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The pulp and paper industry is critical to global industrial and economic development. Recently, India's pulp and paper industries have been facing severe competitive challenges. The challenges have impaired the environmental performance and resulted in the closure of several operations. Assessment and prediction of the performance of the Indian pulp and paper industry using various parameters is a critical task for researchers. This study proposes a framework for performance assessment and prediction based on Data Envelopment Analysis (DEA), Artificial Neural Networks, and Deep Learning (DL) to assist industry administration and decision-making. We presented a case study based on eight industries to demonstrate the methodology's applicability. This study analyses and predicts industry performance based on sample data observations over 30 years. The result suggests the DEA-DL-based efficiency prediction has an overall MSE of 0.08 compared with the actual efficiency. Furthermore, the efficiency rankings are compared between the three techniques. The results suggest that the integrated DEA-DL method is primarily accurate in most scenarios with the actual values. The findings of this study provide a comprehensive analysis of environmental performance for policymakers. [ABSTRACT FROM AUTHOR]

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]

The low-carbon supply chain is one of the predominant topics towards a green economy and it establishes the opportunity to reduce carbon emissions across the product value chain. This paper focuses on recycling and optimized sourcing in the paper industry as a case company. The main objective is to engage the case company with their supplier networks to diminish the greenhouse gases (GHG) emissions and cost in their production process. It proposes a model to support the selection of the best green supplier and an allocation of order among the potential suppliers. The proposed model contains a two-phase hybrid approach. The first phase presents the rating and selection of potential suppliers by considering economics (cost), operational factors (quality and delivery), and environmental criteria (recycle capability and GHG emission control) using Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (Fuzzy TOPSIS) methodology. The second phase presents the order allocation process using multi-objective linear programming in order to minimize cost, material rejection, late delivery, recycle waste and $$\mathrm{CO}_{2}$$ emissions in the production process. A case study from a paper manufacturing industry is presented to elucidate the effectiveness of the proposed model. The results demonstrate a 26.2 % reduction of carbon emission by using recycle products in the production process. The firm benefits by forming a systematic methodology for green supplier evaluation and order allocation. Finally, a conclusion and a suggested direction of future research are introduced. [ABSTRACT FROM AUTHOR]

Since the failure mode and effects analysis (FMEA) technique has the advantage of simple and fast calculation, the FMEA method is widely and commonly used in solving risk assessment issues. The traditional FMEA method uses the product of risk assessment factors (occurrence, severity, and detection) to calculate risk priority number (RPN) for risk ranking. Although the RPN approach is widely adopted by the military and industry, it cannot process uncertain and incomplete information, it does not consider the relative importance of three risk assessment factors and in some situations, it loses some valuable information provided by experts. This results in the same RPN value which cannot provide the accurate risk level. In order to effectively solve these problems, this paper combined the data envelopment analysis (DEA) method and 2-tuple fuzzy linguistic representation model (2-tuple FLRM) and proposed a new model, called the 2-tuple DEA method, for ranking the risk of product (system) failures. In the numerical verification section, this paper applied the risk assessment of crawler crane to verify the rationality and correctness of the 2-tuple DEA approach. The calculation results confirm that the proposed 2-tuple DEA approach provides a more accurate failure risk ranking and retains all valuable information. [ABSTRACT FROM AUTHOR]

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]

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]

This paper addresses the problem of generating an evenly distributed set of Pareto solutions. It appears in real-life applications related to multi-objective optimization when it is important to represent the entire Pareto front with a minimal cost. There exist only a few algorithms which are able to tackle this problem in a general formulation. The Directed Search Domain (DSD) algorithm has proved to be efficient and quite universal. It has successfully been applied to different challengeable test cases. In this paper for the first time the DSD approach is systematically extended and applied to problems with higher dimensions. The modified algorithm does not have any formal limitation on the number of objective functions that is important for practical applications. The efficacy of the algorithm is demonstrated on a number of test cases. [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]

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]

Uniform sampling in the permutation space is very important for solving permutation problems with NP-hard nature. However, due to the complexity of this space, there is no uniform sampling method for it up to now. In this paper, the description of permutation space and a review of uniform sampling in other space are given. After that, the limitation of the random method for uniform sampling is analyzed, and a k-means clustering algorithm with an improved Borda's method is introduced for sampling based on the above analysis. An extended Latin matrix is defined, and a sampling method based on this matrix that can only solve for a fixed number of sampling is presented. The properties of this method are explored and demonstrated. A uniform sampling method is then proposed for an arbitrary number of sampling points. Experiments are implemented under different sizes of permutation spaces and the results show that the method proposed in this paper has superior performance, which is more than 100 times better than the random method. [ABSTRACT FROM AUTHOR]

In this paper, we study the efficacy of several mathematical programming formulations for the single-source shortest path problem, the negative cost cycle detection problem, and the shortest negative cost cycle problem in arc-dependent networks. In an arc-dependent network, the cost of an arc a depends upon the arc preceding a. These networks differ from traditional networks in which the cost associated with an arc is a fixed constant and part of the input. Arc-dependent networks are useful for modeling a number of real-world problems, such as the turn-penalty shortest path problem, which cannot be captured in the traditional network setting. We present new integer and non-linear programming formulations for each problem. We also perform the first known empirical study for arc-dependent networks to contrast the execution times of the two formulations on a set of graphs with varying families and sizes. Our experiments indicate that although non-linear programming formulations are more compact, integer programming formulations are more efficient for the problems studied in this paper. Additionally, we introduce a number of cuts for each integer programming formulation and examine their effectiveness. [ABSTRACT FROM AUTHOR]

