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Substantial R &D efforts are currently directed towards the development of combined heat and power (CHP) systems that automatically and seamlessly connect to the power grid. In this paper we develop a real options model to assess the impact that the operational flexibility characterizing such systems will have on the optimal timing and capacity associated with investments in CHP plants. We take the viewpoint of a manufacturer operating in an energy-intensive industry who contemplates investing in CHP. We discuss and compare investments in two types of CHP systems: a standard one that is operationally rigid and a technologically advanced one that is operationally flexible. The interaction between temporal and operational flexibility under uncertainty and irreversibility is central to our analysis. We show that operational flexibility guarantees earlier investment but has an ambiguous effect in terms of capacity. In particular, when operational flexibility is very valuable the potential investor is opting for investing in a plant with larger productive capacity. The potential investor chooses a smaller CHP unit if otherwise. A numerical exercise calibrated using data from the Italian pulp and paper and electricity industries complements our theoretical analysis. [ABSTRACT FROM AUTHOR]

Wang, and J.-T. Teng in their paper develop and compare the seller's profit per unit time under each of three payment methods - advance, cash, and credit. In their paper, S. Sarkar, S. Tiwari, and B. C. Giri present a continuous-review vendor-buyer supply chain (SC) model and uncovers the best policy that minimizes the system's total expected cost. The authors investigate the contracts that the manufacturer offers to the retailers and finds the optimal pricing and ordering decisions made by retailers and the best contract that includes maximum profit for the supply chain. The analysis of large-scale data generated by humans, online activity in different web and social platforms is a challenging problem faced by researchers, practitioners, and academicians. [Extracted from the article]

In the paper entitled "Stochastic efficiency and inefficiency in portfolio optimization with incomplete information: a set-valued probability approach", the authors extend the notion of stochastic efficiency and inefficiency in portfolio optimization to the case of incomplete information by means of set-valued probabilities. The field of multiple objective optimization was for a long time lead mainly by applied mathematics and computer science researchers. In the paper entitled "Interactive neutrosophic optimization technique for multiobjective programming problems: an application to pharmaceutical supply chain management", the author introduces neutrosophic optimization models under the neutrosophic fuzzy environment. [Extracted from the article]

The paper studies product bundling in a duopoly supply chain network under the influence of different power-balance structures, bundling decisions and advertising efforts on total supply chain profit. Mathematical models comprising two manufacturers and a single retailer are developed to capture the impact of bundling policy and advertisement strategy under three power-balance structures, namely Manufacturer Stackelberg, Retailer Stackelberg and Vertical Nash. Following game theory models and numerical examples, the study found that the total profit of the supply chain is undifferentiated under the manufacturer Stackelberg and Vertical Nash case in the manufacturer bundling and retailer bundling strategies. However, total supply chain profit under manufacturer bundling strongly dominates under retailer bundling in Retailer Stackelberg and Vertical Nash, and remains valid under multiple settings of market size, price elasticity and advertising elasticity. It is also found that manufacturer bundling is significantly affected by advertising effort compared to retailer bundling. The study contributes to the literature interfacing supply chain and marketing by studying bundling policy and advertising strategy simultaneously for homogenous products, under various power-balance structures and price competition. [ABSTRACT FROM AUTHOR]

The theoretical soccer performance dimensions are based on a set of 29 players' attributes periodically produced by Electronics Arts (EA) Sports experts. Data and big data in sports are being collected and analyzed, with the integration of physical and digital sources, increasing the knowledge of professional sports for all parties involved. Player assessment is carried out using recursive feature elimination with random forests, in which context relevant player metrics are considered and the selection of players is based on a modified genetic algorithm. American Football In the paper "Sports analytics in the NFL: classifying the winner of the superbowl", Y.F. Roumani shows an application of sports analytics in the National Football League. [Extracted from the article]

Anomaly detection of three-dimensional (3D) topographic data is a challenging problem in spatial data analysis. In this paper, we investigate spatial patterns of 3D surface data that exhibit multiple in-control modes. In complex manufacturing processes, surfaces of final products could contain different topographic features from one in-control surface to another, thus making it difficult to monitor the surface with existing approaches, which rely on the assumption of the presence of single mode surface topography. We propose a novel anomaly detection approach for monitoring local topographic variations in the presence of multimode surface topography. We present a binarization model to capture the generic behavior of the multimode surfaces and enhance the representation of the surface. To systematically monitor the surface, we introduce a new probabilistic distance measure (PDM) that quantifies the similarity of spatial patterns between two binarized surfaces. The proposed PDM takes advantage of identifying local variations by utilizing the order neighbor statistics, which captures the local property on the surface. Experimental results with numerical simulation data and real-life paper surface data are provided to demonstrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

