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The carbon quota allocation rules of China's pilot carbon emissions trading (CET) regions are various, which mainly include benchmarking, historical carbon intensity reduction and auctioning. When the allocation rules change, it is unresolved how to achieve the optimal product prices and effectively allocate the carbon emissions reduction profits of CET-covered enterprises in the cooperative supply chain. Thus, this paper uses the Stackelberg game, Nash equilibrium and the Shapley value based on cost modification to investigate these issues. The results indicate that: (1) The increasing carbon prices can always improve the retail prices only under the auctioning rule. Meanwhile, the growing low-carbon awareness of consumer cannot be always conducive to improving the wholesale and retail prices, and the similar product prices of non-CET-covered enterprises have greater impact on the wholesale prices than that on the retail prices. (2) Only under the free carbon quota allocation rules, can the optimal wholesale and retail prices under the Stackelberg game be always higher than those under the Nash equilibrium. Meanwhile, the auctioning rule can better reduce carbon emissions than the free allocation rules. (3) Improving carbon emissions reduction contribution and emission reduction costs can be conducive to increasing the carbon emission reduction profits of the supplier and retailer, while the impact of carbon emission reduction contribution on improving the carbon emission reduction profits is not always greater than that of the carbon emission reduction costs. [ABSTRACT FROM AUTHOR]

Alongside many research studies that have been presented in the open innovation domain and smart manufacturing systems, there is a research gap on integrating the outbound individual capabilities with the new smart manufacturing machines to satisfy the customers' varied and uncertain requirements. In this paper, Open Supply Chain Management (OSCM) is conceptualized as a new paradigm in the evolution of SCM. Companies can benefit from integrated physical and conceptual resources to promote efficiency and flexibility throughout the supply chain's main processes, including supplying, manufacturing, distributing, and marketing. The OSCM concept is undergoing several drivers including crowdsourcing, open innovation, Industry 4.0, cloud manufacturing, Internet of Things (IoT), big data, and the digital twin that appeared in the last decades. To validate OSCM in practice, a subset of this concept is investigated to incorporate the designing process with supply chain production planning applying a digital twin network. Additionally, dealing with epistemic uncertainty, a fuzzy tactical planning model is developed, and to study the developed model in more detail, an industrial study in the clothes manufacturing industry is employed. The results illustrate that the products' designing cost consists of only 2% of the supply chain total cost. [ABSTRACT FROM AUTHOR]

This paper investigates a green supply chain (GSC) consisting of one manufacturer and one retailer who possesses private demand forecast information. To promote green consumption, the government may provide subsidies to consumers. Within a dynamic game where the manufacturer serves as the leader and the retailer acts as the follower, three cases are examined: centralized decision, decentralized decision with and without demand forecast information sharing between the retailer and the manufacturer. We mainly examine the value of information sharing on the decisions of a GSC in the context of government subsidies for consumers. We find that: (i) demand forecast information sharing benefits the manufacturer but damages the retailer; (ii) if the predicted value is higher than the determinate part of the demand, the manufacturer is willing to choose a higher green degree of products in the case with information sharing compared with that without information sharing; otherwise, the manufacturer is willing to choose a lower green degree of products; (iii) a two-part tariff contract is appropriate to coordinate the GSC and it is effective in increasing the green degree of products; (iv) information sharing benefits the GSC if the green production efficiency is high enough; (v) the ex-ante social welfare always increases with information accuracy. Finally, numerical analyses are conducted to verify the above findings. [ABSTRACT FROM AUTHOR]

This paper considers a supply chain with one supplier and two retailers. The supplier has limited normal capacity. When the orders of two retailers exceed the capacity, the supplier can satisfy the retailers with the limited normal capacity, or he can start an emergent production to increase his capacity in a short time with a high unit production cost. When the cost of quickly expanding capacity is lower than the market price, it is profitable for the whole system to start the emergent production. But the supplier has no incentives to do additional production if his cost is higher than the wholesale price. To resolve this issue, we propose a supply commitment contract that the supplier will fully fill a retailer's order if the retailer pays a premium to the supplier. We consider a practical setting that each retailer has private information on her average demand. Two contracting scenarios of the supplier are compared: contracting with the two retailers simultaneously (the simultaneous scenario) and sequentially (the sequential scenario). It is shown that the retailers will adopt the threshold strategy based on their private demand information to pay the premium. By numerically studies, we further illustrate the supplier's optimal contract choices and the consequent system performance. First, the suppliers always prefer the retailers to move simultaneous although the supplier's profit is only slightly higher than that of the sequential scenario; second, the supplier sets a lower wholesale price and a higher commitment fee in the sequential scenario than those of simultaneous scenario; finally, the sequential scenario is a better choice from the perspectives of both the whole supply chain system and the retailers. Our research recommends the sequential moving in the supply commitment contract that it benefits the whole supply chain system with only slightly less efficient for the supplier. [ABSTRACT FROM AUTHOR]

Traditional research shows that a retailer suffers a loss due to information sharing with a manufacturer in a supply chain. However, when the manufacturer produces both new and remanufactured products, the retailer's trade-offs change. This paper investigates whether the retailer can share demand information with the manufacturer if they can add remanufactured products to the product line. We reveal that the retailer can benefit from information sharing when the manufacturer may remanufacture. On the one hand, if the retailer shares information, the manufacturer utilizes information to strategically adjust wholesale prices, resulting in a reduction in the retailer's profit. On the other hand, if the retailer does not share the information, the manufacturer abandons remanufacturing, leading to a lower profit for the retailer. Therefore, when the former loss is less than the latter, the retailer is willing to share information with the manufacturer. Furthermore, we find that remanufacturing and information sharing can achieve a triple-win situation for the retailer, the manufacturer and the environment. [ABSTRACT FROM AUTHOR]

Information asymmetry leads to short-term competitive antagonism between downstream manufacturers and upstream suppliers. This paper applies a master-slave game to reproduce the interest conflict between a manufacturer and a supplier, and proposes a cooperative decision-making model based on information sharing, considering the interest consistency of upstream supply chain members. Additionally, the overall profit coordination mechanism is constructed to ensure the continuation of the cooperative decision-making relationship. In addition, to ensure that the supplier participating in cooperative decision-making has the supply capacity desired by the manufacturer, a rational evaluation system combined with game theory is employed to determine the most suitable participating supplier. A numerical example is given to demonstrate the systematization and effectiveness of the proposed method. Finally, through sensitivity analysis and comparative analysis, managerial insights and recommendations are obtained. [ABSTRACT FROM AUTHOR]

