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This paper proposes a new multi-criteria group decision-making (MCGDM) method utilizing q-rung orthopair fuzzy (qROF) sets, improved power weighted operators and improved power weighted Maclaurin symmetric mean (MSM) operators. The power weighted averaging operator and power weighted Maclaurin symmetric mean (MSM) operator used in the existing MCGDM methods have the drawback of being unable to distinguish the priority order of alternatives in some scenarios, especially when one of the qROF numbers being considered has a non-belongingness grade of 0 or a belongingness grade of 1. To address this limitation of existing MCGDM methods, four operators, namely qROF improved power weighted averaging (qROFIPWA), qROF improved power weighted geometric (qROFIPWG), qROF improved power weighted averaging MSM (qROFIPWAMSM) and qROF improved power weighted geometric MSM (qROFIPWGMSM), are proposed in this paper. These operators mitigate the effects of erroneous assessment of information from some biased decision-makers, making the decision-making process more reliable. Following that, a group decision-making methodology is developed that is capable of generating a reasonable ranking order of alternatives when one of the qROF numbers considered has a non-belongingness grade of 0 or a belongingness grade of 1. To investigate the applicability of the proposed approach, a case study is also presented and a comparison-based investigation is used to demonstrate the superiority of the approach.

In order to avoid working in a constrained hazardous environment, manual spray painting operation is gradually being replaced by automated robotic systems in many manufacturing industries. Application of spray painting robots ensures defect-free painting of dissimilar components with higher repeatability, flexibility, productivity, reduced cycle time and minimum wastage of paint. Due to availability of a large number of viable options in the market, selection of a spray painting robot suitable for a given application poses a great problem. Thus, this paper proposes the integrated application of step-wise weight assessment ratio analysis (SWARA) and combined compromise solution (CoCoSo) methods to identify the most apposite spray painting robot for an automobile industry based on seven evaluation criteria (payload, mass, speed, repeatability, reach, cost and power consumption). The SWARA method identifies cost as the most significant criterion based on a set preference order, whereas, Fanuc P-350iA/45 is selected as the best spray painting robot by CoCoSo method. The derived ranking results are also contrasted with other popular multi-criteria decision making (MCDM) techniques (TOPSIS, VIKOR, COPRAS, PROMETHEE and MOORA) and subjective criteria weighting methods (AHP, PIPRECIA, BWM and FUCOM). High degrees of similarity in the ranking patterns between the adopted approach and other MCDM techniques prove its effectiveness in solving complex industrial robot selection problems. This integrated approach is proved to be quite robust being almost unaffected by the changing values of the corresponding tuning parameter in low-dimensional MCDM problems.

Supplier selection in food supply chains (FSCs) is not much explored due to the inherent difficulties, complexities and nature of food industry. Food security and quality are top row topics in today’s world health scenario. During sudden food crisis, it needs extra attention where producers, suppliers, and stakeholders play the most vital roles. This paper puts forward a two-phase sustainable multi-tier supplier selection model for FSC based on an integrated decision analysis under multi-criteria perspectives considering sustainability criteria, suppliers and sub-suppliers. In the first phase, the model estimates supplier selection criteria weights using a combined version of step-wise weight assessment ratio analysis (SWARA) and level based weight assessment (LBWA) in conjunction with D-numbers. In the second phase, Measurement of Alternatives and Ranking according to the COmpromise Solution (MARCOS)-D method is applied to obtain a ranking pre-order of different tier suppliers. Moreover, several sensitivity analyses are carried out in order to examine model reliability. To check application practicability, the proposed model is implemented in a case study of WineSol Corporation in Spain. The proposed model is expected to serve as a kickoff point for developing advanced decision-making models for effectually address multi-tier supplier selection problems under uncertain environment.

