined in a Pythagorean fuzzy environment and uses an integrated entropy measure and TOPSIS to rank the alternatives using defined criteria in the decision-making method to achieve reliable and reasonable results. A relative study is shown to discuss the merits of the method and demonstrate how the suggested method’s computation results are comparable to those of various other modern approaches.
Breast cancer is the leading cause of cancer-related deaths, and choosing a suitable treatment plan for this disease has proved difficult for oncologists owing to the variety of criteria and alternatives that must be considered during the decision-making process. Since prospective treatment options influence patients’ health-related quality of life in a variety of ways, a methodology that can completely and objectively evaluate alternative treatments has become an essential issue. This paper proposes a novel multi-criteria decision-making (MCDM) methodology by integrating the CRiteria Importance Through Intercriteria Correlation (CRITIC) and the REGIME techniques and handles the problem of breast cancer treatment selection problem. CRITIC enables the determination of objective criterion weights based on the decision matrix, while REGIME ranks the options without the need for lengthy computations or normalization procedures. The suggested methodology is demonstrated in a spherical fuzzy atmosphere, which allows decision experts to independently express their degrees of membership, non-membership, and hesitancy in a broad three-dimensional spherical space. In the numerical example provided, three oncologists evaluate four breast cancer treatment alternatives, namely, surgery, radiotherapy, chemotherapy, and hormone therapy, with respect to five criteria, which are disease or tumor type, stage of disease, patient type, side effects, and financial status of the patient. The tumor type is determined to be the most important assessment criterion, and surgery is selected as the best course of action. The stability and validity of the proposed methodology are verified through sensitivity and comparative studies. The discussions, limitations, and future research avenues are also given within the study.
This study mainly aims to develop two effective and practical multi-criteria group decision-making approaches by taking advantage of the ground-breaking theory of PROMETHEE family of outranking methods. The presented variants of Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE) method are acknowledged to address the complex decision-making problems carrying the ambiguous information, expressible in terms of yes, no, abstinence and refusal, owing to the preeminent condition and wider structure of spherical fuzzy sets. Both of the proposed approaches seek help from the Shannon’s entropy formula to evaluate the object weights of the decision criteria. The proposed techniques operate by taking into account the deviation between each pair of potential alternatives in accordance to different types of preference functions to determine the preference indices. The proposed technique of spherical fuzzy PROMETHEE I method carefully compares the positive and negative outranking flows of the alternative to get partial rankings. In contrast, the spherical fuzzy PROMETHEE II method has the edge to eliminate the incomparable pair by employing the net outranking flow to derive the final ranking. The application of proposed approaches is explained via a case study in the field of medical concerning the selection of appropriate site to establish Fangcang shelter hospital in Wuhan to treat COVID-19 patients. The convincing comparisons of the proposed methodologies with q-rung orthopair fuzzy PROMETHEE and spherical fuzzy TOPSIS methods are also included to verify the aptitude of the proposed methodology.
The flue gas desulfurization (FGD) process eliminates Sulphur dioxides from flue gas produced by the combustion of fossil fuels in furnaces, boilers, and other sources. Limestone is an essential element for the FGD process in coal-fired thermal power plants. FGD system contributes efficaciously to the prevention of air pollution through its limestone/gypsum treatment in greater reliability, higher efficiency, and more capability. The selection of the limestone suppliers is thus a crucial task to improve environmental aspects and reduce cost. To optimize the selection of limestone suppliers, appropriate criteria determination and an effective solution approach are the central undertakings, which need to be made scientifically. Addressing the multi-criteria nature of the selection process, this study introduces a new Multi-Criteria-Decision-Making (MCDM) approach to evaluate supplier selection and deliver a group decision making (GDM) method in an ambiguous and fuzzy environment. The proposed method also takes decision makers’ judgments into account in times of pressure, bias, and lack of expertise during the evaluation process. Consequently, this study, for the first time, integrates the Pythagorean Fuzzy Sets (PFSs), Simple Additive Weighting (SAW), and Evaluation Based on Distance from Average Solution (EDAS) under GDM setting. This paper, also for the first time, includes the analyses of an evaluation for the right supplier selection in a thermal power plant. A real case study from Turkey is applied to illustrate the validity of the proposed approach. Sensitivity analysis and comparison of the results with the existing techniques are also presented to verify the significance of the found outcome.
While the circular economy has recently been the subject of considerable theoretical debate, the discussion has yielded limited insight into how its implementation should look. Developing countries’ inadequate regulation and policy hinder the circular economy’s implementation in small- and medium-sized enterprises (SMEs) with resource, strategy, and skill constraints. Therefore, circular economy business models (CEBMs) support SMEs in overcoming the risks of implementation. However, SMEs often struggle to decide which CEBM to use. This study aims to enable developing countries’ SMEs to choose the most appropriate CEBM using the spherical fuzzy TOPSIS method, which is an extremely new method for solving decision-making problems. The four main CEBMs most frequently encountered in the literature and practice have been extensively analysed. The results suggest that the resource recovery model is the most appropriate model for transitioning to a circular economy for SMEs in developing countries. Circular supply is the second suitable business model. Following these stages, after the organisation reaches a specific level of CE maturity, the product life extension and the product–service system model should be applied at the last stage. A comparative assessment and a sensitivity analysis are conducted to test the proposed methodology’s robustness and reliability. The results opened up a space for discussion and for new thoughts that could improve the scope of the CEBMs theory. Using all CEBMs together, we concluded that the transition to CE will not be successful for SMEs. The order in which CEBMs should be applied in the transition to CE has been determined. Their scope, risks, and resources needed were correlated with these data from the field. This practical implementation guide, which we recommend based on theoretical foundations, offers administrators and future researchers original insights.
Logistics processes have been analyzed as one of the most critical expense items for companies. Companies that failed to manage their logistics processes well could not reach the desired growth rates, and some even disappeared. Logistics management without process optimizations can be time and money consuming for the companies. Logistics processes significantly influence organizations' efficiency. Logistic professionals should keep the process flow quality at a certain level, and some decision problems should be analyzed and answered well. Location selection of a warehouse is one of the most crucial decision problems of supply chain and logistics management. Alternatives are evaluated in quantitative and qualitative criteria to decide the best location alternative using scientific MCDM techniques. Different scientific methods have been developed and used to solve this problem. As for this study, the aim is to investigate the warehouse location selection of third-party cold chain logistics suppliers. The importance of keeping perishable products under the right conditions increased, since it is realized that cold chain warehousing and transportation prevent the product spoiled. Organizations can save their funds and time by having effective cold chain management. This study's main scope is to give a new scientific perspective for warehouse selection in cold chain logistics. MCDM technique of Interval-Valued Neutrosophic Fuzzy (IVNF) EDAS method has been used to evaluate essential criteria and choose the best option. Critical cities of Gulf Region countries have been considered alternatives in a numerical illustration.