Consumer uncertainty about product valuation is a significant economic issue in online retailing. In many markets, even though the product quality information is completely disclosed, consumers may still remain unsure about their valuation-for-quality. This paper investigates the effects of consumers' uncertain valuation-for-quality on a platform's and a firm's profits under commonly used wholesale price contract and consignment contract in a distribution channel. Our analysis reveals that regardless of the contract types, when the product quality is relatively low, both the platform and the firm benefit from a higher degree of informativeness of information; when the product quality is relatively high, however, the profits of both the platform and the firm show nonmonotonicity with respect to the informativeness degree (namely, first decrease then increase), which is driven by the joint effects of the value-enhancement effect and the segmentation effect incurred from the consumers' uncertain valuation-for-quality. Additionally, we find that when the quality is of medium level, the firm and the platform may achieve a consensus on contract preferences on the wholesale price contract or the consignment contract, critically dependent on the informative degree. [ABSTRACT FROM AUTHOR]

Information technology and data science have enabled firms to practice price discrimination on an unprecedented scale, arousing privacy concerns among their customers. When consumers know a firm is practicing price discrimination, they may take costly measures to conceal their identities so as to avoid being targeted. Governments, in turn, may require firms to disclose their price discrimination practices in order to protect consumers' interests. In this paper, we consider a pricing game in which two competitive, vertically differentiated firms may implement price discrimination using information purchased from a third-party data supplier. We determine (1) the firms' optimal pricing strategies when consumers can (or cannot) safeguard their personal information by paying a "privacy cost"; (2) the data supplier's optimal sales strategy and the value of the data; and (3) the effects of the cost of consumer privacy and of the disclosure of price discrimination practices on firms and consumers. We find that for the data supplier, the optimal sales strategy is always to sell exclusively to one firm, regardless of whether consumers are aware that the firm practices "personalized pricing". The question of which firm the data broker should sell to depends on what we term the "quality-adjusted cost"—the ratio between the additional cost of the high-quality product and the magnitude of the quality difference. If this ratio is smaller than 1/2, the data broker will sell to the high-quality firm; if greater, to the low-quality firm. Second, by comparing two scenarios involving the disclosure or non-disclosure of price discrimination, we find, somewhat counter-intuitively, that mandatory transparency increases industry profits and decreases consumer surplus when only the high-quality firm has access to consumer data. When only the low-quality firm has such access, transparency lowers industry profits once the quality-adjusted cost exceeds a certain threshold. When the quality-adjusted cost is in the intermediate range, mandatory transparency decreases social welfare. This means that the disclosure of price discrimination practices may have unfavorable consequences from a social planning standpoint. Thus, the new insights our findings offer into competitive personalized pricing in vertically differentiated markets will be useful not only to managers in the industry but also to regulators. [ABSTRACT FROM AUTHOR]

In this paper, we study the optimal ordering and inventory reallocation of the inventory service platform under the retailer demand information sharing. Due to the uncertainty of market demand, retailers' demand information is likely to be inaccurate or even false. In this regard, retailers can reduce demand uncertainty by screening market signals. Therefore, based on the sharing of mean demand information and market signals, we explored the platform's optimal ordering and inventory reallocation strategies, analyzed the retailer's motivation for sharing false demand information, and proposed a corresponding penalty coordination mechanism. Our results show that the sharing of demand information and screening market signals reduces the uncertainty of market demand, thereby improving the accuracy of orders and increasing profit of the system. On the other hand, we find that the inventory reallocation strategy of the platform is affected by uncertain market information, but has nothing to do with the actual average demand and market signals shared by retailers. In this way, retailers will only share real information when the sharing system meets certain key conditions, otherwise they may share false demand information. The proposed punishment mechanism can encourage retailers to share their actual demand information with the platform. [ABSTRACT FROM AUTHOR]

This paper proposes a novel advanced buyback (ABB) contract to coordinate a supply chain that consists of one supplier and one retailer. The contract stipulates when a buyback contract is executed and the amount of buyback. We model the interaction of supply chain participants as a Stackelberg game and incorporate the participants' reservation profits into the game to examine the effectiveness of the ABB contract with complete information. We demonstrate that the ABB contract can conveniently coordinate the supply chain, flexibly allocate profit, and facilitate avoidance of the aggressive overordering behavior of the retailer. This model is extended to the situation of information asymmetry, where the retailer holds private information about the cost of sales in discrete or continuous states. The optimal strategies of the supply chain and the participants are derived. Information asymmetry can damage the supplier but be beneficial to the retailer. The condition under which the supply chain achieves coordination is clarified. A numerical simulation is performed to verify and extend the results of the theoretical analysis. We show that the ABB contract always outperforms the ordinary wholesale price and buyback contracts with complete information. Supply chain coordination is more likely to be achieved if the demand variability is lower when weighing whether to share private information. Our findings can explain why retailers always have an incentive to inflate their cost of sales. [ABSTRACT FROM AUTHOR]

Information asymmetry is particularly common in the supply chain framework. As we know, the downstream retailer usually knows more about the market demand information than the upstream manufacturer because of her proximity to consumers. This paper considers a supply chain that a manufacturer sells products to consumers via a retailer in a local market. Besides, the manufacturer has an option to establish a direct online channel in the overseas market, in which the retailer can sell products as a gray marketer to earn an extra profit. Both markets' demand information is transparent to the retailer but asymmetric to the manufacturer. There are two side an opposite effects of information sharing on the retailer. The positive effect is that when she withholds demand information she can possess an information advantage. However, the negative effect is that when the retailer shares her private demand information, especially when the manufacturer enters the overseas market, the retailer's profit may be negatively affected because of the competition. Therefore, whether the retailer has motivation to share her private demand information with the manufacturer is an intriguing yet unanswered question. We first characterize the tradeoff in three supply chain structures, Local Market, Dual Market and Gray Market. Then, we obtain the equilibrium results of each case and find that a co-opetition strategy may arise because of competition when considering gray market and dual-channel. Finally, we uncover the underlying reasons and develop valuable insights. [ABSTRACT FROM AUTHOR]

In this paper, we study a two-stage model in which a manufacturer expands to a new market through a local retailer and has private information on the advertising effectiveness. The manufacturer chooses the information sharing format with the retailer, either no information sharing or mandatory information sharing. Under no information sharing format, the manufacturer and the retailer play a signaling game. We derive both separating and pooling equilibria and conduct equilibrium refinements for the signaling game. Under mandatory information sharing format, the manufacturer simply informs the retailer the advertising effectiveness. We also establish the stylized model and derive the optimal advertising effort. By comparing the manufacturer's ex ante profit under the two information sharing formats, we find that the manufacturer always prefers mandatory information sharing, under which both the advertising effort and profit can be higher. We also observe that unlike the common case that the channel members may have different preference over the information sharing formats, the manufacturer and the retailer can actually achieve alignment. While some previous studies suggest that the manufacturer and the retailer may have different preference over the information sharing formats, we find that they can actually achieve alignment with asymmetric information on advertising effectiveness. [ABSTRACT FROM AUTHOR]