Industrial 4.0 (I4.0) is believed to revolutionize supply chain (SC) management and the articles in this domain have experienced remarkable increments in recent years. However, the existing insights are scattered over different sub-topics and most of the existing review papers have ignored the underground decision-making process using OR methods. This paper aims to depict the current state of the art of the articles on SC optimization in I4.0 and identify the frontiers and limitations as well as the promising research avenue in this arena. In this study, the systematic literature review methodology combined with the content analysis is adopted to survey the literature between 2013 and 2022. It contributes to the literature by identifying the four OR innovations to typify the recent advances in SC optimization: new modeling conditions, new inputs, new decisions, and new algorithms. Furthermore, we recommend four promising research avenues in this interplay: (1) incorporating new decisions relevant to data-enabled SC decisions, (2) developing data-enabled modeling approaches, (3) preprocessing parameters, and (4) developing data-enabled algorithms. Scholars can take this investigation as a means to ignite collaborative research that tackles the emerging problems in business, whereas practitioners can glean a better understanding of how to employ their OR experts to support digital SC decision-making. [ABSTRACT FROM AUTHOR]

We consider the problem of a project manager of a matrix organization responsible for the timely completion of projects arriving over time and requiring the usage of a set of resources under his/her purview. A project is defined by a set of activities, precedence relations between activities, resource requirements, a customer due date, and a priority (a weight). Arriving projects are released to a flow control system that monitors the progress of activities and schedules the set of available activities, i.e., activities that are ready to be processed, as appropriate. A critical feature of such control systems is the decision process for choosing the next activity to seize a given resource. This is the focus of this paper. In the past several decades, various heuristic priority rules have been proposed in the literature to support this type of decision in differing settings such as the job shop problem and the deterministic resource-constrained project scheduling problem. A gap exists with respect to testing the various rules all together in the more realistic dynamic-stochastic multi-project environment when the objective is to minimize weighted project tardiness. The purpose of this paper is to fill this gap. Results show that the priority rule "Weighted Critical Ratio and Shortest Processing Time" (W (CR + SPT)) is the best performing rule with respect to minimizing weighted project tardiness. W (CR + SPT) is shown to be a variant of the family of "Modified Due Date" rules first introduced by Baker and Bertrand (J Oper Manage 1(3):37–42, 1982). Repeated application of Duncan's Multiple Range test demonstrates the robustness of our findings. For the environmental parameters (due date tightness, variation of expected activity durations, and utilization of resource), the W (CR + SPT) rule is dominant with respect to weighted project tardiness among the eleven priority rules tested. Only when the number of resources is very modest (either 1 or 3 resources) or under a purely parallel resource network is W (CR + SPT) not the dominant rule. In those cases, its variant W (CR + GSPT) is the best performing rule with respect to weighted project tardiness. [ABSTRACT FROM AUTHOR]

This paper considers the problem of scheduling independent jobs with release dates and tails on m identical machines to minimize the makespan. The three main bounds are the preemptive bound, the energetic bound and the Jackson Pseudo Preemptive bound. It is known that they are very close in practice. The aim of this paper is to provide worst case instances where the slack between them is maximal and to prove tight bounds on the slack. We show that the slack is smaller than the maximal processing time. [ABSTRACT FROM AUTHOR]

Project risk management which has been rarely considered, especially in software projects, is a crucial process to complete projects successfully. This paper aims to propose a novel project risk management framework both to evaluate the project risks and to predict the success or failure of software projects based on their risk. This new framework uses Machine Learning (ML) and Multi-Attribute Decision-Making under a cloud model to effectively manage uncertainty. Based on the proposed framework, in the first stage, the important risks of the software projects are identified by an organized approach. Then, the risks are evaluated based on their probability and impacts on time, cost, and quality. In the second stage, the obtained results of the previous stage are entered into a new COPRAS method under the cloud model to rank the risks. Then, the risks are classified into various groups according to their rank. It helps project managers to gain a profound awareness of their high-priority project risks. In this paper, data on risks for fifty software projects has been collected. All the steps of the second stage are implemented on these projects in order to assess their risks. As a result, a data set whose features are nine types of software project risks and the label of success or failure of the projects is created. To recognize the pattern between risks' values and the success or failure of the projects, various efficient ML algorithms such as Naive Bayes, Logistic regression, Decision Tree, Bagging, Random Forest, and AdaBoost are applied. This framework can predict the success or failure of software projects based on their risks with good accuracy. The results depict that the Naive Bayes algorithm has the best results compared to others. [ABSTRACT FROM AUTHOR]

In this paper, two dynamic models, recently proposed to describe the adaptive repeated choices of a boundedly rational consumer, are joined together. One considers a consumer adjusting the consumption level of a given good over time according to the observed discrepancy between expected and realized utility gain and modifies the utility function according to past consumption experience, also including saturation effects when past consumption is excessive. The other one considers the same adjustment mechanism with constant preferences but with a behavioral effect that introduces a tendency (or bias) to imitate a reference group of consumers. Merging these two models, a two-dimensional nonlinear dynamical system is obtained which describes consumers that decide their next period consumption of a given good by following two different (sometimes contrasting) criteria: their own utility maximization on the one side and imitation of a reference group of consumers on the other side. This leads to a greater uncertainty with respect to the model without the behavioral bias. Such uncertainty is studied through a numerical exploration of the long-run dynamics, guided by some global dynamical features of the nonlinear model, such as the folding action of the critical curves that characterize the behavior of the iterated noninvertible map and the singularities related to the presence of a vanishing denominator, namely focal points and prefocal curves. So, the aim of the paper is twofold: on the one side, it tries to contribute to the literature on the economic theory of boundedly rational consumers represented by evolutionary and behavioral approaches; On the other side, it tries to contribute to the recent literature about the global analysis of discrete dynamical systems characterized by contact bifurcations leading to the creation of complex topological structures of the attractors and their basins of attraction. [ABSTRACT FROM AUTHOR]