In a turning operation, involving removal of material from the outer diameter of a rotating cylindrical workpiece using a single-point cutting tool, there exist complex relationships between various cutting parameters and responses. In this paper, a turning operation under dry environment is considered with cutting speed, feed rate, and depth of cut as the input parameters, as well as material removal rate, average surface roughness, and cutting force as the responses. Dry turning operation reduces energy consumption and machining cost, thus eventually resulting in sustainable machining. For the considered process, the corresponding response values are envisaged using four prediction models, that is, multivariate regression analysis, fuzzy logic, artificial neural network, and adaptive neurofuzzy inference system (ANFIS), and their prediction performance is contrasted using five statistical metrics, that is, root mean squared percent error, mean absolute percentage error, root mean squared log error, correlation coefficient, and root relative squared error. It is noticed that ANFIS model consisting of the advantages features of both fuzzy logic and neural network outperforms the other prediction models with respect to the computed values of the considered statistical measures. Based on their acceptable values, it can be propounded that the ANFIS model can be effectively employed for prediction of process responses while treating different machining parameters as the input variables.

Over the last decades, most automakers have started to append sustainable materials to lighten the weight in their vehicles to meet stringent, rigorous environmental regulations, which will be more economical, sustainable and improve fuel efficiency. It will also improve the relationship between power and weight of the vehicles, which will translate into greater performance from the present condition. Nowadays, Multi-criteria Decision Analysis (MCDA) is one of the fastest-growing areas of operations research. The methods of MCDA distinguish themselves in terms of procedures, theoretical assumptions, and type of decision addressed. This diversity poses challenges to the process of selecting the most suited method for a specific real-world decision problem. In this research paper, an inter-criteria co-relation model is adopted to solve a real-world discrete sustainable automotive material selection problem.

"Background: The coronavirus disease pandemic produced changes in the treatment approach of lung cancer patients. They represent a vulnerable population due to the negative impact of delays in diagnosis and treatment and also because of the impaired immune system. Radiation Therapy plays a crucial role in both curative and palliative settings, so finding the best approach for these patients in this context was attempted by many professionals. The aim of this study was to make a synthesis of the current published literature on this topic and summarize the recommendations. Material and methods: We searched the PubMed database for articles published between 20th December 2019 and 30th August 2020. We used 14 key words related to COVID 19 and Lung Cancer. The articles were selected by two senior clinicians who excluded overlapping information and prioritized original research reports and professional organization’s official recommendations. Results: There were 562 papers corresponding to the search criteria, of which 64 were analyzed. Treatment decision must be tailored according to the status of SARS CoV-2 positivity, the aim of the treatment (curative VS palliative), pathological type and clinical stage, disease complications, symptoms, prognostic, performance status, and alternative treatments. Hypofractionation is preferred whenever possible in order to minimize risk for patients and staff. Safety measures have to be implemented, with extra cautions if SARS CoV-2 positive patients have to be treated. Cone beam CT can be used early identification of lung infiltrates and special attention is recommended to differentiate Radiation pneumonitis from COVID-19 pneumonia. Journal of Medical and Radiation Oncology Journal homepage: www.jmedradonc.org Review 94 Conclusion: For SARS CoV-2 positive lung cancer patients the treatment should be postponed, until they are tested negative. For patients which are SARS CoV-2 negative treated with curative intent, hypofractionated schedules are preferred. Palliative treatments should be given according to the life threatening risk."