With the increasingly fierce market competition and the rapid development of advanced logistics technology, service quality guarantee and demand updating become effective ways to promote procurement decisions in logistics supply chain. However, information asymmetry and obedience behavior have made it more complicated. In this paper, we considered the above factors, and studied the capacity procurement issue in a logistics service supply chain consisting of a logistics service integrator (LSI) and a functional logistics service provider (FLSP) in two periods. First, we find the optimal purchase quantities increase with the FLSP's obedience factor, in specific conditions, the LSI's guaranteed service quality and FLSP's obedience behavior can reach the upper limit (or lower limit). Second, the information symmetry creates a win–win situation iff the penalty cost to the FLSP is moderate and the demand is incompletely revealed. Third, demand updating relaxes the condition for the LSI's service quality guarantee reaching to the upper limit. For FLSP, when the penalty cost is moderate, the demand updating makes FLSP less obedient in case that the demand is completely revealed and more obedient when the demand is incompletely revealed. [ABSTRACT FROM AUTHOR]

The Industry 4.0 is important for China to achieve industrial upgrading and promoting the quality of manufacturing development. This paper investigates the driving force of the Industry 4.0 in China's manufacture industry, and evaluates the impact of Industry 4.0 on firm's performance. First, a textual mining is conducted to identify 460 companies that are implementing Industry 4.0 strategy, and then a Probit model is adopted to examine the driving forces of Industry 4.0. Through the propensity scores matching difference-in-difference method, the impacts of Industry 4.0 on firm's performance are evaluated. The results reveal that private and large companies show a higher motivation to promote the Industry 4.0 strategy, and government subsidies have no significant impact on firm's Industry 4.0 decision. The implementation of Industry 4.0 can significant improves firm's financial performance, innovation activities and stock returns, but has no significant impact on supply chain efficiency. In addition, the adoption of Industry 4.0 has positive impact on firm's information transparency grade. [ABSTRACT FROM AUTHOR]

This paper examines the effects of disclosing the actual number of participants in innovation contests with endogenous stochastic entry. We model innovation contests as a two-bidder all-pay auction with complete information, but in which each bidder has to incur a private cost to participate. The contest organizer observes solvers' participation decisions ex post and can commit ex ante to either fully disclosing or concealing the number of participating solvers. We characterize the equilibrium behavior of the solvers and compare the performances of the disclosure policies by four criteria. We find that full concealment dominates full disclosure in terms of expected total bid and expected winner's bid. Full concealment is dominated by full disclosure in terms of prize allocation efficiency and solvers' welfare. These findings are in sharp contrast to those under exogenous entry. [ABSTRACT FROM AUTHOR]

Cash managers who optimize returns and risk rely on biobjective optimization models to select the best policies according to their risk preferences. In the related portfolio selection problem, Merton (J Financ Quant Anal 7(4):1851–1872, 1972) provided the first analytical derivation of the efficient frontier with all non-dominated return and risk combinations. This first proposal was later extended to account for three or more criteria by other authors. However, the cash management literature needs an analytical derivation of the efficient frontier to help cash managers evaluate the implications of selecting policies and risk measures. In this paper, we provide three analytic derivations of the efficient frontier determining a closed-form solution for the expected returns and risk relationship using three different risk measures. We study its main properties and its theoretical implications for policies. Using the variance of returns as a risk measure imposes limitations due to invertibility reasons. [ABSTRACT FROM AUTHOR]

In this paper, we extend the Banzhaf–Coleman–Dubey–Shapley sensitivity index to the class of dichotomous voting games with several levels of approval in input, also known as (j, 2)-simple games. For previous works, on classical simple games ((2, 2)-simple games), a sensitivity index reflects the volatility or degree of suspense in the voting body. Using a set of independent axioms, we provide an axiomatic characterization of that extension on the class of (j, 2)-simple games. [ABSTRACT FROM AUTHOR]

In this paper we show the relations between 4-valued logics (and more precisely the DDT logic) and the use of bi-oriented graphs. Further on we focus on the use of bi-oriented graphs for non conventional preference modelling. More specifically, we show how bi-oriented graphs can be used in order to represent extended preference structures of the type definable using the DDT logic which has been created with the purpose of modelling hesitation in preference statements. We then study how transitive closure can be extended within such extended preference structures. [ABSTRACT FROM AUTHOR]

In this paper, a class of directionally differentiable multiobjective programming problems with inequality, equality and vanishing constraints is considered. Under both the Abadie constraint qualification and the modified Abadie constraint qualification, the Karush–Kuhn–Tucker type necessary optimality conditions are established for such nondifferentiable vector optimization problems by using the nonlinear version Gordan theorem of the alternative for convex functions. Further, the sufficient optimality conditions for such directionally differentiable multiobjective programming problems with vanishing constraints are proved under convexity hypotheses. Furthermore, vector Wolfe dual problem is defined for the considered directionally differentiable multiobjective programming problem vanishing constraints and several duality theorems are established also under appropriate convexity hypotheses. [ABSTRACT FROM AUTHOR]

Course timetables are the organizational foundation of a university's educational program. While students and lecturers perceive timetable quality individually according to their preferences, there are also collective criteria derived normatively such as balanced workloads or idle time avoidance. A recent challenge and opportunity in curriculum-based timetabling consists of customizing timetables with respect to individual student preferences and with respect to integrating online courses as part of modern course programs or in reaction to flexibility requirements as posed in pandemic situations. Curricula consisting of (large) lectures and (small) tutorials further open the possibility for optimizing not only the lecture and tutorial plan for all students but also the assignments of individual students to tutorial slots. In this paper, we develop a multi-level planning process for university timetabling: On the tactical level, a lecture and tutorial plan is determined for a set of study programs; on the operational level, individual timetables are generated for each student interlacing the lecture plan through a selection of tutorials from the tutorial plan favoring individual preferences. We utilize this mathematical-programming-based planning process as part of a matheuristic which implements a genetic algorithm in order to improve lecture plans, tutorial plans, and individual timetables so as to find an overall university program with well-balanced timetable performance criteria. Since the evaluation of the fitness function amounts to invoking the entire planning process, we additionally provide a proxy in the form of an artificial neural network metamodel. Computational results exhibit the procedure's capability of generating high quality schedules. [ABSTRACT FROM AUTHOR]