In this paper we analyze the role of pollution for industry location and residence choice. We present a new economic geography (NEG) model in which manufacturing generates local pollution (that does not accumulate) and uses two types of labour input: unskilled workers that cannot migrate and work where they live; and high-skilled entrepreneurs that choose where to produce and where to live. Taking on board costless commuting or, in alternative, distance working, entrepreneurs can live in a different location from production. Both types of households enjoy utility from consuming all commodities (locally and imported variants) and suffer from local pollution. The resulting model is of the footloose entrepreneur variant, but involves two dynamic equations: the standard one governing the residential choice of entrepreneurs, and another one governing where production is located. The current paper analyses the discrete time dynamic process defined by a two-dimensional piecewise smooth map. Depending on parameters this map can have possibly coexisting attractors of various types (fixed points, cycles, closed curves as well as chaotic attractors). We analytically obtain stability conditions for the fixed points. Using numerical methods we describe also some global dynamic properties of the considered map. Finally, we propose an economic interpretation of the results concerning local stability analysis and global dynamics. [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]

Continuous changes in today's market have exposed supply chain (SC) management to severe challenges. Such changes in the market are divided into two general categories: (1) Time-dependent changes such as political, economic, and cultural changes occurring over time and require decision-makers to consider the dynamics of decisions; and (2) Sudden changes and events such as diseases and natural disasters disrupting SCs. This paper investigates the impact of both changes on the decisions of a green supply chain. Moreover, these challenges may affect the organizations' activities in the market and arouse a conflict in the members' goals, leading to competition among members. This paper studies a game model for a dynamic–stochastic supply channel SC with one manufacturer and one retailer, where two types of products (green and non-green) are sold. In this study, two models are dynamically considered: The first model without demand disruptions and the second model with demand disruptions. Finally, the results are compared. On the other hand, due to environmental goals and to survive manufacturers under disruption, the government supports manufacturers in producing green products. Implementing this cooperation, the manufacturer also invests more in improving the greening level of green products, thereby enhancing its demand. Regarding numerous disruptions common in the automotive industry, this case study is conducted on the automotive supply chain to evaluate the models, and the results are presented in the form of numerical examples. Finally, to extract managerial insights, sensitivity analysis is performed on the main parameters of the problem. [ABSTRACT FROM AUTHOR]

This paper examines demand information value and acquisition strategy for a supply chain composed of a supplier and a retailer under supplier encroachment, and considers the situation that both the enterprises can obtain information directly from the market. The problem is formulated with a Bayesian game model. A signaling mechanism is considered, i.e., the supplier's demand information will be signaled to the retailer through wholesale pricing. Under the signaling mechanism, this paper analyzes whether it is still worthy to the supplier and the retailer of acquiring information at a non-ignorable cost under dual information asymmetry. Moreover, this paper examines the interaction between channel substitution rate and information acquisition. Furthermore, subsidies for inducing information sharing and the signaling effects on the payoffs are also explored. The results propose strategy selections of encroachment and information acquisition based on cost thresholds. A double information inaccuracy effect is discovered, which comes from the fact that an enterprise receives the signal from another supply chain member as well as acquires information from the market. It is interesting to find that the information inaccuracy does not necessarily have a negative effect due to double information inaccuracy. The inaccuracy rate has a positive effect first and then a negative effect on the payoffs of the supply chain under certain conditions. The value of signaling of demand information is analyzed, revealing that lower substitution rate will make the signaling mechanism have a positive impact on the enterprises payoffs. Some managerial insights are presented in the end. [ABSTRACT FROM AUTHOR]

In this paper, we study capital allocation for dynamic risk measures, with an axiomatic approach but also by exploiting the relation between risk measures and BSDEs. Although there is a wide literature on capital allocation rules in a static setting and on dynamic risk measures, only a few recent papers on capital allocation work in a dynamic setting and, moreover, those papers mainly focus on the gradient approach. To fill this gap, we then discuss new perspectives to the capital allocation problem going beyond those already existing in the literature. In particular, we introduce and investigate a general axiomatic approach to dynamic capital allocations as well as an approach suitable for risk measures induced by g-expectations under weaker assumptions than Gateaux differentiability. [ABSTRACT FROM AUTHOR]

This article, published in the Annals of Operations Research, focuses on the integration of methods and concepts between operations research, data science, and decision analytics. The research papers in this special volume aim to enhance decision-making capabilities by combining the strengths of these fields. The volume includes 26 papers that cover a range of topics, including real-world challenges, general methodological advances, and applications in business, industry, and healthcare. The papers present new methods and approaches that are motivated by specific problems and utilize techniques such as machine learning, clustering, and data envelopment analysis. The article expresses gratitude to the referees, the editor-in-chief, and the publishers for their contributions to the special volume. [Extracted from the article]

We consider a project portfolio selection problem faced by research councils in project and call-based R &D grant programs. In such programs, typically, each applicant project receives a score value during specially-designed peer review processes. Each project also has a certain budget, estimated by its principle investigator. The problem is to select an optimal (maximum total score) subset of applicant projects under a budget constraint for the call. At the time of funding decisions, exact expenditures of projects are not known. The research councils typically don't provide more money than they funded a project to start with, so the realized total expenditure of a portfolio usually tends to be lower than the total budget, which causes budgetary slack. In this paper, we attempt to model this phenomenon in a project portfolio selection problem and show that budget utilization of a call can be increased to support more projects and hence achieve higher scientific impact. We model a project's expenditure using a mixture distribution that represents project success, underspending and cancellation situations. We develop a chance-constrained model with policy constraints. Due to the intractability of the developed model, we have shown that Normal distribution can be used for approximation. We also quantify the approximation error of our model via a theoretical bound and simulation. The proposed approach could rigorously increase the budget utilization up to 15.2% along with a prominent rise in expected number of successfully completed projects, which are remarkable metrics for public decision makers. [ABSTRACT FROM AUTHOR]