This paper provides a review of the consensus problem as one of the most challenging issues in the distributed control of the multi-agent systems (MASs). In this survey, firstly, the consensus algorithms for the agents with the single-integrator, double-integrator and high-order dynamic models were collected from various research works, and the convergence condition for each of these algorithms was explained. Secondly, all the consensus-related problems such as those in the sampled-data consensus, quantized consensus, random-network consensus, leader–follower consensus, finite-time consensus, bipartite consensus, group consensus/cluster consensus, and the scaled consensus were analyzed and compared with each other. Thirdly, we focused on the common control techniques used for the consensus problems in the presence of disturbance and divided all these control methods into two categories: robust control and adaptive control. Finally, we reviewed the most prevalent consensus applications in the MASs, including the subjects of rendezvous, formation control, axial alignment and the wireless sensor networks.
Warehouses link suppliers and customers throughout the entire supply chain. The location of the warehouse has a significant impact on the logistics process. Even though all other warehouse activities are successful, if the product dispatched from the warehouse fails to meet the customer needs in time, the company may face with the risk of losing customers. This affects the performance of the whole supply chain therefore the choice of warehouse location is an important decision problem. This problem is a multi-criteria decision-making (MCDM) problem since it involves many criteria and alternatives in the selection process. This study proposes an integrated grey MCDM model including grey preference selection index (GPSI) and grey proximity indexed value (GPIV) to determine the most appropriate warehouse location for a supermarket. This study aims to make three contributions to the literature. PSI and PIV methods combined with grey theory will be introduced for the first time in the literature. In addition, GPSI and GPIV methods will be combined and used to select the best warehouse location. In this study, the performances of five warehouse location alternatives were assessed with twelve criteria. Location 4 is found as the best alternative in GPIV. The GPIV results were compared with other grey MCDM methods, and it was found that GPIV method is reliable. It has been determined from the sensitivity analysis that the change in criteria weights causes a change in the ranking of the locations therefore GPIV method was found to be sensitive to the change in criteria weights.
Many healthcare centres generally have a centralized unit-dose drug distribution system (CUDD). The in-patient’s drugs are warehoused in a centralized location such as hospital pharmacy. The required or prescribed drugs available in the pharmacy will be distributed to the patient by either the nurse or attendant. The attendant often collects the medicine from the pharmacy. This process is quite time consuming and in this COVID'19pandemic should maintain social distancing by the attendant in the pharmacy. In a field where
time is of great importance and can save lives and also, a human intervention which in turn need to maintain social distancing, we can use MeDrones that will dispense medicines to the designated patient(s) location which will avoid human intervention and social distancing to defeat COVID-19 pandemic which is the need of the hour. The drone will be designed to deliver payload (tablets/saline) within/across the hospital premises. Apart from delivering medicines to the respective patients, the drone will be equipped to decide the optimal path to reach patients and prioritize attending to critical patients based on the hospital's central database. The drones can be customized to return the unused medicines from the patient's
location to the pharmacy. Lightweight secured drugs container kit with authentication enabled will be designed for carrying the medicines to distribute to the respective destination inside the healthcare centre. The drone regulations result from increasing demand in UAV that must be devised to permit adaptation extensively as a logistics solution, to escalate efficacy in the healthcare domain.
There is an increase in the number of people who have changed their country for compulsory reasons due to the wars experienced worldwide. This raises the problem of identifying the regions where these people, called refugees, will live in the countries. In this regard, we determine in this paper where the camps established for refugees living in Istanbul should be located. The presence of many quantitative and qualitative factors which should be handled in determining the best location has enabled this problem to have multi-criteria decision-making structure. In addition, the advantage of fuzzy logic is used to convert the evaluations taken from experts into available numbers and to include them in decision-making process. For this purpose, a novel model with the integration of Pythagorean fuzzy AHP and Pythagorean fuzzy WASPAS methods is proposed for the first time in the literature, to select the best location for the refugee camp. In addition, a comparative analysis is applied to determine the validity of the results obtained and the sensitivity analysis is applied to check the robustness of the model. As a result, the most suitable location for a refugee camp in Istanbul is identified reasonably with the proposed model.
Warehouses are crucial infrastructures in supply chains. As a strategic task that would potentially impact various long-term agenda, warehouse location selection becomes an important decision-making process. Due to quantitative and qualitative multiple criteria in selecting alternative warehouse locations, the task becomes a multiple criteria decision-making problem. Current literature offers several approaches to addressing the domain problem. However, the number of factors or criteria considered in the previous works is limited and does not reflect real-life decision-making. In addition, such a problem requires a group decision, with decision-makers having different motivations and value systems. Analysing the varying importance of experts comprising the group would provide insights into how these variations influence the final decision regarding the location. Thus, in this work, we adopted the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to address a warehouse location decision problem under a significant number of decision criteria in a group decision-making environment. To elucidate the proposed approach, a case study in a product distribution firm was carried out. Findings show that decision-makers in this industry emphasise criteria that maintain the distribution networks more efficiently at minimum cost. Results also reveal that varying priorities of the decision-makers have little impact on the group decision, which implies that their degree of knowledge and expertise is comparable to a certain extent. With the efficiency and tractability of the required computations, the TOPSIS method, as demonstrated in this work, provides a useful, practical tool for decision-makers with limited technical computational expertise in addressing the warehouse location problem.
Logistics is an important service sector, contributing to improving the competitiveness of the economy. Therefore, along with increasing the application of technology and effective business models, it is necessary to increase the connectivity of the infrastructure systems of industrial parks, roads, and seaports of regions and the country. Over the past decades, Vietnamese businesses have been step-by-step going through many stages from production, packaging, quality, hygiene, and safety to grasping new stages in the domestic and global value chain. In many industries, businesses are increasing the content of their own designs, exploiting brands, and approaching consumption networks in the target market. The role of the distribution center is becoming more and more important in ensuring a seamless and flawless supply chain. In particular, the distribution center is the most sensitive contact point between supply and demand in each enterprise. Therefore, the key mission of a distribution center is to reconcile supply and demand requirements. Distribution center location selection problems usually involve multiple quantitative and qualitative criteria that the decision maker must take into account for assessing the symmetrical impact of the criteria to reach the most accurate result. In this study, the authors propose a hybrid MCDM model based on Spherical Fuzzy Analytic Hierarchy Process (SF-AHP) and Combined Compromise Solution (CoCoSo) Algorithm to support the distribution location selection problem of perishable agricultural products. The proposed model is then applied to the numerical case study of the sweet potato product of the Mekong Delta region of Vietnam to demonstrate the feasibility of the model. The contribution of this research is to propose an MCDM model for improving the efficiency of the agricultural supply chain through selecting a location distribution center. This proposed model can be applied to the agricultural supply chain around the world.
Environment and social life are open to hazards, because of the distribution, diffusion, and conversion processes of chemicals contained in hazardous materials. These chemicals are very dangerous. Various precautions should be taken into consideration during the displacement of hazardous materials. Therefore, it is important to identify and minimize the risks in the transportation of hazardous material. This work investigates to identify the critical risk factors and their weights for hazardous material transportation operations. The literature is reviewed, critical risk factors for hazardous material transportation are defined, and data from different experts is collected. A two-level hierarchical structure is established to evaluate risk factors. Then, the experts’ evaluations of main and sub-risk factors are consolidated using the modified Delphi method. Weights of main and sub-risk factors are obtained using the Pythagorean fuzzy analytic hierarchy process method. To show the robustness of the proposed decision-making methodology, a sensitivity analysis is conducted.