In this paper we consider the coupled task scheduling problem with exact delay times on a single machine with the objective of minimizing the total completion time of the jobs. We provide constant-factor approximation algorithms for several variants of this problem that are known to be N P -hard, while also proving N P -hardness for two variants whose complexity was unknown before. Using these results, together with constant-factor approximations for the makespan objective from the literature, we also introduce the first results on bi-objective approximation in the coupled task setting. [ABSTRACT FROM AUTHOR]

This paper studies a two-machine flow shop scheduling problem with availability constraints due to a breakdown on the first machine. The starting time of the breakdown is considered stochastic and follows a known probability distribution. A service-level constraint is introduced to model the guarantee with which the obtained schedule takes into account the stochastic nature of the breakdown's starting time. The objective is to find a solution that minimizes the makespan while satisfying the desired service level. The studied problem is strongly NP-hard. We develop two mixed integer linear models that linearize the non-linear model. Using interval modeling of the breakdown, we propose lower bounds and a valid inequality that are used to strengthen both models. When the lower bounds and the valid inequality are applied, the performance of both models is greatly improved by reducing the gap and reaching optimality for more instances. We also introduced two heuristics that exploit the proposed interval modeling. The computational results indicate that both models are able to reach optimality for the 10-job instances. Moreover, the comparisons results between both models with the two heuristics showed their effectiveness. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic forced the partial or total cancellation of most sports competitions worldwide. Sports organizations crucially rely on revenues raised from broadcasting. How should the allocation of these revenues be modified when sports leagues are cancelled? We aim to answer that question in this paper by means of the axiomatic approach. Two extension operators (dubbed zero and leg operators, respectively) will play a major role in our analysis. We show that several combinations of axioms (formalizing ethical or strategic principles) characterize the image via those operators of two focal rules: the equal-split rule and concede-and-divide. [ABSTRACT FROM AUTHOR]

It is standard cardiology practice for patients suffering from ventricular arrhythmias (the main cause of sudden cardiac death) belonging to high risk populations to be treated via the implantation of Subcutaneous Implantable cardioverter-defibrillators (S-ICDs). S-ICDs carry a risk of so-called T wave over sensing (TWOS), which can lead to inappropriate shocks that carry an inherent health risk. For this reason, according to current practice patients' Electrocardiograms (ECGs) are manually screened by a cardiologist over 10 s to assess the T:R ratio—the ratio between the amplitudes of the T and R waves which is used as a marker for the likelihood of TWOS—with a plastic template. Unfortunately, the temporal variability of a patient' T:R ratio can render such a screening procedure, which relies on an inevitably short ECG segment due to its manual nature, unreliable. In this paper, we propose and investigate a tool based on deep learning for the automatic prediction of the T:R ratios from multiple 10-second segments of ECG recordings capable of carrying out a 24-hour automated screening. Thanks to the significantly increased screening window, such a screening would provide far more reliable T:R ratio predictions than the currently utilized 10-second, template-based, manual screening is capable of. Our tool is the first, to the best of our knowledge, to fully automate such an otherwise manual and potentially inaccurate procedure. From a methodological perspective, we evaluate different deep learning model architectures for our tool, assess a range of stochastic-gradient-descent-based optimization methods for training their underlying deep-learning model, perform hyperparameter tuning, and create ensembles of the best performing models in order to identify which combination leads to the best performance. We find that the resulting model, which has been integrated into a prototypical tool for use by clinicians, is able to predict T:R ratios with very high accuracy. Thanks to this, our automated T:R ratio detection tool will enable clinicians to provide a completely automated assessment of whether a patient is eligible for S-ICD implantation which is more reliable than current practice thanks to adopting a significantly longer ECG screening window which better and more accurately captures the behavior of the patient's T:R ratio than the current manual practice. [ABSTRACT FROM AUTHOR]

The operating theatre is the most crucial and costly department in a hospital due to its expensive resources and high patient admission rate. Efficiently allocating operating theatre resources to patients provides hospital management with better utilization and patient flow. In this paper, we tackle both tactical and operational planning over short-term to medium-term horizons. The main goal is to determine an allocation of blocks of time on each day to surgical specialties while also assigning each patient a day and an operating room for surgery. To create a balance between improving patients welfare and satisfying the expectations of hospital administrators, we propose six novel deterioration rates to evaluate patients total clinical condition deterioration. Each deterioration rate is defined as a function of the clinical priorities of patients, their waiting times, and their due dates. To optimize the objective functions, we present mixed integer programming (MIP) models and two dynamic programming based heuristics. Computational experiments have been conducted on a novel well-designed and carefully chosen benchmark dataset, which simulates realistic-sized instances. The results demonstrate the capability of the MIP models in finding excellent solutions (maximum average gap of 4.71% across all instances and objective functions), though, requiring large run-times. The heuristic algorithms provide a time-efficient alternative, where high quality solutions can be found in under a minute. We also analyse each objective function's ability in generating high quality solutions from different perspectives such as patients waiting times, the number of scheduled patients, and operating rooms utilization rates. We provide managerial insights to the decision makers in cases where their intention is to meet KPIs and/or maintaining trade-offs between patients and administrators expectations, more fair assignments, or ensuring that the most urgent patients are taken care of first. [ABSTRACT FROM AUTHOR]

The widespread outbreak of a new Coronavirus (COVID-19) strain has reminded the world of the destructive effects of pandemic and epidemic diseases. Pandemic outbreaks such as COVID-19 are considered a type of risk to supply chains (SCs) affecting SC performance. Healthcare SC performance can be assessed using advanced Management Science (MS) and Operations Research (OR) approaches to improve the efficiency of existing healthcare systems when confronted by pandemic outbreaks such as COVID-19 and Influenza. This paper intends to develop a novel network range directional measure (RDM) approach for evaluating the sustainability and resilience of healthcare SCs in response to the COVID-19 pandemic outbreak. First, we propose a non-radial network RDM method in the presence of negative data. Then, the model is extended to deal with the different types of data such as ratio, integer, undesirable, and zero in efficiency measurement of sustainable and resilient healthcare SCs. To mitigate conditions of uncertainty in performance evaluation results, we use chance-constrained programming (CCP) for the developed model. The proposed approach suggests how to improve the efficiency of healthcare SCs. We present a case study, along with managerial implications, demonstrating the applicability and usefulness of the proposed model. The results show how well our proposed model can assess the sustainability and resilience of healthcare supply chains in the presence of dissimilar types of data and how, under different conditions, the efficiency of decision-making units (DMUs) changes. [ABSTRACT FROM AUTHOR]

