Showing papers in "Annals of Operations Research in 2021"

Consensus reaching for MAGDM with multi-granular hesitant fuzzy linguistic term sets: a minimum adjustment-based approach

Abstract: Due to the uncertainty of decision environment and differences of decision makers’ culture and knowledge background, multi-granular HFLTSs are usually elicited by decision makers in a multi-attribute group decision making (MAGDM) problem. In this paper, a novel consensus model is developed for MAGDM based on multi-granular HFLTSs. First, it is defined the group consensus measure based on the fuzzy envelope of multi-granular HFLTSs. Afterwards, an optimization model which aims to minimize the overall adjustment amount of decision makers’ preference is established. Based on the model, an iterative algorithm is devised to help decision makers reach consensus in MAGDM with multi-granular HFLTSs. Numerical results demonstrate the characteristics of the proposed consensus model.

Prediction of cryptocurrency returns using machine learning

Abstract: In this study, the predictability of the most liquid twelve cryptocurrencies are analyzed at the daily and minute level frequencies using the machine learning classification algorithms including the support vector machines, logistic regression, artificial neural networks, and random forests with the past price information and technical indicators as model features. The average classification accuracy of four algorithms are consistently all above the 50% threshold for all cryptocurrencies and for all the timescales showing that there exists predictability of trends in prices to a certain degree in the cryptocurrency markets. Machine learning classification algorithms reach about 55–65% predictive accuracy on average at the daily or minute level frequencies, while the support vector machines demonstrate the best and consistent results in terms of predictive accuracy compared to the logistic regression, artificial neural networks and random forest classification algorithms.

Exiting the COVID-19 pandemic: after-shock risks and avoidance of disruption tails in supply chains.

Abstract: Entering the COVID-19 pandemic wreaked havoc on supply chains Reacting to the pandemic and adaptation in the "new normal" have been challenging tasks Exiting the pandemic can lead to some after-shock effects such as "disruption tails" While the research community has undertaken considerable efforts to predict the pandemic's impacts and examine supply chain adaptive behaviors during the pandemic, little is known about supply chain management in the course of pandemic elimination and post-disruption recovery If capacity and inventory management are unaware of the after-shock risks, this can result in highly destabilized production-inventory dynamics and decreased performance in the post-disruption period causing product deficits in the markets and high inventory costs in the supply chains In this paper, we use a discrete-event simulation model to investigate some exit strategies for a supply chain in the context of the COVID-19 pandemic Our model can inform managers about the existence and risk of disruption tails in their supply chains and guide the selection of post-pandemic recovery strategies Our results show that supply chains with postponed demand and shutdown capacity during the COVID-19 pandemic are particularly prone to disruption tails We then developed and examined two strategies to avoid these disruption tails First, we observed a conjunction of recovery and supply chain coordination which mitigates the impact of disruption tails by demand smoothing over time in the post-disruption period Second, we found a gradual capacity ramp-up prior to expected peaks of postponed demand to be an effective strategy for disruption tail control

Does data-driven culture impact innovation and performance of a firm? An empirical examination

Abstract: Data-driven culture is considered to bring business-oriented cultural transformation to a firm. It is considered to provide substantial dividends to the firms’ product and process innovations. Recently, several firms have been using different advanced technology-embedded business analytics (BA) tools to improve their business performance. Again, advancement of information and communication technology has helped firms to explore the option to use BA tools with artificial intelligence. This has brought radical change in the business-oriented cultural landscape of the firms to arrive at accurate decision-making to improve their innovation and performance. In this perspective, the aim of this study is to show how a firm’s data-driven culture impacts its product and process innovation, which in turn improves its performance and provides better competitive advantage in the current business environment. With the help of background study, a resource-based view model and different theories, a conceptual model has been developed. The conceptual model has been validated with 456 usable responses from the employees of different firms using different business analytics tools. The study highlights that data-driven culture highly influences both product and process innovation, making the firm more competitive in the industry. In this study, leadership support and data-driven culture have been taken as moderators, whereas firm size, firm age and industry type have been taken as control variables.

Artificial intelligence-driven innovation for enhancing supply chain resilience and performance under the effect of supply chain dynamism: an empirical investigation

Abstract: Supply chain resilience (SCRes) and performance have become increasingly important in the wake of the recent supply chain disruptions caused by subsequent pandemics and crisis Besides, the context of digitalization, integration, and globalization of the supply chain has raised an increasing awareness of advanced information processing techniques such as Artificial Intelligence (AI) in building SCRes and improving supply chain performance (SCP) The present study investigates the direct and indirect effects of AI, SCRes, and SCP under a context of dynamism and uncertainty of the supply chain In doing so, we have conceptualized the use of AI in the supply chain on the organizational information processing theory (OIPT) The developed framework was evaluated using a structural equation modeling (SEM) approach Survey data was collected from 279 firms representing different sizes, operating in various sectors, and countries Our findings suggest that while AI has a direct impact on SCP in the short-term, it is recommended to exploit its information processing capabilities to build SCRes for long-lasting SCP This study is among the first to provide empirical evidence on maximizing the benefits of AI capabilities to generate sustained SCP The study could be further extended using a longitudinal investigation to explore more facets of the phenomenon

Green data analytics, blockchain technology for sustainable development, and sustainable supply chain practices: evidence from small and medium enterprises