PurposeIndustrial organizations often face difficulties in finding out the methods to meet ever increasing customer expectations and to remain competitive in the global market while maintaining controllable expenses. An effective and efficient green supply chain management (GSCM) can provide a competitive edge to the business. This paper focusses on the selection of green suppliers while simultaneously balancing economic, environmental and social issues.Design/methodology/approachIn this study, it is assumed that two types of decision-makers (DMs), namely, the first level and second-level DMs operate at two separate groups in GSC. The first-level DMs always empathise to optimize carbon emissions, per unit energy consumption per product and per unit waste production, while the second-level DMs seek to optimize ordering costs, number of rejected units and number of late delivered units in the entire GSCM. In this paper, fuzzy goal programming (FGP) approach has been adopted to obtain compromise solution of the formulated problem by attaining the uppermost degree of each membership goal while reducing their deviational variables. Furthermore, demand has also been forecasted using exponential smoothing analysis. The model is verified on a real-time industrial case study.FindingsThis research enables DMs to analyse uncertainty scenarios in GSCM when information about different parameters are not known precisely.Research limitations/implicationsThe proposed model is restricted to vagueness only, however, DMs may need to consider probabilistic multi-choice scenarios also.Practical implicationsThe proposed model is generic and can be applied for large-scale GSC environments with little modifications.Originality/valueNo prior attempt is made till date to present interval type-2 fuzzy sets in a multi-objective GSC environment where the DMs are at hierarchical levels. Interval type-2 fuzzy sets are considered as better ways to represent inconsistencies of human judgements, its incompleteness and imprecision more accurately and objectively. Also, crisp or deterministic forms of uncertain parameters have been obtained by taking expected value of the fuzzy parameters.

Purpose The implementation of circular economy strategies is one of the central objectives of several governments seeking a transition toward a sustainable development. Circular economy in agriculture deals with the production of agricultural commodities making an efficient use of resources and avoiding unnecessary waste and carbon emission generation. Disruptions in the production and supply of critical agricultural products can have serious negative repercussions for firms and consumers of the food supply chain. In recent decades, disruptions generated by natural disasters such as hurricanes, thunderstorms and floods have greatly impacted social communities and industrial sectors. Supply chain risks approaches are seen to contribute key elements to address the impacts of natural disaster toward the implementation of circular economy in agriculture, helping to prevent collapses in the production and supply of food. The purpose of this paper is to study and identify flood risk drivers and their effects on the sustainability of an agriculture supply chain in connection with a circular economy strategy. By using an extended Step-wise Weight Assessment Ratio Analysis method combined with a multi-criteria decision analysis, the most essential flood drivers with a degree of importance are reported here. Then, the authors propose an Evaluation of Data based on average ASsessment method, to rank different agricultural projects that pretend to mitigate the flood risks and its impacts on crop areas. The application of this research lies within the framework of a real agricultural project founded by the European Commission Scientific Section, called RUC-APS. Design/methodology/approach The authors use management science-based tools to address circular economy in agriculture. The authors propose a multi-criteria-based methodology to assess the risks of flooding in crops areas. To validate the proposed methodology, a case example from Spain is discussed to rank different agricultural projects that pretend to mitigate the flood risks and its impacts on crop areas. Findings The proposed multi-criteria methodology confirmed a successful application to rank different agricultural projects that pretend to mitigate the flood risks and its impacts on crop areas. Organizations and firms in the agricultural business can use the methodology to identify risks drivers and to detect the best projects to mitigate the highest impacts of flooding risks in crops areas. Originality/value The authors use supply chain risks approaches to address the impacts of natural disaster on the implementation of circular economy in agriculture. The authors propose a robust multi-criteria-based methodology to assess the risks of flooding in crops areas and we used to determine the best mitigating projects to face flooding risks on crop areas.