The paper presents a bibliographic analysis of multicriteria sorting and clustering methods, highlighting influential studies in these areas, identifying the status in the field, and pointing recent research developments and trends. A wide range of decision-making problems in various areas of engineering and management require the assignment of a set of decision options (alternatives) to categories/classes or groups. [Extracted from the article]

In today's competitive environment, industries are required to increase reliability and safety level of their equipment with reasonable cost. Consequently, it is essential to select the appropriate maintenance strategy. In the paper industry, pumping of cooked pulp requires abrasion- and sometimes corrosion-resistant pumps that are able to handle up to 70 % solids content. But the maintenance engineer and supervisor often face failures of the pump. Hence, this article describes the application of multi criteria decision-making (MCDM) technique for the selection of optimum maintenance strategy for pumps used in the paper industry. The proposed hybrid MCDM model comprises fuzzy analytical hierarchy process (FAHP), grey relational analysis (GRA), and technique for order preference by similarity to ideal solution (TOPSIS) technique. The FAHP is used to compute the criteria weights whereas GRA-TOPSIS is used for determining the ranking of alternatives. This study discusses four maintenance strategies: corrective maintenance, predictive maintenance, time-based preventive maintenance, and condition-based maintenance. Four main criteria-safety, cost, added value, and feasibility-have been used to evaluate the optimum maintenance strategy. [ABSTRACT FROM AUTHOR]

Every year agribusinesses develop and market new seed varieties with traits desirable for different planting environments. When agribusinesses experiment the new varieties at different farms, data is generated about the performance of these new seed varieties. However, farmers do not have a decision support tool to process the vast amount of yield performance data to make an informed seed variety selection decision for their farm. An informed decision requires accurate estimation of yield performances of seed varieties on the targeted farmland and balancing trade-offs between the expected yield and the risk associated with the seed varieties selected to grow. This research uses a real data set provided by Syngenta—an agribusiness—to create a decision-support tool. The data set used in this research contains yield information of different soybean varieties experimented at different farms located in the Midwest of the US, as well as information on location, soil, and weather conditions prevailing in those farms. In addition to this data, we also surveyed soybean farmers to understand their preferences and current practices in choosing seed varieties to grow in their farms. We are the first to capture and document farmers' preferences and practices in selecting and growing soybean varieties. The data collected from the survey enabled us to compare the results emerging from the proposed methodology with the status quo practices. Using the Syngenta data and survey responses, this paper proposes an analytics framework that integrates machine learning, clustering, simulation, and portfolio optimization to optimally select soybean varieties to grow at the target farm. We choose a machine learning model, which simulates the yield performance of soybean varieties under different plausible weather scenarios derived from the neighborhood of the target farm. The simulated yields are then used to estimate parameters in a portfolio optimization formulation that selects the optimal portfolio of seed varieties to grow at the target farm. The main methodological contribution of this research is in the development of an approach that integrates machine learning, clustering, simulation, and portfolio optimization to help farmers make an important decision. Specifically, we introduce a novel data-driven simulation-based approach to estimate the parameters needed to solve a portfolio optimization problem. Our analysis indicates that an average farmer will gain as much as $177,369 per year in revenue by utilizing the analytics framework introduced in this research. The methodology developed in this research can be applied to variety selection decisions for other crops and influence farming practice positively. By embracing the machine learning and analytics powered framework introduced in this paper, agribusinesses can position themselves as the innovation leader and create business value by unleashing the potential of the scientific discoveries of agronomy to offer tailored farming decision support to individual farmers. [ABSTRACT FROM AUTHOR]