Gastric Cancer is the most common malignancy of the digestive tract, which is the third leading cause of cancer-related mortality worldwide. The early prognosis methods, especially Machine Learning (ML)-based approaches are one of the main strategies against GC, which have become a necessity to identify and prognosticate the factors that affect the GC. They enable the specialists to accelerate the subsequent clinical management of patients, who suffer from the GC. This paper aims at creating an Ensemble Method inspired from ML to predict the most significant factors of the GC occurrence. The main objective of this research is to predict the probabilities of the GC occurrence and its associated deaths. To achieve this goal, the created EM benefits from some ML-based methods, including Least Absolute Shrinkage and Selection Operator (LASSO)/Ridge Regression, Elastic Net, Logistic Regression (LR), Random Forest (RF), Gradient Boosting Decision Trees (GBDTs), and Deep Neural Network (DNN). The purpose of the provided EM is to lessen the prediction errors for the large number of the patients' features. The main novelties of this research include: (i) A sequential EM created by a Stacking method to predict the probability of the GC and associated deaths; (ii) Benefiting from the significance level to make an accurate prediction; (iii) Employing two Chi-square tests to select the influent features; (iv) Tuning the parameters of the applied ML models to avoid over-fitting and intensifying the errors; (v) Applying different kinds of the regression methods to treat hyper-dimension cases; (vi) A new model for weighting the applied ML models. The outcomes of the implementation of the created EM in seven pioneer hospitals in the field of GC show that the designed EM generates more precise predictions with an accuracy of 97.9% and 76.3% to predict the GC and its associated deaths, respectively. Moreover, the obtained results from the Area Under Curve (AUC) validates and confirms the capability of the created EM to predict the probability of the GC and its related deaths with an accuracy of 98% and 90% to predict the GC and its associated deaths, respectively. [ABSTRACT FROM AUTHOR]

In recent decades, breast cancer has become one of the leading causes of mortality among women. This disease is not preventable because of its unknown causes; however, its early diagnosis increases patients' recovery chances. Machine learning (ML) can be utilized to improve treatment outcomes in healthcare operations while diminishing costs and time. In this research, we suggest two novel feature selection (FS) methods based upon an imperialist competitive algorithm (ICA) and a bat algorithm (BA) and their combination with ML algorithms. This study aims to enhance diagnostic models' efficiency and present a comprehensive analysis to help clinical physicians make more precise and reliable decisions. K-nearest neighbors (KNN), support vector machine (SVM), decision tree (DT), Naive Bayes, AdaBoost (AB), linear discriminant analysis (LDA), random forest (RF), logistic regression (LR), and artificial neural network (ANN) are some of the methods employed. Sensitivity, accuracy, precision, mean absolute error F-score, root mean square error, Kappa, and relative absolute error calculated the performance of the methods. This paper applied a distinctive integration of evaluation measures and ML algorithms using the wrapper feature selection based on ICA (WFSIC) and BA (WFSB) separately. We compared two proposed approaches for the performance of the classifiers. Also, we compared our best diagnostic model with previous works reported in the literature survey. Experimentations were performed on the Wisconsin diagnostic breast cancer (WDBC) dataset. Results reveal that the proposed framework that uses the BA with an accuracy of 99.12% surpasses the framework using the ICA and most previous works. Additionally, the RF classifier in the approach of FS based on BA emerges as the best model and outperforms others regarding its criteria. Besides, the results illustrate the role of our techniques in reducing the dataset dimensions up to 90% and increasing the performance of diagnostic models by over 99%. Moreover, the result demonstrates that there are more critical features than the optimum dataset obtained by proposed FS approaches that have been selected by most ML models, including the standard error of area, concavity, smoothness, perimeter, the worst of texture, compactness, radius, symmetry, smoothness, concavity, and the mean of concave points, fractal dimension, compactness, concavity that can remarkably affect the efficiency of breast cancer prediction. This study illustrates the role of our approaches in enhancing treatment outcomes in healthcare operations. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has become a global health and humanitarian crisis that catastrophically affects many industries. To control the disease spread and restore normal lives, mass vaccination is considered the most effective way. However, the sustainable last-mile cold chain logistics operations of COVID-19 vaccines is a complex short-term planning problem that faces many practical challenges, e.g., low-temperature storage and transportation, supply uncertainty at the early stage, etc. To tackle these challenges, a two-stage decision-support approach is proposed in this paper, which integrates both route optimization and advanced simulation to improve the sustainable performance of last-mile vaccine cold chain logistics operations. Through a real-world case study in Norway during December 2020 and March 2021, the analytical results revealed that the logistics network structure, fleet size, and the composition of heterogeneous vehicles might yield significant impacts on the service level, transportation cost, and CO2 emissions of last-mile vaccine cold chain logistics operations. [ABSTRACT FROM AUTHOR]

Nowadays, the amounts of infectious medical waste (IMW) have surged considerably so waste management has become a critical emergency in many developing countries. However, most large medical waste generation centers (MWGC) are equipped with treatment facilities, small MWGC faces the waste management problem. It reveals the significance of having a proper program for small health centers. This is an indisputable difficulty that governments bordered because it imposes great costs on societies, also the environmental problems caused by improper treatment are irreparable. To attend to all the essential aspects of the problem, this paper recommended a location-routing model with four objective functions to minimize the total costs, environmental pollution, the risk imposed on the population around disposal sites, and the total violation from the expected arrival time. Considering a multi-period problem with a maximum acceptable delay plays a key role to connect the assumptions to the real-world problem. In addition, for solving mathematical models based on case studies, the role of uncertainty is undeniable. The demand for dental waste treatment is not definite and is changed based on the different conditions thus fuzzy chance-constrained programming is proposed for this problem to tackle the uncertainty. The revised multi-choice goal programming method is considered to solve the model and a real case study for dental clinics in Babol city of Iran is investigated to illustrate the validation of the proposed model. The results indicate that the solution method can create a balance between four objective functions. Finally, sensitivity analyses are performed for some parameters to analyze the behavior of the objective functions. [ABSTRACT FROM AUTHOR]