The aim of this paper is to propose a new hybridized framework for analyzing the performance issues of a chemical process plant by utilizing uncertain, imprecise and vague information. In the proposed framework, Fuzzy Lambda–Tau (FLT) approach has been used for computing reliability, availability and maintainability (RAM) parameters of the considered system. Further, for enhancing the RAM characteristics of the system, improved Fuzzy Failure Mode Effect Analysis (FMEA) approach is adopted. Under improved Fuzzy FMEA approach, defined Fuzzy linguistic rating values in the form of triangular and trapezoidal Fuzzy numbers have been assigned by the experts to each risk factor of the listed failure causes. The proposed framework is demonstrated with an industrial application in a chlorine production plant of a chemical process industry. The results show decreasing trend for system availability and deposition of solid Nacl, mechanical failure, corrosion due to wet chlorine, scanty lubrication, improper adsorption and valve malfunctioning are identified as the most critical failure causes for the considered system. A comparative performance analysis between the proposed framework, Fuzzy technique for order of preference by similarity to ideal solution (Fuzzy TOPSIS), Fuzzy evaluation based on distance from average solution (Fuzzy EDAS) and Fuzzy Vlse Kriterijumska Optimizacija I Kompromisno Resenje (Fuzzy VIKOR) are then carried out to show the competence of the proposed framework. It is expected that the analytical results would be highly useful in formulating an optimal maintenance policy for such complex systems and may also be used for improving performance of similar plants.

Supplier evaluation is a complex multi-criteria decision-making (MCDM) problem that deals with assessment of suppliers as the potential alternatives against various types of criteria. We consider the context where decision makers (DMs) have complete information about the suppliers and criteria. To address the needs of decision makers, a multi-criteria evaluation method named Ranking based on optimal points (RBOP) is developed in this paper. By imitating and simulating human decision-making behavioural patterns, the developed MCDM method selects the best alternative that is closer to what the DM desires. Furthermore, a novel subjective MCDM weighting method s called win-loss-draw (WLD) method is also developed, which is also based on human behavioural pattern. A real case study of domestic cheese brands is considered to apply the developed methods to select the best cheese supplier for an Iranian hypermarket. Compared to other MCDM methods, outputs of the RBOP method show some differences due to the impact of WLD method, which intensified divergence and optimal points during the decision-making process.

BackgroundThis study aims to analyze the dynamics of the published articles and preprints of Covid-19 related literature from different scientific databases and sharing platforms.MethodsThe PubMed, Elsevier, and Research Gate (RG) databases were under consideration in this study over a specific time. Analyses were carried out on the number of publications as (a) function of time (day), (b) journals and (c) authors. Doubling time of the number of publications was analyzed for PubMed “all articles” and Elsevier published articles. Analyzed databases were (1A) PubMed “all articles” (01/12/2019-12/06/2020) (1B) PubMed Review articles (01/12/2019-2/5/2020) and (1C) PubMed Clinical Trials (01/01/2020-30/06/2020) (2) Elsevier all publications (01/12/2019-25/05/2020) (3) RG (Article, Pre Print, Technical Report) (15/04/2020–30/4/2020).FindingsTotal publications in the observation period for PubMed, Elsevier, and RG were 23000, 5898 and 5393 respectively. The average number of publications/day for PubMed, Elsevier and RG were 70.0 ±128.6, 77.6±125.3 and 255.6±205.8 respectively. PubMed shows an avalanche in the number of publication around May 10, number of publications jumped from 6.0±8.4/day to 282.5±110.3/day. The average doubling time for PubMed, Elsevier, and RG was 10.3±4 days, 20.6 days, and 2.3±2.0 days respectively. In PubMed average articles/journal was 5.2±10.3 and top 20 authors representing 935 articles are of Chinese descent. The average number of publications per author for PubMed, Elsevier, and RG was 1.2±1.4, 1.3±0.9, and 1.1±0.4 respectively. Subgroup analysis, PubMed review articles mean and median review time for each article were and 13.9 days respectively; and reducing at a rate of-0.21 days (count)/day.InterpretationAlthough the disease has been known for around 6 months, the number of publications related to the Covid-19 until now is huge and growing very fast with time. It is essential to rationalize the publications scientifically by the researchers, authors, reviewers, and publishing houses.FundingNone