Failure modes and effects analysis (FMEA) is a commonly used step-by-step approach to assess potential failures existing in a product or process design. In this paper, a modified FMEA model based on an interval-valued spherical fuzzy extension of technique for order preference by similarity to ideal solution (IVSF-TOPSIS) is proposed to cope with drawbacks of the traditional risk priority number (RPN) computation. Spherical fuzzy sets are the integration of Pythagorean fuzzy sets and neutrosophic sets. They provide more freedom to experts in decision making by including the degree of membership, non-membership, and hesitation of fuzzy sets. Therefore, initially, TOPSIS is merged with a special branch of spherical sets “interval-valued spherical fuzzy sets” to determine priorities of emerged failures. As a novelty to traditional RPN of FMEA, three parameters called cost, prevention, and effectiveness in addition to the existed parameters of severity, occurrence and detection are attached to the proposed approach. Weights of these parameters are determined via an interval-valued spherical weighted arithmetic mean operator (IVSWAM). As a demonstration, a case study in a marble manufacturing facility is provided to show the applicability of the novel model. Results show that the most crucial failure modes concern with the maintenance and repairing works of the factory and the lack of technical periodic checks of lifting vehicles regarding “block area: crane” failures. Some comparative and validation studies are also performed to test the solidity of the approach.
Rapid diagnostic testing for COVID-19 is key to guiding social distancing orders and containing emerging disease clusters by contact tracing and isolation. However, communities throughout the US do not yet have adequate access to tests. Pharmacies are already engaged in testing, but there is capacity to greatly increase coverage. Using a facility location optimization model and willingness-to-travel estimates from US National Household Travel Survey data, we find that if COVID-19 testing became available in all US pharmacies, an estimated 94% of the US population would be willing to travel to obtain a test, if warranted. Whereas the largest chain provides high coverage in densely populated states, like Massachusetts, Rhode Island, New Jersey, and Connecticut, independent pharmacies would be required for sufficient coverage in Montana, South Dakota, and Wyoming. If only 1,000 ZIP code areas for pharmacies in the US are selected to provide testing, judicious selection, using our optimization model, provides estimated access to 29 million more people than selecting pharmacies simply based on population density.
To effectively manage large industrial companies, decision-making regarding the location of distribution warehouses are particularly important. Locating distribution warehouses is a multi-criteria problem in which the decision-making process is influenced by several quantitative and qualitative criteria. Among the methods proposed to aid location selection, the UTASTAR method is shown to be suitable for clarifying the decision-making process. This method determines single utility functions for each alternative location considered. This aids in the evaluation and comparison of alternative location options based on the preferences of the decision makers expressed in terms of multiple criteria. UTASTAR results enable managers and decision makers to rank the identified options and thereby justify the most suitable option to select as the preferred warehouse location. This research shows the effective application of the UTASTAR method in a case study relating to the selection by the Damghan Steel Company (Iran), from several alternatives, of the best location to construct its central warehouse. The approach proposed offers a new perspective to the problem of warehouse location selection by calculating single utility functions that meaningfully represent the relative values of alternative warehouse locations from a decision makers perspective.
The location selection of distribution center covers one of the important strategic decision issues for the logistics system managers. In view of the inherent uncertainty and inaccuracy of human decision-making, the future behavior of the market and companies, this paper adopts the improved multi-attribute and multi-Actor decision-making (MAADM) method as a fuzzy multi-attribute and multi-actor decision-making (FMAADM) method for solving the selection problem under an uncertain environment. The great strengths of our proposed method are: first, the integration of the decision-makers group preferences into the decision-making process, second, the consideration of the informations related to the alternatives and the criteria weights which are inaccurate, uncertain or incomplete, third, the verification of the obtained solution by both tests of concordance and non-discordance. To validate the FMAADM method, a decision support system was developed. Different experiments were provided based on comparative analysis of results and the sensitivity analysis. These experiments demonstrate the efficiency of our proposed method and its superiority over another existing methods.
Warehousing as well known, one of the most important parts of logistics. Today, the efficient and safe operation of storage activities are vital for companies. For this reason, companies have to pay attention to many factors when they are going to construct a warehouse. The most important factors are the structure of land, service availability, the geographical location, distance to customs or ports, climate, natural disaster risk, the social structure of the environment, distance to service markets, infrastructure availability (electricity, gas, natural gas), distance to hospitals and pharmacies. The goal of the paper is to evaluate the factors affecting the decision-making process of location selection of a warehouse for medical materials and services. As part of special material handling, medical goods need to be handled much more carefully. That is why, as far as the medical issues concerned additional criteria might be taken into consideration by the decision-makers. The priorities and alternatives will be discussed and ranked in the following part of this research. Besides, basic and supplementary criteria and their interrelation are determined by Pythagorean fuzzy set (PFS) based DEMATEL technique in order to be a guide for the medical executives.
Selecting a proper warehouse location serving to satisfy the demands of the goods from a certain business area is important to a successful retail business. However, the large solution space, uncertain traffic conditions, and varying business preferences impose great challenges on warehouse location selection. Conventional approaches mainly summarize relevant evaluation criteria and compile them into an analysis report to facilitate rapid data absorption but fail to support a comprehensive and joint decision‐making process in warehouse location selection. In this paper, we propose a visual analytics approach to facilitating warehouse location selection. We first visually centralize relevant information of warehouses and adapts a widely‐used methodology to efficiently rank warehouse candidates. We then design a delivering estimation model based on massive logistics trajectories to resolve the uncertainty issue of traffic conditions of warehouses. Based on these techniques, an interactive framework is proposed to generate and explore the candidate warehouses. We conduct a case study and a within‐subject study with baseline systems to assess the efficacy of our system. Experts ‘feedback also suggests that our approach indeed helps them better tackle the problem of finding an ideal warehouse in the field of retail logistics management.
The petrol station location selection problem is taken into consideration in this study. In order to identify the main and sub-criteria for evaluation, the literature is reviewed and five experts from different companies are interviewed. After that, thirteen different alternative locations are for specified to evaluation. Then, the novel spherical fuzzy AHP-integrated spherical WASPAS methodology is structured in a fuzzy environment, and the petrol station location selection problem is evaluated with this methodology. In this study, a real application is presented for Istanbul to show the applicability of the proposed methodology. Subsequently, a sensitivity analysis is performed to explain and analyze the proposed methodology results. Finally, the results are presented and discussed with future directions.