In this paper, we consider a two-stage stochastic weekly operating room planning problem with an exponential number of scenarios. The objective function is to minimize the sum of the fixed opening cost of operating rooms and the expected overtime costs that are computed in the second stage. We propose a state-variable model to formulate the two-stage stochastic operating room planning problem and prove its validity. The main advantage of the proposed state-variable model is that it has a pseudo-polynomial number of variables and constraints that are significantly fewer than the number of variables and constraints in an equivalent scenario-based stochastic programming model. We improve the quality of the proposed model by developing an enhanced model that includes remarkably fewer variables and constraints. We also strengthen the model by developing several valid inequalities, including worst-case scenario and symmetry-breaking cuts. We carried out extensive computational experiments to evaluate the performance of the proposed model. The computational results show that the proposed model is capable of finding optimal solutions of instances with 50 surgeries and 1.55E+40 scenarios that is a significant improvement over the state-of-the-art models. The results revealed that the model finds feasible solutions with an average optimality gap of 0.78% for instances with 80 surgeries and 1.48E+64 scenarios. [ABSTRACT FROM AUTHOR]

Due to the high necessity of medical face masks and face shields during the COVID-19 pandemic, healthcare centers dealing with infected patients have faced serious challenges due to the high consumption rate face masks and face shields. In this regard, the supply chain of healthcare centers should put all of their efforts into avoiding any shortages of masks and shields as these products are considered as primary ways to prevent the spread of the virus. Since, any shortages in these products would lead to irrecoverable and costly consequences in terms of the mortality rate of patients and medical staff. Therefore, healthcare centers should decide on best supplier to supply required products, considering technical, and sustainability measures. Dynamicity and uncertainty of the pandemic are other factors that add up to the complexity of the supplier selection problem. Therefore, this paper develops a novel decision-making approach using Measuring attractiveness through a categorical-based evaluation technique (MACBETH) and a new combinative distance-based assessment method to address the supplier selection problem during the COVID-19 pandemic. Due to high uncertainty, vague and incomplete information for decision-making problems during the COVID-19 pandemic, the developed decision-making approach is implemented under fuzzy rough numbers as a superior uncertainty set of the traditional fuzzy set and rough numbers. Extensive sensitivity analysis tests are performed based on parameters of the decision-making approach, impacts of weight coefficients, and consistency of results in comparison to other MCDM methods. A real-life case study is investigated for a hospital in Istanbul, Turkey to show the applicability of the developed approach. Based on the results of MACBETH method, job creation and occupational health and safety systems are two top criteria. Results of the case study for five suppliers indicate that supplier (A1) is the best supplier with a distance score of 3.308. [ABSTRACT FROM AUTHOR]

Fake news, misinformation and disinformation have significantly increased over the past years, and they have a profound effect on societies and supply chains. This paper examines the relationship of information risks with supply chain disruptions and proposes blockchain applications and strategies to mitigate and manage them. We critically review the literature of SCRM and SCRES and find that information flows and risks are relatively attracting less attention. We contribute by suggesting that information integrates other flows, processes and operations, and it is an overarching theme that is essential in every part of the supply chain. Based on related studies we create a theoretical framework that incorporates fake news, misinformation and disinformation. To our knowledge, this is a first attempt to combine types of misleading information and SCRM/SCRES. We find that fake news, misinformation and disinformation can be amplified and cause larger supply chain disruptions, especially when they are exogenous and intentional. Finally, we present both theoretical and practical applications of blockchain technology to supply chain and find support that blockchain can actually advance risk management and resilience of supply chains. Cooperation and information sharing are effective strategies. [ABSTRACT FROM AUTHOR]

In this work, we focus on the following research question: "Could fake news extracted on Google be helpful in explaining the production and supply process in the food sector of the US economy?" In order to tackle this research question, we trace the supply chain of the US food sector based on Input–Output (IO) mapping. In fact, IO analysis is an essential tool for engineers, managers and decision makers across the globe, due to its direct link with the supply chain framework. The whole supply chain is perfectly captured based on the IO model. In this context, the paper studies the possibility of the production and supply processes being influenced by fakes news, captured by key phrases of Google searches, such as "collapse + US economy". In this work, we incorporate fake news on the production process and estimate, using a suitably adjusted version of the traditional ARDL model augmented with Neural Network terms, their impact on the production process of the US food sector. We conclude that searches for fake news referring to the collapse of the US economy, could lead to significant improvement in the explanatory capability of the production process in the US food sector. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) and Big Data Analytics (BDA) have the potential to significantly improve resilience of supply chains and to facilitate more effective management of supply chain resources. Despite such potential benefits and the increase in popularity of AI and BDA in the context of supply chains, research to date is dispersed into research streams that is largely based on the publication outlet. We curate and synthesise this dispersed knowledge by conducting a systematic literature review of AI and BDA research in supply chain resilience that have been published in the Chartered Association of Business School (CABS) ranked journals between 2011 and 2021. The search strategy resulted in 522 studies, of which 23 were identified as primary papers relevant to this research. The findings advance knowledge by (i) assessing the current state of AI and BDA in supply chain literature, (ii) identifying the phases of supply chain resilience (readiness, response, recovery, adaptability) that AI and BDA have been reported to improve, and (iii) synthesising the reported benefits of AI and BDA in the context of supply chain resilience. [ABSTRACT FROM AUTHOR]

This paper aims to investigate the impact of blockchain application on trust levels in supply chains. Through the systematic review of the relevant literature, three dimensions of trust, i.e., the trustor–trustee perspective, forms of trust, and time orientation, are investigated. Our findings show that, first, there are three pairs of trustors and trustees involved in blockchain implementation: (a) the user and the blockchain, (b) two supply chain partners, and (c) the consumer/public and a supply chain unit. Second, the two forms of trust, namely cognition-based and institution-based trust, are likely to be enhanced by blockchain execution, while affect-based trust may not be directly impacted by the technology. Third, the presence of blockchain technology would facilitate swift trust-building between unknown supply chain partners under specific circumstances. Moreover, we also find contradicting assertions among scholars on the implications of blockchain for trust in supply chains. While some studies pointed out that blockchain will enable a trustless trusted scheme, others expected the reinforcement of interorganizational trust. To test these assertions, we develop the blockchain-entrusted supply chain models to present the three-step process of how trust is developed through the blockchain and diffused to supply chain partners and external stakeholders. [ABSTRACT FROM AUTHOR]

Distributed Ledger Technology (DLT) is highly applicable in various fields, especially the supply chain in many sectors. Against limited empirical evidence, this paper analyzes the relations between the Kensho Distributed Ledger Technology Index and stock indices of 12 sectors, including communication services, consumer discretionary, consumer staples, energy, health care, financials, industrials, information technology, materials, utilities, and real estate, and ESG by employing the quantile coherency and dynamic connectedness techniques. Our results reveal that the quantile coherency between the DLT stock index and the sectoral stock indices in almost all cases is significant and positive. The positive co-movement tends to be stronger in the longer terms and as we move from the lower to the higher quantiles, implying that they are more strongly connected in the long term and during the bearish market condition. Moreover, the dynamic connectedness indicates that the DLT stocks and the sectoral stocks are highly connected, with the former being a net transmitter of spillover shocks. The spillovers are also time-varying, and the results significantly corroborate those of the quantiles coherency methods. Among other relevant implications, DLT can be an important factor in the development and enhancement of these sectors. [ABSTRACT FROM AUTHOR]