Multi-attribute decision-making (MADM) methods represent reliable ways to solve real-world problems for various applications by providing rational and logical solutions. In reaching such a goal, it is expected that MADM methods would eliminate inconsistencies like rank reversal issues in a given solution. In this paper, an endeavor is taken to put forward a new MADM method, called RAFSI (Ranking of Alternatives through Functional mapping of criterion sub-intervals into a Single Interval), which successfully eliminates the rank reversal problem. The developed RAFSI method has three major advantages that recommend it for further use: (i) its simple algorithm helps in solving complex real-world problems, (ii) RAFSI method has a new approach for data normalization, which transfers data from the starting decision-making matrix into any interval, suitable for making rational decisions, (iii) mathematical formulation of RAFSI method eliminates the rank reversal problem, which is one of the most significant shortcomings of existing MADM methods. A real-time case study that shows the advantages of RAFSI method is presented. Additional comprehensive analysis, including a comparison with other three traditional MADM methods that use different ways for data normalization and testing the resistance of RAFSI method and other MADM methods to rank the reversal problem, is also carried out.

There has been a paradigm shift in the automobile industry due to e-mobility which reduces green-house gas emission and air pollution. In this context, selection of the most feasible automotive passenger vehicle is a complex decision-making problem due to the use of different power source, technology, specification and price. In this paper, five alternative vehicles based on fuel cell, hybrid electric, battery electric, plug in hybrid electric and compressed natural gas bi-fuel are evaluated using an integrated criteria importance through inter-criteria correlation (CRITIC) - Combined Compromise Solution (CoCoSo) method. CRITIC method is used to obtain the weights of the vehicle selection criteria, whereas, CoCoSo method is employed to rank the vehicles considering different technical and operational criteria such as greenhouse gas emission, fuel economy, vehicle range, accelerating time, annual fuel cost and vehicle base model cost. It is found that battery electric vehicle out performs all other considered alternatives. The validity of the results is verified by comparing with other well popular MCDM methods. Further, a sensitivity analysis is conducted by changing the criteria weights to establish the stability of the model.

The decision-making in energy sector involves finding a set of energy sources and conversion devices to meet the energy demands in an optimal way. Making an energy planning decision involves the balancing of diverse ecological, social, technical and economic aspects across space and time. Usually, technical and environmental aspects are represented in the form of multiple criteria and indicators that are often expressed as conflicting objectives. In order to attain higher efficiency in the implementation of renewable energy (RE) systems, the developers and investors have to deploy multi-criteria decision-making techniques. In this paper, a novel hybrid Decision Making Trial and Evaluation Laboratory and analytic network process (DEMATEL-ANP) model is proposed in order to stress the importance of the evaluation criteria when selecting alternative REs and the causal relationships between the criteria. Finally, complex proportional assessment and weighted aggregated sum product assessment methods are used to assess the performances of the REs with respect to different evaluating criteria. An illustrative example from Costs assessment of sustainable energy systems (CASES) project, financed by European Commission Framework 6 programme (EU FM 6) for EU member states is presented in order to demonstrate the application feasibility of the proposed model for the comparative assessment and ranking of RE technologies. Sensitivity analysis, result validation and critical outcomes are provided as well to offer guidelines for the policy makers in the selection of the best alternative RE with the maximum effectiveness. European Commission Framework 6 programme (EU FM 6) No data JCR 2018 0.636 SJR (2018) Q2, 38/200 Environmental Engineering, 122/350 Management, Monitoring, Policy and Law; Q3, 55/119 Environmental Chemistry No data IDR 2018 UEM

Evolution of supply chain management (SCM) in recent years has transformed it beyond the simple logic of benefit and economic point of views. One of such key strategic elements in establishing a sustainable and socially responsive SCM is supplier selection and performance assessment. This study brings forward a sustainable supplier evaluation structure under multiple criteria and interval valued fuzzy neutrosophic (IVFN) model. The proposed structure uses CRiteria Importance Through Inter -criteria Correlation (CRITIC) and combined compromised solution (CoCoSo) under IVFN environment for evaluation and selection of suppliers for a dairy company in Iran. Alternative supplier 5 (S5) emerges as the best supplier with the highest overall score (1.168). Average Spearman rank correlation coefficient between the proposed model and other well established decision-making models methods is found as 0.9651 which establishes reliability of model outcomes. The estimated zero value of Gini Index indicates that supplier 5 has a constant ranking in all considered methods. This is a practical sustainable supplier selection platform which allows decision makers in procurement and SC industries to select the most suitable supplier in any pre-determined period.