Data envelopment analysis, a non-parametric programming approach, has been extended to situations in which all total decision-making unit (DMU) outputs are fixed, and a secondary goal approach based on a minimum reduction strategy proposed to achieve an equilibrium efficient frontier to evaluate these fixed-sum output DMUs. However, the non-uniqueness of the equilibrium efficient frontier and the calculation burden of the iterative procedure have reduced the practicability of these approaches. Therefore, to address these problems, this paper developed a fairness based common equilibrium efficient frontier data envelopment analysis approach (CEEFDEA) that can guarantee the uniqueness of the common equilibrium efficient frontier and achieve such a frontier in only one step. Fairness is also included into the proposed CEEFDEA approach and the price of fairness is defined. One numerical example from previous studies and one case study focused on an efficiency evaluation of the Chinese appliance industry in 2019 are given to illustrate the effectiveness of the proposed approach. The results from the numerical example showed that the proposed CEEFDEA approach was able to achieve a fairer common equilibrium efficient frontier at the expense of a 5.85% increase in the adjustment proportion. [ABSTRACT FROM AUTHOR]

Online and offline integration is an increasingly popular method of performing modern medical services. To provide suggestions for physician selection by patients with full use of previous online and offline patient evaluations, this paper investigates online and offline social network group decision making (OAOSNGDM) in depth. With the aim of inferring the indirect trust relationships of online and offline patients, we first construct a q-rung orthopair fuzzy dual trust propagation operator based on the q-rung orthopair fuzzy trust function, which can effectively deal with inconsistency in trust functions among patients. Considering patient inconsistency in online and offline scenarios, which can increase the uncertainty of the trust relationship in cross-network propagation, we propose a q-rung orthopair fuzzy dual trust cross-network propagation operator by introducing cross-network propagation efficiency. Considering the signal-to-noise ratio, we calculate the trust propagation efficiency and introduce it into the trust propagation operators. To aggregate the trust information of multiple trust paths among patients, we introduce the Dempster rule from evidence theory which can handle the uncertainty of trust functions. In addition, to accurately determine the patient weights according to online and offline social networks, we integrate the ranking results of patients in terms of degree centrality, neighbor importance and betweenness centrality by developing an improved linear assignment method. We then propose a novel decision-making method for OAOSNGDM and design a complete decision-making process for the evaluation of physicians. Finally, we verify the effectiveness of our proposed method for the evaluation of physicians in an online and offline scenario. [ABSTRACT FROM AUTHOR]

The dynamic multiple-criteria decision-making (MCDM) approach has drawn immense attention in the decision analysis domain over recent years. An essential problem of this dynamic process is how to retrieve the utmost from historical data. Although most previous dynamic MCDM studies process historical data only with external representatives, namely scores given to alternatives on each criterion, we argue that internal patterns of historical data are also essential to achieve a more comprehensive evaluation of alternatives. Nevertheless, a data-driven methodology aiming to capture internal patterns of historical data and consequently provide decision-makers with meaningful insights is still lacking. In this paper, we propose a framework that serves as integration within which both external representations and internal patterns are used to make a more comprehensive evaluation. Then we apply our framework to student evaluations and country-level risk assessments in the contexts of additive and fuzzy measures, respectively. Both cases show that either using external representatives or internal patterns alone leads to entirely different and even biased results. On the contrary, integrating external representations and internal patterns can offer more flexibility and higher interpretation power. [ABSTRACT FROM AUTHOR]

Data Envelopment Analysis (DEA) is a mathematical programming model that calculates the relative efficiency of homogenous Decision Making Units (DMUs). The conventional DEA models used to calculate the efficiency require the exact amount of inputs and outputs; in real business situations, however, it is often impossible to determine the exact numeral value of some inputs and outputs. At the same time the Common Set of Weights (CSW) overcomes the weakness of DEA models for assessment under same conditions. On the other hands, it is important to considering the balance in evaluation and calculation of indicators. This study develops a new model to calculate the CSW in fuzzy environments, considering the balanced environment using the Balanced Scorecard (BSC). Our proposed model is linear for fairly and equitably evaluating the DMUs on the same scale, also enables us to deal with fuzzy environment and greatly reduces the computational complexities for enormous volumes of data in many real applications and treat difficulties in fuzzy DEA models. From a managerial point of view, this paper aims to provide an integrated framework to form a better strategic decision-making process about organization performance, which ultimately leads to the competitive advantages and success of the organization in the long run. Finally, in the field of performance management, the proposed model was applied to evaluate the performances of ten manufacturing enterprises in to confirm the validity and applicability of the proposed approach. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to enhance current practices in business-to-business (B2B) customer churn prediction modelling. Following the recent trend from accuracy-based to profit-driven evaluation business-to-customer churn prediction, we present a novel expected maximum profit measure for B2B customer churn (EMPB), which is used to demonstrate how current practices are suboptimal due to large discrepancies in customer value. To directly incorporate the heterogeneity of customer values and profit concerns of the company, we propose an instance-dependent profit maximizing classifier based on gradient boosting, named B2Boost. The main innovation of B2Boost is the fact that it considers these differences and incorporates them into the model construction by maximizing the objective function in terms of the EMPB. The results indicate that the expected maximal profit gains made in our analyses are substantial. This study arguments towards both deploying models based on customer-specific profitability differences, as well as evaluating based on our instance-dependent EMPB measure. [ABSTRACT FROM AUTHOR]