With the development of economic globalization and increasing international trade, the maritime transportation system (MTS) is becoming more and more complex. A failure of any supply line in the MTS can seriously affect the operation of the system. Resilience describes the ability of a system to withstand or recover from a disaster and is therefore an important method of disaster management in MTS. This paper analyzes the impact of disasters on MTS, using the data of Suez Canal "Century of Congestion" as an example. In practice, the severity of a disaster is dynamic. This paper categorizes disasters into different levels, which are then modelled by the Markov chain. The concept of a repair line set is proposed and is determined with the aim to minimize the total loss and maximize the resilience increment of the line to the system. The resilience measure of MTS is defined to determine the repair line sequence in the repair line set. Finally, a maritime transportation system network from the Far East to the Mediterranean Sea is used to validate the applicability of the proposed method. [ABSTRACT FROM AUTHOR]

The importance of linear programming, and especially mixed-integer linear programming, has increased over time as computers have gotten more capable, algorithms have improved and more software solutions have become available. This approach has been successfully applied for solving hard problems in operations research (like traveling salesman problem, routing problems and telecommunications network design problems). In the eighteenth paper, "Exact method for the two-machine flow-shop problem with time delays" Mkadem, Moukrim and Serairi propose an exact method for solving the two-machine flow-shop problem with time delays. [Extracted from the article]

The COVID-19 global health crisis forced border closings, strained resources and tightened funding, forcing humanitarian organisations to innovate. This paper aims to identify gaps in the literature on innovation in humanitarian supply chains, and to develop an appropriate framework for future research through a systematic literature review. We use a systematic literature review approach and synthesis the discussion of innovation in humanitarian supply chains after reviewing 43 papers. The synthesis identifies the different contexts for and outcomes of innovation in humanitarian supply chains. Our findings indicate that research on innovation in humanitarian supply chains is an underdeveloped topic. Gaps we identified in regards to the humanitarian context are: (1) a limited discussion of the contribution by the beneficiary to the supply chain; (2) a limited discussion of reconstruction innovations; (3) a lack of study on field application for complex innovations; (4) the lack of discussion of the role of individual knowledge in humanitarian supply chain innovation and finally (5) a lack of study of position innovations where humanitarian organisations use supply chains as a way to market effectively towards donors. [ABSTRACT FROM AUTHOR]

In recent years, emerging technologies have gained popularity and being implemented in different fields. Thus, critical leading-edge technologies such as artificial intelligence and other related technologies (blockchain, simulation, 3d printing, etc.) are transforming the operations and other traditional fields and proving their value in fighting against unprecedented COVID-19 pandemic outbreaks. However, due to this relation's novelty, little is known about the interplay between emerging technologies and COVID-19 and its implications to operations-related fields. In this vein, we mapped the extant literature on this integration by a structured literature review approach and found essential outcomes. In addition to the literature mapping, this paper's main contributions were identifying literature scarcity on this hot topic by operations-related fields; consequently, our paper emphasizes an urgent call to action. Also, we present a novel framework considering the primary emerging technologies and the operations processes concerning this pandemic outbreak. Also, we provided an exciting research agenda and four propositions derived from the framework, which are collated to operations processes angle. Thus, scholars and practitioners have the opportunity to adapt and advance the framework and empirically investigate and validate the propositions for this and other highly disruptive crisis. [ABSTRACT FROM AUTHOR]

The paper examines whether the structure of the risk factor disclosure in an IPO prospectus helps explain the cross-section of first-day returns in a sample of Chinese initial public offerings. This paper analyzes the semantics and content of risk disclosure based on an unsupervised machine learning algorithm. From both long-term and short-term perspectives, this paper explores how the information effect and risk effect of risk disclosure play their respective roles. The results show that risk disclosure has a stronger risk effect at the semantic novelty level and a more substantial information effect at the risk content level. A novel aspect of the paper lies in the use of text analysis (semantic novelty and content richness) to characterize the structure of the risk factor disclosure. The study shows that initial IPO returns negatively correlate with semantic novelty and content richness. We show the interaction between risk effect and information effect on risk disclosure under the nature of the same stock plate. When enterprise information transparency is low, the impact of semantic novelty and content richness on the IPO market is respectively enhanced. [ABSTRACT FROM AUTHOR]

The 2017 International Conference on Multiple Objective Programming and Goal Programming (MOPGP 2017) has been organized to disseminate recent theoretical and methodological developments, significant technical applications, case studies and survey results in the related area. Multiple Objective Programming (MOP), also known in literature as multi-objective optimization, vector optimization, multicriteria optimization, multiattribute optimization or Pareto optimization, is a subarea of Multiple Criteria Decision Making (MCDM) or Multiple Criteria Decision Analysis (MCDA) and it is concerned with mathematical optimization problems involving more than one objective function to be optimized simultaneously. If we suppose that HT ht is ordered by the classical Pareto cone HT ht , then HT ht if HT ht . [Extracted from the article]