Supply chains (SCs) are susceptible to risks because of their dynamic and complex nature. Big data analytics (BDA) through blockchain technology (BCT) can significantly contribute to managing SC risks. However, to date, the combined effect of BDA-BCT for SC risks has not been investigated extensively in the literature. This paper aims to identify the risk factors of the BDA-BCT initiative for Indian manufacturing organisations. Through the literature and experts' judgments, sixteen risk factors were identified. Data was collected from machine tool, automobile component, and electrical manufacturing organisations. Further interrelations between risk factors were evaluated using the grey DEMATEL approach. The results show that 'supply chain visibility risks', 'infrastructure and development costs', 'demand forecasting and sensing risks', 'data privacy and security risks', 'policy and legality related risks', and 'supply chain resilience' were identified as common factors in the adoption of BDA-BCT practices by the three organisations. The cause-effect relationship between risk factors can assist managers, suppliers, service providers, and policymakers in the significant adoption of BDA-BCT in the context of manufacturing organisations. The study provides a novel way to utilise BDA-BCT in minimising supply chain risks. Limitations of the study are that it was conducted only for Indian organizations. In the future, the findings of the study can be validated through empirical analysis. [ABSTRACT FROM AUTHOR]

Tools established for managing information flow in supply chain management and logistics should match digital transformations. This issue is particularly salient for developing nations that hope to achieve sustainable development goals in a globalized era. Modern technologies are required to ensure a secure, transparent, and traceable path of information flow in global supply chains; however, it is not always straightforward for businesses in developing economies to adopt new digital technologies while sustaining productivity. One of the foundational technologies that can be used to create a basis for economic and social systems and to affect manufacturing supply chains in developing economies is blockchain. In this study, we analyze the barriers to blockchain technology adoption in manufacturing supply chains using the neutrosophic analytic hierarchy process (N-AHP). We propose an action plan framework for the validation of blockchain technology in a developing economy. The findings demonstrate that "transaction-level uncertainties" comprise the most critical barrier and have the highest weight in the final ranking followed by "usage in the underground economy", "managerial commitment", "challenges in scalability", and "privacy risks". This paper can assist industrial managers and experts in emerging economies to more clearly identify barriers to the implementation of blockchain technology and show them how to successfully employ blockchain technology in their supply chains. [ABSTRACT FROM AUTHOR]

This paper considers a defensive resource allocation problem in which a defender protects a set of assets either individually or collectively using overarching protections. An overarching protection refers to an option that protects multiple assets at the same time, e.g., emergency response, border security and counter intelligence. Most of the defensive resource allocation models with overarching protections assume that there is only one option that protects all targets. However, this may not be realistic considering that, for example, emergency response investment may cover only a certain region. In this paper, we develop a new resource allocation model to accommodate generalized overarching protections against intentional attacks. The model also considers multiple natural disaster types. We show that the proposed optimization model is a convex optimization problem and therefore can be solved to optimality in polynomial time. Furthermore, the overall country-level resource allocation problem can be decomposed into smaller city-level subproblems, thus resulting in a more efficient algorithm. The numerical experiments demonstrate the performance of the proposed approach. [ABSTRACT FROM AUTHOR]

In this paper, a mixed-integer linear programming model is discussed to provide joint decision making for facility location and production–distribution across countries for both forward and reverse logistics. A hybrid facility network is considered for cost-cutting and equipment sharing where the facilities of forward logistics are also equipped to provide reverse logistics services. The model considers the dynamic production and storage capacity of the facilities which can be expanded if required. Furthermore, the effectiveness of the model is tested to deal with disruptions due to man-made or natural disasters. The dynamic facility allocation enables the model to withstand the demand/supply disruptions in a disaster-affected zone. Besides this, the model considers carbon emissions caused due to manufacturing, remanufacturing, repair, storage and transportation. These emissions are regulated using cap and trade policy Thus, the proposed model balances resilience and sustainability under uncertain market demand and product returns. The chance-constrained approach is used to obtain the deterministic equivalence of the stochastic demand and returns. The paper also investigates the changes in emission and production level in each country under demand and supply disruptions. The parameters of the model are mapped with the various dimensions of big data such as volume, velocity and variety. The proposed model is solved using randomly generated data sets having realistic parameters with essential big data characteristics. [ABSTRACT FROM AUTHOR]

In the knapsack problem a group of agents want to fill a knapsack with several goods. Two issues must be considered. The first is to decide optimally what goods to select for the knapsack. This issue has been studied in many papers in the literature on Operations Research and Management Science. The second issue is to divide the total revenue among the agents. This issue has been studied in only a few papers, and this is one of them. For each knapsack problem we consider three associated cooperative games. One of them (the pessimistic game) has already been considered in the literature. The other two (realistic and optimistic games) are defined in this paper. The pessimistic and realistic games have non-empty cores but the core of the optimistic game could be empty. We then follow the axiomatic approach. We propose two rules: The first is based on the optimal solution of the knapsack problem. The second is the Shapley value of the so called optimistic game. We offer axiomatic characterizations of both rules. [ABSTRACT FROM AUTHOR]

In this paper we discuss on several ways to extend power indices defined on simple games to the context of (j, 2)-games, i.e., games with 2 possible outputs and j possible input levels. The purpose of this paper is more conceptual than technical, and shows that different power indices can be obtained in (j, 2)-games depending on how the criticality of a player is measured. Some power indices for simple games, regarded as measures of power as payment, can be obtained from the selection of two items: (1) a probabilistic model, and (2) a collection of winning coalitions. The combination of two particular probabilistic models and three collections of winning coalitions lead to six well-known power indices for simple games. The Banzhaf and the Johnston power indices are among them. The main goal of the paper is to discuss the generalizations of these six power indices from (2, 2)-games to (j, 2)-games for j ≥ 3 . The probabilistic model and the collection of winning coalitions are trivially extendable to (j, 2)-games. However, at least four possible ways of measuring the criticality of a player can be considered in this new context, and lead to different possible extensions of the six power indices to (j, 2)-games. We highlight two of them as being the most interesting under our point of view. The first one would be suitable for assessing sensitivity, because it measures the player's ability to modify the output by a small change in his input level. The second one would be adequate to evaluate strength, because it measures the voter's ability to modify the output by any change in his voting. The main conclusion is that different extensions of the considered power indices, as the Banzhaf and the Jonnston indices, are possible for j ≥ 3 and that they should be taken into account when working in a multiple input context. [ABSTRACT FROM AUTHOR]