Clustering validity indices are designed for evaluating the performance of clustering algorithms in terms of quality of clusters. They are also used for detecting the correct number of clusters. Numerous clustering validity indices have been proposed in the literature for different types of data. However, to the best of our knowledge, there are a few well-known clustering validity indices for graph data. In this paper, we propose a new clustering validity index for graph data called the node-based clustering validity index. The main characteristic of our proposed index is that it captures the exclusive contribution of each node to the separation and compactness of the clusters in a graph. This characteristic gives an advantage to the method which is the detection of the correct number of clusters for cases that two sets of nodes are bonded to each other with a single node. We provide an illustrative example to show that many existing validity indices fail to detect the correct number of clusters in such cases. We evaluate the performance of our proposed index with several experiments on different real-world and synthetic graphs. Experiments show that our proposed node-based index outperforms the existing validity indices for different graph data. [ABSTRACT FROM AUTHOR]

This paper considers optimal pricing in a system with limited substitutable resources, such as certain goods or services. Prices for the different resources have to be set and then customers with heterogeneous preferences show up sequentially. Customers, of n types, select an item from the m available resources, depending on their valuations of the resources and the prices. The goal is to analyze this optimization problem, characterize a set of candidates to optimal solutions and provide methods for solving it. We prove that this problem is NP-hard to approximate within a factor O (n 1 - ε) for any fixed ε > 0 . Another important contribution is to prove that, the space of prices (which in principle is a continuous domain in R m ), can be reduced to a finite set of vectors of cardinality m m - 2 n m 2 m . For a deterministic version of the problem, where the customer types are known to the firm, we provide a mathematical program that chooses the best set of prices. We report extensive computational results showing the usefulness of our exact approach to solve medium size problems with up to 200 customers and different assortments of products and customer types. We then show how to approximate the stochastic model by a small number of solutions of deterministic scenarios solved using a mixed-integer linear program. [ABSTRACT FROM AUTHOR]

In a general high-dimensional process, a large number of process parameters or quality characteristics is found to be featured through their dependencies and relevance. The features that have similar characteristics or behaviors in the process operation can be categorized into multiple groups. Thus, when a few quality characteristics in the process change, it is highly probable that the process shift would have occurred in a few relevant groups. Recently, several advanced statistical process control techniques are developed to monitor the changes in high-dimensional processes under sparsity. However, monitoring schemes that utilize the grouped pattern of the quality characteristics are sparse. This paper proposes a new method to monitor the high-dimensional process when the grouped structure of the process data is observed. The proposed method identifies the potentially changed groups and individual variables within the groups based on a modified sparse group LASSO (MSGL) model. Then, a monitoring statistic is obtained using MSGL-based likelihood function to test abnormality of the process. Extensive numerical studies are conducted to demonstrate the effectiveness and efficiency of the proposed method. In addition, a real-life application of a liquefied natural gas process is presented to illustrate the proposed method. [ABSTRACT FROM AUTHOR]

Incomplete cooperative games generalize the classical model of cooperative games by omitting the values of some of the coalitions. This allows for incorporating uncertainty into the model and studying the underlying games and possible payoff distributions based only on the partial information. In this paper, we conduct a systematic investigation of incomplete games, focusing on two important classes: positive and convex games. Regarding positivity, we generalize previous results from a special class of minimal incomplete games to a general setting. We characterize the non-extendability to a positive game by the existence of a certificate and provide a description of the set of positive extensions using its extreme games. These results also enable the construction of explicit formulas for several classes of incomplete games with special structures. The second part deals with convexity. We begin with the case of non-negative, minimal incomplete games. We establish the connection between incomplete games and the problem of completing partial functions and, consequently, provide a characterization of extendability and a full description of the set of symmetric convex extensions. This set serves as an approximation of the set of convex extensions. [ABSTRACT FROM AUTHOR]

This paper introduces a new framework for cooperative games with restricted permutations, focusing on situations where not all possible player permutations occur. The framework allows players within an ordered coalition to exchange positions if it benefits them. To ensure stable connections among players, we introduce the concept of stably connected ordered coalitions, which incorporates the notions of objection and counter-objection. We then define an order-restricted game, a generalized characteristic function game derived from the original game. Additionally, inspired by the Shapley value, we propose an allowable value and provide its axiomatization. Finally, we explore several extensions of this value to further investigate its potential applications. [ABSTRACT FROM AUTHOR]

The pharmaceutical supply chain (PSC) is a highly important strategic issue in the healthcare domain because of its significant effect on human wellbeing and healthcare costs. This paper provides a mathematical optimization model to design a PSC by considering the drug's corruption. Given the importance of the sustainable development goals, three objectives are considered: economic, social, and environmental. The cost of fixed installation, transportation, holding, collection, distribution, recycling and disposal is reduced by the economic objective. A reduction in greenhouse gas emissions, water use, and nonrenewable energy consumption is achieved by the environmental objective. Based on the social objective, the number of employment opportunities, economic growth, job satisfaction, and national self-sufficiency are all increased, while the number of days lost due to work-related accidents decreases. To achieve the weight of elements in environmental and social objectives, the fuzzy best–worst method is used. Due to the inherent uncertainty in the PSC, its main parameters are referred to as unpredictable, which calls for a robust optimization approach to eliminate the model parameters' uncertainty. A fuzzy programming approach is used to solve the developed model. Numerical evidence drawn from a practical case shows that the suggested model is efficacious and valid. We suggest that the priorities and challenges of sustainable growth contribute to the determination of the PSC's managerial and operational characteristics. [ABSTRACT FROM AUTHOR]