Business operations have entered the digital era in which artificial intelligence (AI), machine learning (ML) and blockchain (BKC) have emerged as major disruptive forces. In AI and ML, deep learning is a critical area. In this paper, we aim to investigate how deep learning and BKC together can help improve business operations. We first examine the operations research (OR) literature related to the applications of deep learning for business operations. Then, we discuss the prior studies on using BKC for operations. After that, we explore deep learning's applications for BKC, BKC's applications for deep learning as well as how deep learning and BKC have been used together for business operations. Then, we construct a research framework and propose future research directions. [ABSTRACT FROM AUTHOR]

In the context of Industry 4.0 (I4.0) and Industry 5.0 (I5.0), a supply chain (SC) is digital and dynamic with a multi-structural framework, and should be resilient in the case of an abrupt disruption. To uncover SC characteristics under this environment, this paper proposes a five-layer interdependent supply chain network (SCN) to depict this multi-structural framework of a SC, then analyzes the topological features and dynamic evolution process of the multilayer SCN. To explore the impact of SCN structure on mitigating disruption propagation, this study presents the risk propagation mechanism in the multilayer SCN. Simulations considering four risk scenarios are carried out based on the Tesla SCN and its dynamic evolution to reveal the resilience of the multilayer SCN for mitigating the ripple effect of disruption propagation. Results show that (1) the ripple effect caused by node failure differs from that caused by the failure of the sub-network layer, which is more serious and significantly different with different network structures of SCN; (2) under the same SCN structure, different sub-networks have different impacts on the entire SCN in terms of mitigating the ripple effect; (3) in the five-layer SCN proposed in this study, product and technological structures are crucial for maintaining the stability of the SCN. Organizational, financial, and information structures are most unstable elements in the SCN. Once the nodes in these three structures fail, risk easily spreads through the entire SCN. Suggestions are provided to encourage a fresh approach to improving the resilience of the SCN. [ABSTRACT FROM AUTHOR]

In the Industry 4.0 era, manufacturers commonly adopt digital technologies such as Internet of Things (IoT) and blockchain technology to improve information disclosure. In this paper, we focus on how the government can properly set an extended producer responsibility (EPR) policy (penalty or subsidy) and impose it on the manufacturer to entice the manufacturer to set the optimal pollutant emissions information disclosure (PEID) level which maximizes the social welfare. In the basic model, we build a simple consumer utility based stylized analytical model. We first derive from the government perspective the social welfare function and obtain the closed-form expression of the optimal level of PEID. Then, we explore the manufacturer's problem on PEID under the EPR policy. We show how the EPR policy can be set so that the government can entice the manufacturer to set the PEID level which maximizes the social welfare. We also uncover the factors governing the EPR policy, and show the situations under which the EPR policy is in fact a subsidy scheme (i.e., the government sponsors the manufacturer). To derive more insights and check for robustness, we extend the basic model in a number of ways, namely (i) the situation when the manufacturer is risk sensitive, (ii) the case with the extended consumer responsibility (ECR) policy, (iii) the scenario when there is a per unit PEID level dependent operating cost (i.e., a variable cost), and (iv) the case when the platform is not supported by blockchain. We find that the form of EPR policy and the qualitative insights remain valid for the cases when the manufacturer is risk sensitive and there is a PEID level dependent operating cost. Interestingly, we uncover that, unlike the EPR policy, the use of ECR policy fails to help. [ABSTRACT FROM AUTHOR]

This paper develops a reward model for the optimization of preventive maintenance for a complex production system functioning in any one of k unobservable operating states. The changes of the states are driven by a non-homogeneous Markov (NHM) process X(t) with known characteristics. The system fails according to a point process whose intensity is modulated by the unobservable state. Failures are rectified through minimal repairs (MRs) whose costs are associated with age and the state process X(t). The modeling approach also allows both the revenue stream and the preventive maintenance cost to be characterized by the state process X(t). The paper first formulates the reward model depending on the unobservable state process estimated through the filtering theorem argument by projection on the observed history including failure point process observations. The estimation of the state process allows failure prediction and maximizing revenue stream implemented through scheduling periodic overhauls. A case study is provided to illustrate the proposed method. [ABSTRACT FROM AUTHOR]