COVID-19 pandemic has affected the entire world. During the Covid-19 pandemic, which is tried to be prevented by all countries of the world, regulations have been made to reduce the effect of the virus in sectors such as banking, tourism, and especially transportation. Social isolation is one of the most critical factors for people who have or are at risk of contracting COVID-19 disease. Many countries have developed different solutions to ensure social isolation. By applying lockdown for specific periods, preventing the movement of people will reduce the rate of transmission. However, some private and public institutions that have to serve during the lockdown period should be carefully determined. In this study, we aim to determine the petrol stations to serve during the COVID-19 lockdown, and this problem is handled as a multi-criteria decision-making problem. We extend the spherical fuzzy VlseKriterijumska Optimizacija IKompromisno Resenje (SF-VIKOR) method with the spherical fuzzy Analytic Hierarchy Process (SF-AHP). To show its applicability in complex decision-making problems, Istanbul is selected to perform a case study; thirteen petrol stations are evaluated as potential serving petrol station alternatives during the lockdown. Then, the novel SF-AHP integrated SF-VIKOR methodology is structured; the problem is solved with this methodology, and the best alternative is determined to serve in lockdown. Accessibility of the petrol station and Measures taken by station managers are determined to be essential for the effectiveness of the lockdown process. The neighborhood population and the station's proximity to hospitals are also critical inner factors to fight the pandemic. To test the methodology, Spherical Fuzzy the Weighted Aggregated Sum-Product Assessment (SF-WASPAS) is utilized. Public or private organizations can use the proposed methodology to improve their strategies and operations to prevent the spreading of COVID-19.
Picture fuzzy sets (PFSs) are one of the most promising extensions of ordinary fuzzy sets with three parameters, namely positive membership, neutral membership, and negative membership, for defining the membership status of an element to a set. CRiteria Importance Through Intercriteria Correlation (CRITIC) & REGIME methods are recently developed multi criteria decision making (MCDM) methods for calculating the criteria weights and ranking alternatives, respectively. CRITIC method determines the criteria weights by using the values in the decision matrix. REGIME method is a compensatory MCDM method employing superiority and guide indices, superiority identifier and impacts, and REGIME matrices. In this paper, an integrated CRITIC & REGIME methodology is developed for the first time by using single-valued PFSs in order to use the advantage of PFSs in handling ambiguity and impreciseness. The main contribution of our study is to demonstrate theoretically and practically how to transform superiority and guide indices, superiority identifier and impacts, and REGIME matrices to the PF environment. A new interval valued Relative Magnitude Index scale and an original Percentile Rank under Vagueness function have been developed. The developed methodology is applied to the selection problem of wearable health technology (WHT). Comparative and sensitivity analyses are presented. These analyses show that CRITIC & REGIME methodology produces very effective and valid results, and unlike the other methods, it shows slight ranking differences due to the statistical-based calculations it contains.
Multi-criteria decision-making (MCDM) methods used in solving real-life problems are very useful tools as they allow the evaluation of many qualitative and quantitative factors simultaneously. An increasing number of new methods and approaches are being introduced into the literature to overcome different MCDM problems with many proportional and contradictory features. Since the assessments and judgments made for the addressed MCDM problems may vary depending on the conditions that arise in the future, most of the current static MCDM methods can lead to ineffective and wrong decisions. Therefore, there is a need to develop flexible decision models that will enable to deal with a dynamic decision system using current and future information in the literature. In addition, as the decision process brings with its uncertainties arising from incomplete information, the use of intuitionistic fuzzy sets (IFSs) in the decision process will provide a more accurate representation of data and better handle uncertainties that may arise in decision problems. In this study, the extension of the CRITIC method to IFSs is first developed, which takes into account the objective weights of the criteria in an uncertain environment for weighting the criteria. Then, it is intended to develop the extension of the DEVADA method to IFSs in order to create a dynamic decision system capable of dealing with uncertainties. In addition, a stronger multi-measurement system is proposed by considering Euclidean and cosine distances together. To better demonstrate the feasibility and efficiency of the method, the waste disposal location selection problem, where the evaluations are open to temporal changes, is discussed. A comprehensive sensitivity analysis is then performed to verify the stability and effectiveness of the method. Besides, a comparative analysis is presented with distance-based MCDM methods showing the superiority and advantages of the developed method.
s
One of the most significant ways of providing supply security in energy and effective utilization of energy sources is energy storage. Hydrogen, which is a crucial energy carrier, has received more attention in recent years due to its importance for a carbon-free economy and society. There are various physical and material-based storage methods for hydrogen storage. When examined in terms of factors that may affect the storage decision, each storage alternative has positive and negative features. In this regard, determining the most suitable hydrogen energy storage alternative by considering economic, technological, environmental, and social factors is a decision-making problem which is needed to be handled carefully to benefit from this technology in the most effective way. Since this problem requires the consideration of many criteria, some of which have dependencies among themselves or conflict with each other, and experts' judgments including uncertainty along with different degrees of reliability, a multi-criteria decision-making (MCDM) approach based on z-fuzzy numbers will be an appropriate tool to make an efficient investment decision. Through that, in this study, a novel combined methodology consisting of Decision-MAking Trial and Evaluation Laboratory (DEMATEL), cognitive mapping, and VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) methods under z-fuzzy environment has been introduced. In the study, after the proposed z-fuzzy DEMATEL method is used to identify the dependencies among energy storage technologies’ assessment criteria, z-fuzzy cognitive mapping is employed to determine the importance degrees of criteria. Finally, the most suitable hydrogen energy storage alternative is selected by utilizing the z-fuzzy VIKOR technique. As a result of applying this methodology, storage capacity and response time have been determined as the most and the least important criteria with the weights of “0.0649” and “0.0447” respectively, and the alternative “carbon nanotubes” has been determined as the most appropriate option for Turkey. Comparative and sensitivity analyses have also been carried out to validate the proposed methodology and demonstrate the flexibility and robustness of decisions. As a result, we aimed to present a beneficial road map for researchers and policymakers studying hydrogen storage.
With the increasing product variety, companies aim to become better than their competitors by providing a superior product developed with a customer–oriented product design approach and a quality strategy. In order to achieve this, companies should well understand customer expectations and quickly be able to convert these expectations to technical characteristics. Since the expectations consist of mostly subjective judgments, this evaluation process contains vagueness and impreciseness. A triplet represents the uncertainty in subjective judgments: the degrees of belongingness or Truthiness (T), non-belongingness or Falsity (F), and indeterminacy (I). For this reason, in this paper, a neutrosophic Quality Function Deployment (QFD) methodology based on neutrosophic AHP and neutrosophic DEMATEL is developed and applied to the design of a car seat. In this methodology, the weighting of customer requirements is performed by neutrosophic AHP. The relationships among the technical characteristics are determined by neutrosophic DEMATEL for the customer-oriented product design, considering both impreciseness in the data and indeterminacy of the decision-makers. In other words, the contribution of this paper is that the proposed methodology provides better integration of the voice of customers into technical characteristics through a practical fuzzy multi–criteria decision analysis. Based on the results, it is revealed that seat height is the most important technical characteristic, followed by vertical travel range and horizontal travel range. Moreover, validity and verification of the proposed methodology have been tested with other methods presented in the literature. Also, sensitivity analyses have been carried out to show the flexibility of the given decisions under different cases. Lastly, possible implications on theoretical and managerial aspects have been discussed.