In the logistics world, special attention should be given to warehousing systems, cost rationalization, and improvement of all the factors that affect efficiency and contribute to smooth functioning of logistics subsystems. In real time industrial practice, the issue of evaluating and selecting the most appropriate forklift involves a complex decision-making problem that should be formulated through an efficient analytical model. The forklifts efficiency plays a very important role in the company. The forklifts are being used on a daily basis and no logistical processes could be done without them. Therefore, it has been decided to determine their efficiency, which will contribute to the optimization of the process in this logistics subsystem. This study puts forward an integrated forklift selection model using Data Envelopment Analysis (DEA), Full Consistency Method (FUCOM) and Measurement Alternatives and Ranking According to the Compromise Solution (MARCOS) methods. Five input parameters (regular servicing costs, fuel costs, exceptional servicing costs, total number of all minor accidents and damage caused by forklifts) and one output parameter (number of operating hours) were first identified to assess efficiency of eight forklifts in a warehousing system of the Natron-Hayat company using the DEA model. This step allows sorting of efficient forklifts which are subsequently evaluated and ranked using FUCOM and MARCOS methods. A sensitivity analysis is also performed in order to check reliability and accuracy of the results. The findings of this research clearly show that the proposed decision-making model can significantly contribute to all spheres of business applications.

Oil in casting industries is one of the major sources of environmental pollution as its burning directly results in the emission of toxic gases such as carbon monoxide (CO), carbon dioxide (CO2), nitrogen dioxide (NO2), sulphur dioxide (SO2) and suspended particulate matters (SPM). The aim of this work is to select the best sustainable oil for a cleaner and environment friendly production system in small scale casting industries of Agra city located in India. Performance, quality and cost are considered as the most influential parameters, while jatropha, vegetable oil, karanja oil and light diesel oil are considered as the alternatives. A novel integrated three phase decision-making framework is proposed, encompassing fuzzy analytical hierarchy process (FAHP), fuzzy technique for order of preference by similarity to ideal solution (FTOPSIS) and fuzzy evaluation based on distance from average solution (FEDAS) methods. FAHP is implemented for computing weights of the considered criteria and sub-criteria. These weights are subsequently used in FTOPSIS and FEDAS methods to derive ranking of the considered oils. Light diesel oil emerges out as the best oil under the considered criteria with the highest closeness coefficient (0.9807) and appraisal score (0.6683) values. For validating the results of the proposed decision framework, sensitivity analysis (SA) has been performed. From the twelve experiments of SA, it is observed that light diesel oil retained its highest score in nine experiments which substantiate its top priority as well as FTOPSIS and FEDAS-based results.

Supplier evaluation and selection is a significant strategic decision for reducing operating costs and improving organizational competitiveness to develop business opportunities. Moreover, with increasing concern towards environmental protection and sustainable development, it becomes important to pay more attention to environmental requirements and evaluating the potential suppliers by incorporating green factors into the selection process. Thus, the aim of this paper is to put forward an integrated approach for green supplier selection by considering various environmental performance requirements and criteria. The proposed approach addresses the inter-relationships between the customer requirements (CRs) with the aid of decision-making trial and evaluation laboratory (DEMATEL) method while constructing a relationship structure. Quality function deployment (QFD) model is used to establish a central relationship matrix in order to identify degree of relationship between each pair of supplier selection criteria and CRs. Finally, complex proportional assessment (COPRAS) applied to prioritize and rank the alternative suppliers. A case study is presented to reveal the potentiality and aptness of the proposed methodology. Sin financiación 5.651 JCR (2017) Q1, 6/33 Green and Sustainable Science and Technology, 7/50 Engineering, Environmental, 21/241 Environmental Sciences UEM