This paper introduces dual fairness concerns into the classic two-level supply chain consisting of the fairness neutral supplier and fairness concerned retailers. The bargaining process is modeled under both simultaneous and sequential game to analyze the different situation of fairness concerns. The impact of dual fairness concerns is considered comprehensively on both short-term and long-term games. In short-term game, we conduct a sensitivity analysis on the optimal decision in a single cycle and find that the bargaining power and distributional fairness concern has opposite effects on the optimal solutions. Similarly, the impact of dual fairness concerns on that is also opposite. In long-term game, the dynamic system is constructed to investigate the influence of dual fairness concerns on system stability. At last, comparing the performance of the supplier and retailers, this paper explores the supplier's timing choice based on the equilibrium point. The comparison shows that sequential game is more beneficial to the supplier because peer-induced fairness concern exacerbates the internal friction of retailers. [ABSTRACT FROM AUTHOR]

Applications of data envelopment analysis (DEA) often include inputs and outputs represented as percentages, ratios and averages, collectively referred to as ratio measures. It is known that conventional DEA models cannot correctly incorporate such measures. To address this gap, the authors have previously developed new variable and constant returns-to-scale models and computational procedures suitable for the treatment of ratio measures. The focus of this new paper is on the scale characteristics of the variable returns-to-scale production frontiers with ratio inputs and outputs. This includes the notions of the most productive scale size (MPSS), scale and overall efficiency as measures of divergence from MPSS. Additional development concerns alternative notions of returns to scale arising in models with ratio measures. To keep the exposition as general as possible and suitable in different contexts, we allow all scale characteristics to be evaluated with respect to any selected subsets of volume and ratio inputs and outputs, while keeping the remaining measures constant. Overall, this new paper aims at expanding the range of techniques available in applications with ratio measures. [ABSTRACT FROM AUTHOR]

Global corporate giants are keen to adopt Industry 4.0 (I4.0) owing to its continuous, impactful, and evident benefits. However, implementing I4.0 remains a significant challenge for many organizations, mainly due to the absence of a systematic and comprehensive framework. The risk assessment study is key to the flawless execution of any project is a proven fact. This paper aims to develop a KPIs-based sustainable integrated model to assess and evaluate risks associated with the I4.0 implementation. This research paper has developed the I4.0 risks evaluation model through fifteen expert interventions and an extensive systematic literature review. This research, based on sixteen KPIs evaluates six risks impacting the organization's decision to adopt I4.0. Initially, the Fuzzy Decision-Making Trial and Evaluation Laboratory method is used to map the causal relationship among the KPIs. Further, the additive ratio assessment with interval triangular fuzzy numbers method is used to rank the risks. The study revealed that information technology infrastructure and prediction capabilities are the most crucial prominence and receiver KPIs. Simultaneously, technological and social risks are found to be highly significant in the I4.0 implementation decision-making process. The developed model meticulously supports the manufacturer's, policymaker, and researchers' viewpoint toward I4.0 implementation in the present and post COVID-19 pandemic phases in manufacturing companies. The comprehensive yet simple model developed in this study contributes to the larger ambit of new knowledge and extant literature. The integrated model is exceptionally based on the most prominent risks and a wider range of KPIs that are further analyzed by aptly fitting two fuzzy MCDM techniques, which makes the study special as it perfectly takes care of the uncertainties and vagueness in the decision-making process. Hence, this study is pioneering and unique in context to I4.0 risks prioritization aiming to accelerate I4.0 adoption. [ABSTRACT FROM AUTHOR]

This paper surveys the increasing use of statistical approaches in non-parametric efficiency studies. Data Envelopment Analysis (DEA) and Free Disposable Hull (FDH) are recognized as standard non-parametric methods developed in the field of operations research. Kneip et al. (Econom Theory, 14:783–793, 1998) and Park et al. (Econom Theory, 16:855–877, 2000) develop statistical properties of the variable returns-to-scale (VRS) version of DEA estimators and FDH estimators, respectively. Simar & Wilson (Manag Sci 44, 49–61, 1998) show that conventional bootstrap methods cannot provide valid inference in the context of DEA or FDH estimators and introduce a smoothed bootstrap for use with DEA or FDH efficiency estimators. By doing so, they address the main drawback of non-parametric models as being deterministic and without a statistical interpretation. Since then, many articles have applied this innovative approach to examine efficiency and productivity in various fields while providing confidence interval estimates to gauge uncertainty. Despite this increasing research attention and significant theoretical and methodological developments in its first two decades, a specific and comprehensive bibliometric analysis of bootstrap DEA/FDH literature and subsequent statistical approaches is still missing. This paper thus, aims to provide an extensive overview of the key articles and their impact in the field. Specifically, in addition to some summary statistics such as citations, the most influential academic journals and authorship network analysis, we review the methodological developments as well as the pertinent software applications. [ABSTRACT FROM AUTHOR]

Simultaneous monitoring of multi-dimensional processes becomes much more challenging as the dimension increases, especially when there are only a few or moderate number of process variables that are responsible for the process change, and when the size of change is particularly small. In this paper, we develop an efficient statistical process monitoring methodology in high-dimensional processes based on the Bayesian approach. The key idea of this paper is to sequentially update a posterior distribution of the process parameter of interest through the Bayesian rule. In particular, a sparsity promoting prior distribution of the parameter is applied properly under sparsity, and is sequentially updated in online processing. A Bayesian hierarchical model with a data-driven way of determining the hyperparameters enables the monitoring scheme to be effective to the detection of process shifts and to be efficient to the computational complexity in the high-dimensional processes. Comparison with recently proposed methods for monitoring high-dimensional processes demonstrates the superiority of the proposed method in detecting small shifts. In addition, graphical presentations in tracking the process parameter provide the information about decisions regarding whether a process needs to be adjusted before it triggers alarm. [ABSTRACT FROM AUTHOR]

This paper extends Berge's maximum theorem for possibly noncompact action sets and unbounded cost functions to minimax problems and studies applications of these extensions to two-player zero-sum games with possibly noncompact action sets and unbounded payoffs. For games with perfect information, also known under the name of turn-based games, this paper establishes continuity properties of value functions and solution multifunctions. For games with simultaneous moves, it provides results on the existence of lopsided values (the values in the asymmetric form) and solutions. This paper also establishes continuity properties of the lopsided values and solution multifunctions. [ABSTRACT FROM AUTHOR]