Agriculture 4.0 is the usage of advanced technologies for ensuring sustainability and efficient use of resources in agriculture, which can be accepted one of the biggest challenges of mankind. With Agriculture 4.0 technologies, it is aimed to raise productivity, reduce waste and costs. To ensure that agriculture 4.0 technologies are adopted by farmers as soon as possible, the perspective and perception of farmers to these technologies should be analyzed firstly. For this purpose, we propose a decision-making framework to measure farmers' view of Agriculture 4.0 technologies and to perform a prioritization study based on the perception of use among these technologies. Multicriteria decision analysis is also utilized to deal with all qualitative and quantitative factors in the decision process. Within the scope of this study, interval-valued spherical fuzzy numbers are used to model the vagueness in the process in the best manner and to be able to reflect the uncertainty arising from the usage of linguistic variables in the decision process. The SWARA and MAIRCA multicriteria decision making (MCDM) methods, which have been used frequently in the literature and are applied very successfully in MCDM problems, have been firstly extended by spherical fuzzy sets (SFSs) and the advantages for these methods have been utilized within the framework of fuzzy logic. The proposed method allows decision-makers to mirror their hesitations in decision-making using a linguistic evaluation scale established upon interval-valued SFSs. A comparative analysis based on the ordinary fuzzy sets is also performed for the obtained results and the clear superiority of the proposed approach is presented. In addition, the robustness of the model is tested with sensitivity analysis. After these analyzes it is obtained that this paper provides a unique perspective to the literature due to its originality in both the subject and adopted fuzzy methodology.
Internal audit is an independent and objective assurance and consulting activity that aims to improve the operations of an organization and add value to them. Planning internal audit by prioritizing the units to be audit is critical in terms of effective use of available audit and financial resources. In this paper, a new ELimination and Choice Translating Reality (ELECTRE) based decision support model is developed for addressing an internal audit prioritization problem. Spherical fuzzy sets are used for modeling the uncertainty in the nature of the problem and three different approaches are proposed within the study. The first approach is constructed with gradual concordance and discordance sets by comparing spherical fuzzy membership, non-membership, and hesitancy degrees of alternatives; the second approach is developed based on a single type of outranking relation obtained by utilizing score and accuracy functions of spherical fuzzy sets, and the third approach provides an increased fuzziness modeling capacity by using interval-valued spherical fuzzy sets. In the application part of the study, the units of an organization are prioritized for internal audit activity based on five components of the internationally recognized Committee of Sponsoring Organizations (COSO) framework. Sensitivity analyses for decision-maker and criterion weights and a comparative analysis with six other state-of-the-art multi-criteria decision making (MCDM) models are also presented to analyze the consistency and validity of the proposed spherical fuzzy ELECTRE model.
A dangerous infectious disease of the current century, the COVID-19 has apparently originated in a city in China and turned into a widespread pandemic within a short time. In this paper, a novel method has been presented for improving the screening and classification of COVID-19 patients based on their chest X-Ray (CXR) images. This method eliminates the severe dependence of the deep learning models on large datasets and the deep features extracted from them. In this approach, we have not only resolved the data limitation problem by combining the traditional data augmentation techniques with the generative adversarial networks (GANs), but also have enabled a deeper extraction of features by applying different filter banks such as the Sobel, Laplacian of Gaussian (LoG) and the Gabor filters. To verify the satisfactory performance of the proposed approach, it was applied on several deep transfer models and the results in each step were compared with each other. For training the entire models, we used 4560 CXR images of various patients with the viral, bacterial, fungal, and other diseases; 360 of these images are in the COVID-19 category and the rest belong to the non-COVID-19 diseases. According to the results, the Gabor filter bank achieves the highest growth in the values of the defined evaluation criteria and in just 45 epochs, it is able to elevate the accuracy by up to 32%. We then applied the proposed model on the DenseNet-201 model and compared its performance in terms of the detection accuracy with the performances of 10 existing COVID-19 detection techniques. Our approach was able to achieve an accuracy of 98.5% in the two-class classification procedure; which makes it a state-of-the-art method for detecting the COVID-19.
Blockchain technology (BT), which provides to record transactions among many computers as a distributed ledger, is commonly utilized by companies to develop and manage business processes. Since it is a multi-process and multi-agent complex system, the implementation process of BT contains many risk factors. Identifying and analyzing these risks in order to minimize their adverse effects during the implementation process is also completely critical for companies. Since evaluating these risks must be handled by considering many different perspectives, the evaluation of risk factors for BT can be considered as a multi-criteria decision-making (MCDM) problem. To cope with uncertainty in this process, the fuzzy set theory (FST) can be effectively utilized with MCDM methods to increase their ability to obtain more concrete and realistic results. Therefore, this paper proposes an MCDM methodology consisting of Decision-Making Trail and Evaluating Laboratory (DEMATEL) and cognitive mapping methods based on FST. These methods have been reconstructed with hesitant fuzzy Z-numbers that enable not only to state impreciseness of the assessments but also to consider indeterminacy and hesitancy of the experts to evaluate risk factors with respect to BT implementation. While the dependencies of risk factors have been determined via the hesitant fuzzy Z-DEMATEL method, their weights have been obtained through the hesitant fuzzy Z-cognitive mapping method. As a result, this study aims to prioritize risk factors related to the BT implementation process with a novel fuzzy decision-making methodology and provide a road map for companies. With this study, which is the first paper in the literature in terms of the subject of the study and the method adopted, an integrated, comprehensive fuzzy approach is introduced for the first time in evaluating BT risks. As a result of detailed research, risk factors that companies may encounter in BT use and the degree of their importance have been determined. Through that, essential decision analysis has been put forward that companies can apply for their strategic decisions. A sensitivity analysis based on changes in the initial vector values has also been adapted to analyze the effects of the risk factors.
The spherical fuzzy set (SFS), as a generalization of Picture fuzzy set, can express and process uncertainty much better. In this article, we first reveal the comparison issues in spherical fuzzy setting and then formulate a novel score function to remedy the drawbacks of the existing score functions. Some of its interesting properties have also been verified in detail. Next, we research two novel spherical fuzzy operations, i.e., subtraction and division operations, and their relevant characteristics. Further, based on the proposed score function, a formula for the conversion process of decision-makers weight information from the spherical fuzzy form to classical form is formulated. In addition, criteria importance through intercriteria correlation model is extended to the spherical fuzzy environment to tackle the situation where the criteria weight information is completely unknown. Following this, measurement of alternatives and ranking according to the compromise solution method is built under the background of SFSs. After that, an illustrative example of smartphone selection problem is considered to show the implication and supremacy of the presented study. Meanwhile, sensitivity analysis, a validity test and a detailed comparison are carried out to evaluate the supremacy and the validity of the created method.