The accurate due-date reliability evaluation of demand satisfaction is vital for due-time performance prediction of production systems. This paper proposes an improved multi-state reliability evaluation approach for production systems with finite buffers subject to time-dependent failures (TDFs), operation-dependent failures (ODFs). Instead of TDF machine model or ODF machine model, a modified machine reliability model considering both TDFs and ODFs is presented as a basis. And then an equivalent workstation reliability model is proposed by steady probability analysis of finite buffers. A united reliability calculating framework is constructed for the multi-state production system based on universal generating function. A case study of a piston production line is conducted to prove the feasibility and effectiveness of the proposed methodology. [ABSTRACT FROM AUTHOR]

The use of redundancies or spares in a system is a widely adopted technique to enhance system reliability and reduce the risk of system failure. Redundancies are typically incorporated into systems at the component or system levels. It is a significant problem to allocate appropriate redundancies into a system from a set of available options for the same. In this paper, we establish sufficient conditions to compare the reliability of coherent systems of dependent components with different sets of active redundancy, whether at the component level or the system level, based on some stochastic orders. We have obtained the results for the component lifetimes following the proportional odds (PO) model (the Marshall–Olkin family of distributions) for any lifetime distribution as a baseline distribution. We have studied the problem in the most general setup, with the consideration of coherent system that includes most of the common system structures, the consideration of non-matching spares, the consideration of dependencies of the components with different associated parameters of the copulas, and the consideration of general distribution as the baseline distribution of the PO model. We provide examples satisfying the sufficient conditions of the theoretical results. Additionally, we illustrate some of the results using real-world data. [ABSTRACT FROM AUTHOR]

The multi-state flow network (MSFN) serves as a fundamental framework for real-life network-structured systems and various applications. The system reliability of the MSFN, denoted as Rd, is defined as the probability of successfully transmitting at least d units of demand from a source to a terminal. Current analytical algorithms are characterized by their computational complexity, specifically falling into the NP-hard problem to evaluate exact system reliability. Moreover, existing analytical algorithms for calculating Rd are basically designed for predetermined values of d. This limitation hinders the ability of decision-makers to flexibly choose the most appropriate based on the specific characteristics of the given scenarios or applications. This means that these methods are incapable of simultaneously calculating system reliability for various demand levels. Therefore, this paper develops a simulation-based algorithm to estimate system reliability for all possible demand levels simultaneously such that we can eliminate the need to rely on repeat procedures for each specified d. An experimental investigation was carried out on a benchmark network and a practical network to validate the effectiveness and performance of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Given the deteriorative nature of industrial systems, implementation of advanced Preventive Maintenance (PM) strategies becomes of paramount importance to cope with the maintenance needs of ever-changing complex industrial and safety-critical systems. Conventionally, PM approaches are developed based on the perfect maintenance assumption, i.e., the underlying system is renewed to the as-good-as-new state after each preventive repair, or corrective maintenance action. Such an assumption, however, is not realistic in applications such as military machinery, power generation networks, and Cyber-Physical Systems (CPS), rendering conventional PM strategies impractical. In such application domains, it is not feasible to perform all the required maintenance actions during the available time leading to imperfect maintenance. Overlooking imperfect maintenance is critically problematic as it further deteriorates the reliability of the underlying system shortening its life span. Therefore, it is critical to perform optimal maintenance decisions under imperfect maintenance assumptions. While Geometric Process (GP) is broadly used for imperfect maintenance modeling and analysis of repairable systems, its utilization to describe the failure mechanism of production systems/processes is still in its infancy. Existing works, typically, consider restrictive assumptions to simplify the maintenance models, which limits their applicability. This paper addresses this gap and proposes a rigorous mathematical model without the incorporation of restrictive assumptions. More specifically, we consider a system for which the operational states are observable through inspections performed at specified time points and only the failure state is immediately observable. Upon the inspection, if the system is found to be in a partially-failed state, a Minor Repair (MIR) action is conducted. The effect of MIR is imperfect and MIR can only be conducted a maximum number of N times during a production cycle. After performing a MIR action, the failure mechanism of the system changes according to a stochastic decreasing GP. When the system enters the failure state, a Major Repair (MJR) action is conducted, which brings the system back to the as-good-as-new state. A comprehensive set of numerical examples, comparative studies, and sensitivity analyses are conducted to evaluate the efficacy of the proposed maintenance policy. [ABSTRACT FROM AUTHOR]

In system reliability, coherent systems play a central role since without them any system is considered flawed. A system is coherent if all the components are relevant to that system i.e. functioning of every component has an effect in the functioning of the system and the structure of the system is monotone. Monotone structure means that improving any component will not deteriorate the system. A mixed system is a stochastic mixture of coherent systems and any coherent system is a special case of a mixed system. In reliability engineering, one major problem is to compare various coherent and mixed systems so that the better system can be used to increase the overall reliability. Another important problem is to measure the complexity of systems. A highly complex system will naturally have a higher running and maintenance cost associated with it and it is desirable for a reliability engineer to have an understanding regarding the complexity of systems beforehand. In this paper, we address these two problems from an information theoretic approach. Extropy is a measure of information which is the dual of the famous Shannon entropy measure. Recently, a new measure related to extropy, called cumulative residual extropy (CREx), was introduced in the literature by Jahanshahi et al. (Probab Eng Inf Sci 1–21, 2019). This measure is based on the survival function of the underlying random variable and it has some advantages over extropy measure. In this work, we analyze the CREx measure for coherent and mixed systems and develop some comparison results among systems. We also obtain some bounds of CREx of coherent and mixed systems consisting of independent and identically distributed (iid) and dependent and identically distributed (d.i.d.) components. We propose a new divergence measure to calculate the complexity of systems having iid components. Also, we introduced a new discrimination measure to compare various coherent and mixed systems when pairwise comparisons by usual stochastic order is not possible. Finally, we discuss analysis of the CREx measure of coherent systems having heterogeneous components. We also provide applications involving redundancy allocation. Various numerical examples are considered for illustrative purposes. [ABSTRACT FROM AUTHOR]