In the paper, "An exact algorithm for the edge coloring by total labeling problem", Borghini, Méndez-Díaz, and Zabala address the edge coloring problem, a classical problem in graph theory. The problem is modeled as a mixed integer nonlinear programming problem considering maximum number of orders, capacity, and budget constraints. In the paper, "A two-level evolutionary algorithm for solving the petrol station replenishment problem with periodicity constraints and service choice", Al-Hinai and Triki address the petrol station replenishment problem with periodicity constraints and introduce the frequency service choice as a decision variable. Arpón, Homem-de-Mello, and Pagnoncelli in their paper, "An ADMM algorithm for two-stage stochastic programming problems", show that the Alternate Direction Method of Multipliers (ADMM) can be applied to solve two-stage stochastic programming problems, and propose an implementation in three blocks, with or without proximal terms, that can be used as an algorithm to solve convex problems with a block structure. [Extracted from the article]

2018 saw the 40th anniversary of the publication of the seminal paper on Data Envelopment Analysis (DEA) (Charnes et al. [1]). The participants included, apart from academics researching in the field of DEA, non-academic practitioners using DEA in the course of their employment. Indeed, the conference featured a stream dedicated to non-academic practitioners from the field of Regulation, a major area of use of DEA. [Extracted from the article]

Understanding the data and reaching accurate conclusions are of paramount importance in the present era of big data. Machine learning and probability theory methods have been widely used for this purpose in various fields. One critically important yet less explored aspect is capturing and analyzing uncertainties in the data and model. Proper quantification of uncertainty helps to provide valuable information to obtain accurate diagnosis. This paper reviewed related studies conducted in the last 30 years (from 1991 to 2020) in handling uncertainties in medical data using probability theory and machine learning techniques. Medical data is more prone to uncertainty due to the presence of noise in the data. So, it is very important to have clean medical data without any noise to get accurate diagnosis. The sources of noise in the medical data need to be known to address this issue. Based on the medical data obtained by the physician, diagnosis of disease, and treatment plan are prescribed. Hence, the uncertainty is growing in healthcare and there is limited knowledge to address these problems. Our findings indicate that there are few challenges to be addressed in handling the uncertainty in medical raw data and new models. In this work, we have summarized various methods employed to overcome this problem. Nowadays, various novel deep learning techniques have been proposed to deal with such uncertainties and improve the performance in decision making. [ABSTRACT FROM AUTHOR]

Identifying the root causes of part-to-part variation is a central problem in most six-sigma programs, especially of modern manufacturing processes. This is challenging as the sources and patterns of the variation are often unknown or previously unidentified. A small literature aims to address this problem by discovering unknown, previously unidentified variation sources, in a manner that helps understand their nature, from only a sample of measurement data. However, the common solution of this literature is unideal for this objective in terms of both methodology and metrology aspects. This paper proposes a convolutional generative modeling framework for optical scanning data to address this limitation. The proposed approach can correctly discover the true variation sources and visualize their individual patterns in two manufacturing examples, without any prior knowledge of the variation. The approach also outperforms state-of-the-art methods in these examples. [ABSTRACT FROM AUTHOR]

Identifying purchase intention by analyzing the Query and the Document of the product description (Q &D) text is one of the most important means of promoting Purchase Rate (PR). In view of that customers sometimes cannot describe their purchasing intention in queries, this paper aims to identify purchase intention from implicit queries by computing semantic similarity between Q &D and proposes a novel model based on Word2Vec algorithm, Long Short-term Memory (LSTM) and Deep Structured Semantic Model (DSSM). Besides, an empirical analysis is conducted through the Keras framework and based on the factual retrieval data of the Home Depot, an E-commerce website selling building materials in America. The results show that the proposed model has achieved improving F1-score on test dataset compared with other existing models. The novel model combines Word2Vec and LSTM to extract text features and applies DSSM to further fetch high-dimension representations by maximizing semantic similarity between the user query and the description of the correct merchandise. Our proposed model can be used to remove or minimize subjective factors in extracting features, improves the performance of purchasing intention identification, and also improves the customer experience of online shopping. [ABSTRACT FROM AUTHOR]