Autonomous vehicle driving systems (AVDSs) have independent decision structures from the users to manage and control the operations of the vehicles both in normal conditions and unexpected situations. Although there are some advantages, such as decreasing accidents reasoned by human errors and efficient energy usage, it is obvious that some risks are rooted in this technology usage. Therefore, it will be beneficial to realize a risk assessment application for autonomous vehicles (AVs) and/or driving systems (DSs) since whose risks are crucial to test and solve. In this paper, a multi-criteria decision making (MCDM) methodology integrating DEcision MAking Trial and Evaluation Laboratory (DEMATEL), Analytical Network Process (ANP), and VlseKriterijuska Optimizacija I Komoromisno Resenje (VIKOR) techniques under spherical fuzzy environment have been suggested to evaluate AVDS alternatives in terms of considered risk criteria. Spherical fuzzy sets (SFS), which are the extension of the ordinary fuzzy sets, have been used to consider the hesitancy of experts and decision-makers as well as uncertainty and impreciseness in the available data. In the application, six AVDS alternatives have been evaluated in terms of seven main criteria and forty sub-criteria. The factors “Software Specifications” and “Reliability” have been determined as the most important main and sub-criteria with the weights 0.193 and 0.066, respectively. Additionally, comparative and sensitivity analyses have been applied to present flexibility, validation and verification of the proposed methodology together with the sensitivity of the given decisions. Based on the application results and conducted analyses, possible implications by views of theoretical, managerial, and policy context have been discussed.
Many studies have used different financial ratios for financial accounting fraud detection. This study focuses on multi criteria decision-making (MCDM) for ranking financial ratios in detecting financial accounting fraud using interval-valued spherical fuzzy sets (IVSFS) and single-valued spherical fuzzy sets (SVSFS) to overcome uncertainties in decision-making process of financial analysts. This study proposes an integrated Analytic Hierarchy Process (AHP) and Multi-Objective Optimization by a Ratio Analysis plus the Full Multiplicative Form (MULTIMOORA) approach using IVSFS and SVSFS. Comparative results are obtained and discussed in prioritization of financial ratios for both IVSFS and SVSFS.
In today’s highly competitive business market, the issue of sustainable supplier selection attracts great attention due to increased awareness of both social and environmental issues. The power of companies in the supply chain is determined not only by their own performance, but also by the power of other actors in the chain. As a field of study, the selection of sustainable suppliers remains its popularity because it requires developing a systematic procedure that addresses conflicting quantifiable and non-quantifiable factors simultaneously. In this study, one of the advanced fuzzy approaches called Spherical Fuzzy Sets (SFS) has been used to prioritize criteria that affect sustainable supplier selection. The proposed methodology combines the Spherical Fuzzy Sets and the analytic hierarchy process (AHP) and takes into account four main criteria: economic, quality, social and environmental criteria. In order to prioritize the main and sub criteria, questionnaires were conducted with three experts who have valuable experience in this field of study. After then, for the selection of the best sustainable supplier, a real selection problem in an international company was handled. The figured out ranking of the criteria and supplier alternatives can be used as a guide by the researchers and industrial experts who are responsible for sustainable supplier selection. To measure how much unit increase in weights assigned to key criteria affects supplier selection, a sensitivity analysis was carried out. It was noticed that the selection procedure for the selected company is not highly sensitive to changes.
The membership functions of the intuitionistic fuzzy sets, Pythagorean fuzzy sets, neutrosophic sets and spherical fuzzy sets are based on three dimensions. The aim is to collect the expert’s judgments. Physicians serve patients in the physician selection problem. It is difficult to measure the service’s quality due to the variability in patients’ preferences. The patients physician preference criteria is differing and uncertainties. Thus, solving this problem with fuzzy method is more appropriate. In this study, we considered the physician selection as a multi-criteria decision-making problem. Solving this problem, we proposed a spherical fuzzy TOPSIS method. We used the five alternatives and eight criteria. The application was performed in the neurology clinics of Konya city state hospitals. In addition, we solved the same problem by the intuitionistic fuzzy TOPSIS method. We compared the solutions of two methods with each other. We found that the spherical fuzzy TOPSIS method is effective for solving the physician selection problem.
Digitalization is the key trend of the Industry 4.0 revolution. Industrial companies are transforming the way they design and maintain their products and solutions. The user requirements become more demanding. Competition among the manufacturing companies is at its limits and transforms the products to be more complex. Yet, other challenges such as faster time to market, higher quality requirements and legislation force enterprises to provide new ways of design, manufacture and service their end products. Product Lifecycle Management (PLM) is a key solution to track the entire lifespan of the product from idea to design, design to manufacture and manufacture to service. Besides the complexity of products and production, the selection of the right PLM solution which will become the backbone of enterprises is an open problem. In this paper, a thorough literature review is conducted to analyze the most important features for selecting the right PLM solution for manufacturing firms. Moreover, to overcome the challenge of decision makers’ (DM) subjective judgments, a novel interval value spherical fuzzy COPRAS (IVSF-COPRAS) multi-criteria decision making (MCDM) method is introduced. The paper aims to help enterprises rapidly identify the best alternative vendor/solution to be selected based on the need of the organization. In order to show the applicability, DM inputs are collected from a leading defense company where the PLM selection process is ongoing. The industrial case study is provided to demonstrate the success of the proposed selection framework.
Linguistic terms are quite suitable to make evaluations in multiple criteria decision making problems since humans prefer them rather than sharp evaluations. When linguistic evaluations are used in the decision matrix instead of exact numerical values, fuzzy set theory can capture the vagueness in the linguistic evaluations. Ordinary fuzzy sets have been extended to many new types of fuzzy sets such as intuitionistic fuzzy sets, neutrosophic sets, spherical fuzzy sets and picture fuzzy sets. Spherical fuzzy sets are an extension of picture fuzzy sets whose squared sum of their parameters is at most equal to one. This paper develops a novel spherical fuzzy CRiteria Importance Through Intercriteria Correlation (CRITIC) method and applies it for prioritizing supplier selection criteria. Supplier selection is one of the most critical aspects of any organization since any mistake in this process may cause poor supplier performance and inefficiencies in the business processes. Supplier selection is a multi-criteria decision making problem involving several conflicting criteria and alternatives. A numerical illustration of the proposed method is also given for this problem.
In line with the increase in consciousness on sustainability in today’s global world, great emphasis has been attached to food waste management. Food waste is a complex issue to manage due to uncertainties on quality, quantity, location, and time of wastes, and it involves different decisions at many stages from seed to post-consumption. These ambiguities re-quire that some decisions should be handled in a linguistic and ambiguous environment. That forces researchers to benefit from fuzzy sets mostly utilized to deal with subjectivity that causes uncertainty. In this study, as a novel approach, the spherical fuzzy analytic hierarchy process (SFAHP) was used to select the best food treatment option. In the model, four main criteria (infrastructural, governmental, economic, and environmental) and their thirteen sub-criteria are considered. A real case is conducted to show how the proposed model can be used to assess four food waste treatment options (composting, anaerobic digestion, landfilling, and incineration). Also, a sensitivity analysis is generated to check whether the evaluations on the main criteria can change the results or not. The proposed model aims to create a subsidiary tool for decision makers in relevant companies and institutions.