As the process yield has significantly raised because of the advanced development of manufacturing technology today, engineers would logically attempt to inspect fewer sample items for the quality evaluation of processes or products. Therefore, in this paper, an efficient sampling inspection method based on the process yield index Spk is developed for lot sentencing, wherein the inspection is performed only on a fractional submitted lot rather than examining every following submission. Both the average sample number (ASN) and operating characteristic (OC) functions of the proposed method are derived on the basis of the Markov chain technique. Further, an optimization model that minimizes the ASN and constrains two OC functions restricted to given quality requirements and tolerable risks is constructed. Performance comparisons in terms of economy and discriminatory power are analyzed by contrasting ASN and OC curves with existing Spk-based methods under the same quality conditions to emphasize the superiority of the proposed method. For easy implementation, we prove the applicability of the proposed method by demonstrating a case study taken from an integrated circuit packaging company. [ABSTRACT FROM AUTHOR]

Availability contracting is a prominent procurement strategy among many defense and other capital equipment suppliers and their customers. Under availability contracting, the revenue generated for a service provider is a function of equipment/system availability over the predetermined contract period. A major challenge in implementing availability contracting is predicting the availability of equipment, defining the target availability, and determining the best contract duration based on the target availability. In this paper, we extend the classical operational availability model for estimating equipment availability under hierarchical maintenance, which is frequently used for complex equipment such as aircraft. Under an availability contract, bounds have been proposed on system-level operational availability. The current study also develops optimization models for finding the optimal availability contract duration under different scenarios: stochastic and non-stochastic contract durations; contract written at parts level, system level; minimizing total maintenance costs with minimum level of target operational availability as a constraint etc. The study also shows that the optimal contract duration is a non-decreasing function of spares on hand, and the inherent availability of system part. The results from the study have been validated using numerical study with both simulated and real data. [ABSTRACT FROM AUTHOR]

Providing accurate travel time prediction plays an important role in Intelligent Transportation System. It is critical in urban travel decision making and significant for traffic control. The main limitation of existing studies is that they do not fully consider the spatiotemporal dependence, exogenous dependence and dynamics of travel time prediction. In this paper, we propose a deep learning model, called DLSF-GR, based on graph neural networks and recurrent neural networks for travel time prediction, which combines multiple learning components to improve learning efficiency. We evaluate the proposed model on the real-world trip dataset in China by comparing with several state-of-the-art methods. The results demonstrate that the developed model performs the best in terms of all considered indicators compared to several state-of-the-art methods, and that the developed specified cross-validation method can enhance the performance of the comparison methods against to the random cross-validation method. [ABSTRACT FROM AUTHOR]

This paper presents a generalized software reliability modeling framework that predominantly encompasses randomness of field environment, fault reduction factor (FRF), and change point to study their simultaneous effect on software bug removal process. To address and capture unpredicted randomness of the field environment in testing phase it is assumed that the environment before and after a change point may not be the same and therefore there is a need to consider distinct environment factors before and after the change point in the modeling of bug removal action. Based on the general approach, three new change point-based software reliability growth models (SRGMs) are proposed incorporating time-dependent exponentiated Weibull (EW) FRF and distinct random environment distributions. To elaborate, in SRGM-I and SRGM-II, a steady environment is supposed before the change point whereas one-parameter exponential environment is assumed after the change point in SRGM-I and a two-parameter Gamma environment is considered after the change point in SRGM-II while in SRGM-III, exponential environment before change point and Gamma environment after change point are taken into consideration. In addition, a few established FRF-based models are deduced from the proposed general approach. Estimation of parameters of proposed SRGMs is carried out in two phases using three real test data sets and their validation is evaluated through several performance measures including coefficient of determination, mean square error, bias, variation, root mean square prediction error and Akaike information criteria. Fitting of the models to the datasets is also examined in related goodness of fit curves. Further, numerical illustration is worked out to conduct a cost-reliability assessment and suggest optimum release time. [ABSTRACT FROM AUTHOR]

In this paper, a new repairable k-out-of-n system model is proposed, in which the deviation cost of the system is taken into account. Firstly, the standard k-out-of-n system is replaced at planned time or at the failure time. Next, replacement policies are studied at planned time or periodic time when k is a constant. Thirdly, replacement policies are studied at planned time or periodic time when k is a random number, respectively. Finally, replacement policies are planned at the completion of random working time. The expected cost rate functions and its optimum replacement policies are derived analytically for each model. Meanwhile, optimum number of units is designed. The analytical solution of replacement policies for each model is discussed. Numerical examples are given to demonstrate our results. [ABSTRACT FROM AUTHOR]

The paper presents the model resilience measurement based on the complex network theory and analyzes the resilience of project portfolio network considering risk propagation. The model can be used to evaluate the resilience and improve its success probability of projects in the portfolio network. Firstly, the research analyzes the dynamic changes of the portfolio network derived from the construction of the project portfolio matrix, as well as the main factors to be taken into consideration in measuring the resilience of the project portfolio. Further, the research measures the resilience of the project portfolio network according to the node attributes (project) and the relationship attributes (the relationship that one project will impact another in the portfolio network) respectively. Then, to integrate the resilience of the projects and influence relationship between projects, the research proposes the dynamic PageRank algorithm to analyze the resilience of the project portfolio based on the analysis of traditional PageRank algorithm. In addition, resilience is not only affected by the projects and its influence relationship between them, but is also affected by risk propagation. Therefore, the research presents a model for analyzing the portfolio network resilience considering multiple risk propagation. Finally, a research and development project portfolio are taken as an example to demonstrate the effectiveness of the method presented in this research. Our approach can be used by managers to identify the scores of project resilience capacity in portfolio network. Our method explicitly allows to uncover the most resilient projects considering the resilience of project (node) and their influence relationship (network structure), and the risk propagation. Utilizing the outcomes of this research can enhance the capacity of the whole project portfolio to manage risks and improve the success probability of the whole project portfolio by enhancing network resilience. [ABSTRACT FROM AUTHOR]