Consumer preferences in sustaining and designing a product are a vital driver in a company&rsquo, s long-term strategy. In a supply chain management (SCM), realizing, configuring and analyzing consumer point of view and making sure the product is highly fitted to the consumer dimensions are essential responsibilities. For this purpose, a sustainable supply chain (SSC) can define a platform in order to reach consumer satisfaction. This paper examines the utility and factors related to the use of a phone in the market incorporating sustainable attributes. We firstly identify main factors and indicators that influence the selection of a sustainable phone. Thereafter, we propose decision analysis tools as decision-making trial and evaluation laboratory (DEMATEL) and analytical hierarchy process (AHP) for the realization of the cause, effect, and interrelation of the indicators. The comparisons between them report a high similarity while best and worst indicators are in the same positions. Best worst method (BWM) is then formulated in order to achieve optimal ranking and to express the importance. Counting on this information is of special relevance in marketing decision-making, where companies must look for competitive advantages prioritizing its product attributes, attending both to resources and to consumer preferences. For this project, we invited six experts in various areas (information science, consumer organizations, fair trade, public administration-cooperation office and telecommunication) to participate and fill the questionnaires. The results are analyzed by market experts in terms of comparison and conformity.

This paper deals with the modeling and optimization of a bi-level multi-objective production planning problem, where some of the coefficients of objective functions and parameters of constraints are multi-choice. A general transformation technique based on a binary variable has been used to transform the multi-choices parameters of the problem into their equivalent deterministic form. Finally, two different types of secularization technique have been used to achieve the maximum degree of individually membership goals by minimizing their deviational variables and obtained the most satisfactory solution of the formulated problem. An illustrative real case study of production planning has been discussed and, also compared to validate the efficiency and usefulness of the proposed work.

Information management is a common paradigm in modern decision-making. A wide range of decision-making techniques have been proposed in the literature to model complex business processes. In this Special Issue, 16 selected and peer-reviewed original research articles contribute to business information management in various current real-world problems by proposing crisp or uncertain multiple-criteria decision-making (MCDM) models and techniques, mostly including multi-attribute decision-making (MADM) approaches in addition to a single paper proposing an interactive multi-objective decision-making (MODM) approach. The papers are mainly concentrated in three application areas: supplier selection and rational order allocation, the evaluation and selection of goods or facilities, and personnel selection/partner selection. A number of new approaches are proposed that are expected to attract great interest from the research community., This article belongs to the Special Issue Multiple-Criteria Decision-Making (MCDM) Techniques for Business Processes Information Management

This paper endeavors to measure the consistency of a decision-making tool, popularly known as Best Worst Method (BWM), which is one of the latest developments in multiple-criteria decision analysis (MCDA). BWM is finding a vast arra of applications in the literature. Several investigators have extended this tool. BWM measures the weight of decision-making criterion and is recognized as a subjective decision tool. The first step in this method is to find the best and worst criterion, while we suppose several experts are asked to present their evaluation over set of criteria. The aim is to measure how these judgments are consistent and reliable. So, we statistically (using χ2 distribution) add a pre-evaluation to experts’ opinion and analyze whether the agreement of experts’ opinions is satisfactory and group opinion is established. This action improves the quality of the decision-making process by incorporating the reliability evaluation of experts’ idea. This extension for BWM helps decision makers in facilitating and getting results that are more consistent for criteria evaluation. We present examples in sustainable construction and architecture project.