Critical chain buffer management (CCBM) has been extensively studied in recent years. This paper investigates a new formulation of CCBM, the multimode chance-constrained CCBM problem. A flow-based mixed-integer linear programming model is described and the chance constraints are tackled using a scenario approach. A reinforcement learning (RL)-based algorithm is proposed to solve the problem. A factorial experiment is conducted and the results of this study indicate that solving the chance-constrained problem produces shorter project durations than the traditional approach that inserts time buffers into a baseline schedule generated by solving the deterministic problem. This paper also demonstrates that our RL method produces competitive schedules compared to established benchmarks. The importance of solving the chance-constrained problem and obtaining a project buffer tailored to the desired probability of completing the project on schedule directly from the solution is highlighted. Because of its potential for generating shorter schedules with the same on-time probabilities as the traditional approach, this research can be a useful aid for decision makers. [ABSTRACT FROM AUTHOR]

The integration between blockchain and artificial intelligence (AI) has gained a lot of attention in recent years, especially since such integration can improve security, efficiency, and productivity of applications in business environments characterised by volatility, uncertainty, complexity, and ambiguity. In particular, supply chain is one of the areas that have been shown to benefit tremendously from blockchain and AI, by enhancing information and process resilience, enabling faster and more cost-efficient delivery of products, and augmenting products' traceability, among others. This paper performs a state-of-the-art review of blockchain and AI in the field of supply chains. More specifically, we sought to answer the following three principal questions: Q1—What are the current studies on the integration of blockchain and AI in supply chain?, Q2—What are the current blockchain and AI use cases in supply chain?, and Q3—What are the potential research directions for future studies involving the integration of blockchain and AI? The analysis performed in this paper has identified relevant research studies that have contributed both conceptually and empirically to the expansion and accumulation of intellectual wealth in the supply chain discipline through the integration of blockchain and AI. [ABSTRACT FROM AUTHOR]

Big Data brings about a mix of opportunities for and challenges to the development of intelligent transportation and logistics industries. Incorporating the tremendous trove of data and data analytics into operations research greatly increases capabilities to solve real-world complex problems. "DEA under big data: data enabled analytics and network data envelopment analysis" by Zhu, proposes that data envelopment analysis (DEA) should be viewed as a method for data-oriented analytics in performance evaluation and benchmarking, and demonstrate that network DEA can be different from conventional DEA in many aspects. [Extracted from the article]

With increasing pressure to cut carbon emissions and develop sustainability plans, companies need carbon credits to offset emissions that cannot be eliminated from their operations, new global carbon exchange to launch in Singapore by 2021 end, the high-quality credit can bridge this gap and play an important role in the overall climate change mitigation strategy. The purpose of this research is to use the production data (including carbon emissions) of paper-making companies in Taiwan to establish a circular economy mathematical programming model and the concept based on the activity cost method (ABC) to explore the impact of different carbon emissions costs (such carbon tax, carbon cap-and-trade, etc.) on the company's production structure and profitability impact. The research results show that different carbon emission cost models have different effects on the company's optimal product-mix and profitability. The managerial implication is to combine the extensive application of the carbon emission credit mechanism, which can offset the carbon emissions in the production process and have a significant impact on the company's sustainable competitiveness. In addition, this study can also enable decision makers to understand the impact of different carbon emission cost models on the profitability of the company's product mix, which can be used as a reference for production planning decisions. [ABSTRACT FROM AUTHOR]

Purchasing occupies a strategic role in supply chain management for a firm and is the driver of competitive advantage. Owing to the high purchase cost to revenue ratio, decisions such as evaluation, selection, and performance management of suppliers are of the matter of immense interest to firms. Multi-criteria decision making tools allow the purchasing managers to evaluate the suppliers holistically. One such tool, data envelopment analysis (DEA) has been used extensively for supplier evaluation and selection. This paper presents a comprehensive review of 161 articles published since 2000, on the application of DEA in supplier selection. These articles are located from the Scopus database. With little existing literature on a full-fledged review, this work envisages to be first of its kind, by aiding DEA practitioners in purchasing function. The analysis of the study indicates the emergence of the theme of green supply chain and sustainability in recent years as well as the adoption of hybrid approaches to solving the problem of supplier selection using DEA. The paper presents various classifications of DEA methods based on input criteria, sectors of application, and industry-wide case studies, which can be used as a quick reckoner by an academician or a purchasing manager. [ABSTRACT FROM AUTHOR]