Process mining (PM) supports organizations by improving their processes using event log data collected from information technology systems. Its primary purposes are discovering actual process models, monitoring and comparing actual and desired workflows, and enhancing processes by considering the discovered model and desired flow. Because process mining gains attraction day by day, various technology companies developed process mining tools to support organizations managing their business processes with data science. Technology selection is a complicated multi-criteria decision-making (MDCM) problem under several criteria and experts’ evaluation, including uncertainty and subjectivity. Spherical fuzzy set is a powerful concept to cope with uncertainty by presenting a wider decision-making area and identifying hesitancy. A fuzzy MDCM approach based on spherical fuzzy AHP is offered in this study to manage the problem of selecting process mining technology under uncertain and ambiguous conditions. Then, one-at-a-time sensitivity analysis is applied to reduce the decision-makers’ subjectivity. This study results in that Price, Process Discovery, Process Analysis&Analytics are the most relevant criteria to decide PM technology. It is interesting that even although Operational Support is one of the less important criteria, it may change the decision on selecting the best PM technology.
Undoubtedly, dams are one of the projects developed by people to make optimal water use and generate hydroelectric energy power. In addition to the importance of detailed project planning, faultless production, and optimal working conditions in dam constructions, it is equally important to identify and analyze the risks from the occupational health and safety (OHS) perspective that may arise during construction and operation. In this context, hazard identification, risk analysis, and control, which are the main issues of dam safety, have gained importance. In this study, a novel approach is developed for dam construction safety. Two newly proposed multi-criteria decision-making (MCDM) methods of “Best-Worst Method-BWM” and “measurement of alternatives and ranking to Compromise solution-MARCOS”, are merged under the context of interval type-2 fuzzy sets (IT2FSs). Severity and probability, two essential parameters of the risk score, have been weighted using BWM. Subsequently, the priority orders of hazards have been determined by the MARCOS. In implementing the approach to the blasting process in dam construction, several control measures are suggested for stakeholders. Some comparative works and sensitivity analyses are performed to test the approach's validity and solidity from the methodological viewpoint.
Bottlenecks and wastes are prevalent issues in warehouses, particularly in the pharmaceutical industry with large stock volumes. Value stream mapping (VSM) is a powerful lean instrument to detect value-adding and process efficient feasibilities in a production chain. It has the ability to connect all the stakeholders in the chain and manage customer demand. Optimum information and material flow are needed in a pharmaceutical warehouse supply chain without backlogs to avoid the supply chain lead time build-up. In this study, a pharmaceutical warehouse has been studied by dividing it into two major segments and the VSMs for both present state and future state were drawn to find out possible feasibility of lean implementation to improve the warehouse operations. There was a 20.22 % increase in the value-added time and 23.17 % decrease in the non-value added time based on the lean proposals and assumptions in the future state. But still, more quantifiable methods are needed to dynamically visualize the proposed lean tools in the supply chain.
The hybrid of AHP and TOPSIS has led researchers to integrate the combination with different extensions of fuzzy sets. The recently developed three-dimensional spherical fuzzy set is an extension of the fuzzy set, which is effective in handling uncertainty and quantifying expert judgements. In this paper, a novel framework is elaborated which combines AHP and TOPSIS with a spherical fuzzy set. Spherical fuzzy AHP is used to calculate the spherical fuzzy weights of the criteria, while spherical fuzzy TOPSIS is used to find the final rank of the alternatives. A new spherical fuzzy geometric mean formula is proposed for calculating the spherical fuzzy criteria weights. A new eleven-point spherical fuzzy linguistic term scale is presented, which can be used by the experts to quantify the preference. The proposed framework is applied to an advanced manufacturing system selection problem with six evaluation criteria and four alternatives. It is found that spherical fuzzy AHP-TOPSIS is effective in handling uncertainty in decision making and leads to robust and competitive results compared with state-of-the-art multi-criteria decision-making (MCDM) approaches.
In this paper, we propose a novel Interval Type-2 (IT2) Fuzzy Cognitive Map (FCM)-based flight control system to solve the altitude, attitude and position control problems of quadcopters. The proposed IT2-FCM encompasses all concepts related to drone for a satisfactory path-tracking and stabilizing control performance. The degree of mutual influences of the concepts is designed with opinions of three experts that take account the dynamics of drone and rules governing proportional integral derivative (PID) controllers. To model the inter-uncertainty of the experts’ opinions, IT2 fuzzy logic systems are utilized as they are powerful tools to model high level of uncertainties. Thus, the proposed IT2-FCM has a qualitative representation as it merges the advantages of IT2 fuzzy logic systems and FCMs. We present comparative simulations results in presence of uncertainties where the superiority of the proposed IT2-FCM-based flight control system is shown in comparison with its type-1 fuzzy counterpart.
Purpose
Locating disaster response centers is one of the key elements of efficient relief operations. The location and infrastructure of the candidate facilities usually conform to the required criteria at different levels. This study aims to identify the criteria for the main and local distribution center location problem separately and prioritize each candidate distribution center using a hybrid multiple criteria decision-making approach.
Design/methodology/approach
The proposed model incorporates analytic hierarchy process (AHP) and Technique for Order Preference by Similarity to Ideal Solutions (TOPSIS) under interval type-2 fuzzy sets (IT2FSs) to overcome the uncertainty of experts` judgments and expressions in the evaluations of candidate distribution centers. In the proposed approach, weights of the criteria are determined using type-2 fuzzy AHP and the candidate distribution centers are prioritized using type-2 fuzzy TOPSIS.
Findings
Transportation, cost, infrastructure and security are determined as the main criteria for the main distribution center location criteria. Cost, warehouse facilities and security are the main criteria for local distribution center location selection. Prioritization enables decision-makers to assess each alternative accordingly to be able to select the best locations/facilities for efficient disaster response operations.
Originality/value
This study proposes new multi-criteria decision support models for prioritizing disaster response distribution centers. IT2FSs are used to be able to reflect both the complexity and vagueness of disaster environment and expert opinions. Different support models are suggested for main and local distribution centers considering their different missions. The proposed methodology is applied in Istanbul city, Turkey, where a high-magnitude earthquake is expected.
Spherical fuzzy sets (SFSs) were recently introduced to reflect experts’ judgements explicitly and in a more informative manner. TOmada de Decisão Interativa e Multicritério (TODIM) is one of the multi-criteria decision-making techniques. It is founded on the prospect theory that considers the decision-makers psychological behaviours under risk. In this article, TODIM is extended using the novel concept of SFSs to exploit its superior features to incorporate a new point of view in fuzzy decision-making under risk, allowing the decision-makers to express their hesitation independently. To establish the TODIM strategy, two evaluation functions are employed to form the partial dominance degree matrices: the importance evaluation function and the performance evaluation function. A practical example in supplier selection is solved, and a case study on selecting green occupational health and safety (OHS) equipment supplier is given. The results are compared with the results of the spherical fuzzy Technique of Order Preference by Similarity to an Ideal Solution (SF-TOPSIS) and the spherical fuzzy VIsekriterijumska optimizacija i KOmpromisno Resenje (SF-VIKOR). The effect of the attenuation factor of losses on the solution is examined. Finally, a different implementation of the SF-TODIM is used and the processing time of the two implementations is compared.