Purpose – sustainability in industrial organizations is becoming one of the predominant concepts in the context of modern industrialization due to global warming, economic significance, and social awareness. These have prompted a huge concern toward sustainable supply chain management (SSCM) to be adopted and promoted as an innovative business model. Supplier evaluation and selection play a significant role in SSCM for taking appropriate procurement decisions. Research methodology – a hybrid MADM model based on Best Worst Method (BWM) and Combined Comprise Solution (CoCoSo) method. Findings – a case study in the steel industry is presented to demonstrate the effectiveness of the proposed approach. The results show the potentiality of the proposed model in resolving complex sustainability issues in the SCM environment. Research limitations – other weighting techniques like the analytic network process (ANP) and decision making trial and evaluation laboratory ( DEMATEL ) approaches can also be combined and performances can be compared. Practical implications – the proposed model can be used by the organizations to select the most appropriate suppliers who contribute to the movement of the SC towards sustainability. Originality/Value – a multi-criteria evaluation model has been proposed for solving a sustainable supplier selection problem while considering economic, environmental and social criteria simultaneously by integrating BWM-COCOSO methods. DOI: https://doi.org/10.3846/cibmee.2019.081

In the present work, multi-response optimization of electro-discharge machining (EDM) process is carried out based on an experimental analysis of machining superalloy Inconel-718. The study aims at optimizing and determining an optimal set of process variables, namely discharge current (), pulse-on duration () and dielectric fluid-pressure () for achieving optimal machining performance in EDM. Nine independent experiments based on L9 orthogonal array are carried out by using tungsten as the electrode. The productivity performance of the EDM process is measured in terms of material removal rate (MRR) and its cost parameter is measured in terms of tool wear rate (TWR) and electrode wear rate (EWR). The TOPSIS is used in conjunction with five different criterion weight allocation strategies— (namely, mean weight (MW), standard deviation (SDV), entropy, analytic hierarchy process (AHP) and Fuzzy). While MW, SDV and entropy are based on the objective evaluation of the decision-maker (DM), the AHP can model the DM’s subjective evaluation. On the other hand, the uncertainty in the DM’s evaluation is analyzed by using the fuzzy weighing approach.

Logistics centers are those areas where all the national and international logistics and transportation operations are managed and directed to various business operations. One of the essential elements of an urban development system is to identify the appropriate location for a logistics center. In practice, the issue of evaluating and selecting the most suitable geographical area for a logistics center is considered as a complex decision making problem that can be well formulated through analytical and mathematical models. An exhaustive review of literature indicates that no concrete study has still proposed any integrated evaluation approach for logistics center selection. Thus, this paper aims in developing a two-stage decision making model to find out the most preferred zone in the autonomous communities of Spain for establishment of logistics centers. In the first stage, the considered communities are compared based on five evaluation criteria using data envelopment analysis (DEA) to identify the efficient and inefficient alternatives. In the second stage, a model is designed to evaluate the performance of the efficient communities using rough full consistency (R-FUCOM) and combined compromise solution (R-CoCoSo) methods. The adopted model allows capturing the uncertainty and vagueness in the decision makers’ judgments as involved in the evaluation process with the use of rough set theory (RST). The R-FUCOM method is utilized to obtain the optimal weights of the criteria, while R-CoCoSo method is finally used to rank the efficient communities. In addition, sensitivity analyses are performed to validate the robustness of the derived results.

Healthcare waste disposal management is one of the biggest day-to-day challenges faced by healthcare providers and urban municipalities. Poor management of healthcare waste can cause serious problems for healthcare workers, patients, and the general public. Healthcare providers and urban planners usually struggle with the action of locating an appropriate waste disposal center in a municipal area. Healthcare waste disposal location planning is a difficult task due to complexities inherent in the evaluation of alternative locations according to multiple and often competing criteria. We propose a new best-worst method with interval rough numbers (IRN) for healthcare waste disposal location decisions. A new IRN Dombi-Bonferroni (IRNDBM) means the operator is also introduced to process the rough data because of the unavailability of precise information. A case study at a private hospital in Madrid is presented to demonstrate the applicability and exhibit the efficacy of the proposed multi-criteria evaluation method.