Power distribution networks should strive for reliable delivery of energy. In this paper, we support this endeavor by addressing the Maintenance Resources Allocation Problem (MRAP). This problem consists of scheduling preventive maintenance plans on the equipment of distribution networks for a planning horizon, seeking the best trade-offs between system reliability and maintenance budgets. We propose a novel integer linear programming (ILP) formulation to effectively model and solve the MRAP for a single distribution network. The formulation also enables flexibility to suit new developments, such as different reliability metrics and smart-grid innovations. Then we develop a straightforward ILP formulation to address the MRAP for several distribution networks which takes the advantages of exchanging maintenance information between local agents and upper management. Using a general-purpose ILP solver, we performed computational experiments to assess the performance of the proposed approaches. Optimal maintenance trade-offs were achieved with the new formulations for real-scale distribution networks within short running times. To the best of our knowledge, this is the first time that the MRAP is optimally solved using ILP, for single or multiple distribution networks. [ABSTRACT FROM AUTHOR]

Multiple stakeholders involved in the decision-making process have inherent interests that are sought to be maximized along with the collective goals specified by the organization as a whole. Due to the nature of these interests being diverse and often conflicting, an apparent dispute emerges which results in an even greater chaotic situation among stakeholders. Despite the introduction of several analytical and optimization tools to put the perspective of stakeholders into balance, there appears to be an inadequacy of frameworks that objectively incorporates the interests of stakeholders into a single metric. To advance this significant gap, this paper proposes a multiple stakeholder-based target-oriented robust optimization (MS-TORO) model which aggregates the interests of stakeholders into a single model with account for uncertainty. The conceptual and mathematical properties of the classical TORO model are used as a part of the MS-TORO framework to generate a satisficing solution with respect to the interests of multiple stakeholders. To demonstrate the applicability and validity of the proposed model, a hypothetical case study is performed in the decision process involving the post-departure aircraft rerouting problem. The system of the rerouting process involves multiple stakeholders each with inherent interests in an uncertain environment. Implementing the model provided solutions which satisfices the interests of multiple stakeholders as represented by the target metric minimizing the deviation from the performance targets of stakeholders. The proposed model not only confirmed the preferences of stakeholders in instances when a common route is selected but also showed non-biased solutions, thereby, adequately balancing interests. [ABSTRACT FROM AUTHOR]

Aiming to improve the robustness and adaptiveness of the recently investigated capped norm linear discriminant analysis (CLDA), this paper proposes a regularized linear discriminant analysis based on the generalized capped l 2 , q -norm (GCLDA). Compared to CLDA, there are two improvements in GCLDA. Firstly, GCLDA uses the capped l 2 , q -norm rather than the capped l 2 , 1 -norm to measure the within-class and between-class distances for arbitrary q > 0 . By selecting an appropriate q, GCLDA is adaptive to different data, and also removes extreme outliers and suppresses the effect of noise more effectively. Secondly, by taking into account a regularization term, GCLDA not only improves its generalization ability but also avoids singularity. GCLDA is solved through a series of generalized eigenvalue problems. Experiments on an artificial dataset, some real world datasets and a high-dimensional dataset demonstrate the effectiveness of GCLDA. [ABSTRACT FROM AUTHOR]

The vector autoregressive (VAR) model has been popularly employed in operational practice to study multivariate time series. Despite its usefulness in providing associated metrics such as the impulse response function (IRF) and forecast error variance decomposition (FEVD), the traditional VAR model estimated via the usual ordinary least squares is vulnerable to outliers. To handle potential outliers in multivariate time series, this paper investigates two robust estimation methods of the VAR model, the reweighted multivariate least trimmed squares and the multivariate MM-estimation. The robust information criteria are also proposed to select the appropriate number of temporal lags. Via extensive simulation studies, we show that the robust VAR models lead to much more accurate estimates than the original VAR in the presence of outliers. Our empirical results include logged daily realized volatilities of six common safe haven assets: futures of gold, silver, Brent oil and West Texas Intermediate (WTI) oil and currencies of Swiss Francs and Japanese Yen. Our sample covers July 2017–June 2020, which includes the history-writing price drop of WTI on April 20, 2020. Our baseline results suggest that the traditional VAR model may significantly overestimate some parameters, as well as IRF and FEVD metrics. In contrast, robust VAR models provide more reliable results, the validity of which is verified via various approaches. Empirical implications based on robust estimates are further illustrated. [ABSTRACT FROM AUTHOR]

As one of the most popular techniques for performance evaluation, Data Envelopment Analysis (DEA) has been widely applied in many areas. However, the self-evaluation used in DEA leaves it open to much criticism. Moreover, most researchers have ignored the fact that reality abounds with uncertainty and have assumed that the data used for evaluation is deterministic and accurate. Both assumptions make it difficult to evaluate the efficiency of real-world production processes correctly and reasonably. In this paper, we propose a series of robust cross-efficiency (RCE) models based on robust optimization theory and cross-efficiency to deal with these problems. First of all, the proposed RCE models allow the conservatism level to be adjusted easily to suit the attitude of the decision-maker towards uncertainty. In addition, the RCE models have better discrimination power than the existing robust CCR models. We present two applications to demonstrate the effectiveness and stability of our models. [ABSTRACT FROM AUTHOR]