Purpose
Lean implementation is vastly incorporated in core manufacturing processes; however, its applicability in the supply chain and service industry is still in its infancy. To acquire performance excellence and thrive in the global competitive market, many firms are adopting newer methodologies. But, there is a stringent need for production simulation systems to analyze supply chains both inbound and outbound. The era of face validation is slowly disappearing. Lean tools and procedures that provide future state assumptions need advanced tools and techniques to measure, quantify, analyze and validate them. The purpose of this study is to enable dynamic quantification and visualization of the future state of a warehouse supply chain value stream map using discrete event simulation (DES) technique.
Design/methodology/approach
This study aimed to apply an integrated approach of the value stream mapping (VSM) and DES in a Malaysian pharmaceutical production warehouse. The main focus is diverted towards reducing the warehouse supply chain lead time by initially constructing a supply chain value stream map (both present state and future state) and integrating its data in a DES modelling and simulation software to dynamically visualize the changes in future state value stream map.
Findings
The DES simulation was able to mimic the future state lead time reductions successfully, which assists in better decision-making. Improvements were seen related to total lead time, process time, value and non-value-added percentage. Warehouse performance metrics such as receiving, put away and storage rates were substantially improved along with pallet processing time, worker and forklift throughput usage percentage. Detailed findings are clearly stated at the end of this paper.
Research limitations/implications
This study is limited to the warehouse environment and further additional process models and functional upgrades in the DES software systems are very much needed to directly visualize and quantify all the possible Lean assumptions such as radio frequency image identification/Andon (Jidoka), 5S, Kanban, Just-In-Time and Heijunka. However, DES has a leading edge in extracting dynamic characteristics out of a static VSM timeline and capture details on discrete events precisely by picturizing facility modification and lead time related to it.
Practical implications
This paper includes all the fundamental pharmaceutical warehouse supply chain processes and the simulations of the future state VSM in a real-life context by successfully reducing supply chain lead time and allowing managers in inculcating near-optimal decision-making, controlling and coordinating warehouse supply chain activities as a whole.
Social implications
This integrated approach of DES and VSM can involve managers and top management to support the adoption of anticipated changes. This study also has the potential to engage practitioners, researchers and decision-makers in the warehouse industry.
Originality/value
This study involves a powerful DES software package that can mimic the real situation as a virtual simulation and all the data and model building are based on a real warehouse scenario in the pharmaceutical industry.
Leftover/end of life (EOL) medications not only cause financial losses for the pharmacy but also lead to disposal costs and high governmental penalties for the pharmaceutical supplier. In this study, a two-echelon pharmaceutical supply chain (PSC), including a single pharmaceutical supplier (pharma-supplier) and a single pharmaceutical retailer (pharmacy) with one type of fixed shelf-life medicine, is analyzed. A reducing pharmaceutical wastes scheme is developed to decrease relevant costs of leftover/EOL medicines, avoid governmental penalties, and also ensure patient service level (PSL). In the proposed model, excess medications are collected from the pharmacy warehouse based on the realized demand at a specific time before the expiration date to be resold in the secondary/alternative market. The introduced model is investigated in decentralized and centralized model structures. A novel combination of buyback and shortage risk-sharing contract (B&SRS) is proposed to attain channel coordination, maximize the whole PSC profits, and motivate members to participate in the scheme. The numerical investigation demonstrates that the proposed B&SRS contract can coordinate the developed PSC, enhance the whole channel profit, and also guarantee both channel members’ profitability. Besides, the investigated model helps pharmaceutical managers to decrease EOL/expired medicines related costs and ensure PSL by making decision properly on buyback quantity.
Purpose
The purpose of this study is to apply value stream mapping (VSM) in Malaysian pharmaceutical production warehouse. A current and future state value stream map from the raw material receiving end to the production unit was developed to find out waste and unwanted lead time. It was very much essential to cut down the supply chain lead time at the initial phase as the raw material unloading, sorting, temporary storage and dispatch to production were seen contributing to a huge lead time build-up.
Design/methodology/approach
The study was initiated with the selection of a product family, construction of the current state map, identification of various wastes and the development of future state map.
Findings
The expected outcomes of the study include the quantification of wastes, improvement in value-added percentage and lead time reduction.
Research limitations/implications
The study was carried out in a single pharmaceutical company. The results of the study are deployable and can be functional in similar production organizations. Contrary to common VSMs that capture core production processes, this study provides strong insights that shall help design lean supply chains, especially in the pharmaceutical domain. This paper has also addressed the viability of the lean in the pharmaceutical warehouse and the reduction in lead time to improve demand forecasting, marketing and sales.
Practical implications
The results of this study have indicated that a significant reduction in pharmaceutical warehouse supply chain lead time is possible as a result of the implementation of VSM from the supply chain’s perspective.
Social implications
The insights from this study help in understanding the pharmaceutical supply chain risks and their outcomes.
Originality/value
The paper reports a real-time study conducted in a warehouse of a pharmaceutical organization. Hence, the contributions are original.
In this study, a new approach based on fuzzy cognitive map (FCM) and neuro-fuzzy inference system (NFIS), called the neuro-fuzzy cognitive map (NFCM), is proposed. Here, the NFCM is used for diagnosis of autoimmune hepatitis (AIH). AIH is a chronic inflammatory liver disease. AIH primarily affects women and typically responds to immunosuppressive therapy with clinical, biochemical, and histological remission. An untreated AIH can lead to scarring of the liver and ultimately to liver failure. If rapidly diagnosed, AIH can often be controlled by medication. NFCM is a new extension of FCM, which employs a NFIS to determine the causal relationships between concepts. In the proposed approach, weights are calculated using the knowledge and experience of experts as well as the advantages of NFIS. This makes the presented model more accurate. Having a high convergence speed, the proposed NFCM model performs well by achieving an AIH diagnosis accuracy of 89.81%. The superiority of the proposed NFCM model over the conventional FCM is that, it uses the NFIS to determine the link weights which train system parameters.
Vehicle routing plays an important role in Home Health Care (HHC) logistics. In this paper, HHC concerns the delivery of medical commodities from pharmacy to patient, delivery of special drugs from hospital to patient, pickup of unused medical commodities from patients. The remaining capacity in the journey from pharmacy to hospital of all route is calculated which can be used for the transportation from pharmacy to hospital. The problem can be considered as a special vehicle routing problem with simultaneous pickup and delivery and time windows, with three types of demands: delivery from pharmacy to the patient, delivery from hospital to patient, pick up from patient to pharmacy. The vehicles are associated with capacity and demands of patients with time windows. In this way, a mixed integer programming model is proposed for optimal scheduling in HHC. The model is linearized and then solved by applying an optimization software named GUROBI. The output of the model contains an optimal vehicle routes assignment which helps the HHC Company making a good decision for patient visits.