Abstract: With attempts being made by both governments and organizations to reduce environmental hazards, blockchain technology is becoming a crucial tool for attaining supply chain sustainability in small and medium enterprises (SME). Recognizing this fact, this study investigates the effect of blockchain technology on sustainable supply chain practices to improve organizational performance. In this investigation, data was gathered from 364 respondents, including middle and upper-level managers from SME manufacturing companies in China and Pakistan. The relationships among the endogenous and exogenous variables were tested by employing partial least squares structural equation modeling (PLS-SEM). The primary results suggest that blockchain technology and green information systems positively influence sustainable supply chain practices. Moreover, green information systems and sustainable supply chain practices possess a significant positive association. Sustainable supply chain practices have a positive and significant relationship with sustainability; i.e., operational, environmental, and economic performance. The findings also reveal that three dimensions of organizational sustainability—i.e., operational, environmental, and economic performance—have a significant effect on organizational performance. Additionally, the results of this study provide valuable insights into blockchain technology and offer policy implications for manufacturers and legislators regarding the implementation and promotion of green supply chain practices.

Invesitigation of finance industry on risk awareness model and digital economic growth

Abstract: Financial risk is unintended to lose money on an enterprise or investment. Credit risk, Liquidity risk, and operational risk are some more prevalent and unique financial concerns. This is a form of risk that can lead to a capital loss for stakeholders. Building a company from the bottom up is expensive. Any firm may need to go for cash outside to develop at some time in their lives. Financial hazards occur and influence almost every person in various forms and sizes. Digital Financial Services are financial services that rely on customer distribution and the use of digital technologies. While digital financial inclusion (DFI) is important in stimulating economic growth, there is only relatively little empirical data. But whether digital finance is the solution both the bad and the good results of financial inclusion raise. This essay will investigate the importance of digital financial inclusion, utilizing information and communications technology (DFI-ICT) techniques to promote sustainable growth via economic stability. Fast digital technology is currently being used to deliver financial services considerably reduced cost, thereby enhancing financial inclusion and allowing large-scale economic productivity improvements. Although there has been a broad-ranging mention of the benefits of digital finance—financial services offered through mobile telephones, the internet, or cards—we try to measure the size of the economic effect. The experimental result shows that the classification risk level ratio is achieved to 18.9%, and the error rate of classification of the model is checked.

Supply chain disruptions and resilience: a major review and future research agenda

Abstract: Our study examines the literature that has been published in important journals on supply chain disruptions, a topic that has emerged the last 20 years, with an emphasis in the latest developments in the field. Based on a review process important studies have been identified and analyzed. The content analysis of these studies synthesized existing information about the types of disruptions, their impact on supply chains, resilience methods in supply chain design and recovery strategies proposed by the studies supported by cost–benefit analysis. Our review also examines the most popular modeling approaches on the topic with indicative examples and the IT tools that enhance resilience and reduce disruption risks. Finally, a detailed future research agenda is formed about SC disruptions, which identifies the research gaps yet to be addressed. The aim of this study is to amalgamate knowledge on supply chain disruptions which constitutes an important and timely as the frequency and impact of disruptions increase. The study summarizes and builds upon the knowledge of other well-cited reviews and surveys in this research area.

Adoption and use of AI tools: a research agenda grounded in UTAUT

Abstract: This paper is motivated by the widespread availability of AI tools, whose adoption and consequent benefits are still not well understood. As a first step, some critical issues that relate to AI tools in general, humans in the context of AI tools, and AI tools in the context of operations management are identified. A discussion of how these issues could hinder employee adoption and use of AI tools is presented. Building on this discussion, the unified theory of acceptance and use of technology is used as a theoretical basis to propose individual characteristics, technology characteristics, environmental characteristics and interventions as viable research directions that could not only contribute to the adoption literature, particularly as it relates to AI tools, but also, if pursued, such research could help organizations positively influence the adoption of AI tools.

Conversational commerce: entering the next stage of AI-powered digital assistants

Abstract: Digital assistant is a recent advancement benefited through data-driven innovation. Though digital assistants have become an integral member of user conversations, but there is no theory that relates user perception towards this AI powered technology. The purpose of the research is to investigate the role of technology attitude and AI attributes in enhancing purchase intention through digital assistants. A conceptual model is proposed after identifying three major AI factors namely, perceived anthropomorphism, perceived intelligence, and perceived animacy. To test the model, the study employed structural equation modeling using 440 sample. The results indicated that perceived anthropomorphism plays the most significant role in building a positive attitude and purchase intention through digital assistants. Though the study is built using technology-related variables, the hypotheses are proposed based on various psychology-related theories such as uncanny valley theory, the theory of mind, developmental psychology, and cognitive psychology theory. The study’s theoretical contributions are discussed within the scope of these theories. Besides the theoretical contribution, the study also offers illuminating practical implications for developers and marketers’ benefit.

Fighting against COVID-19: what operations research can help and the sense-and-respond framework.

Abstract: COVID-19 is affecting all walks of life. To deal with it, we need to make use of scientifically sound tools and models. Operations research (OR), as a well-established field which focuses on deploying analytical tools to solving decision making problems, comes to the rescue. In this paper, by examining the OR literature and practices related to pandemics (including COVID-19), we discuss what OR can help to tackle challenges under COVID-19. We classify the literature into three stages, namely "before pandemic", "during pandemic" and "after pandemic". We examine the related literature and reveal the respective research areas and OR methods employed. Then, we propose a future research agenda. Finally, we establish the sense-and-respond OR framework regarding what specific actions should be taken to cope with COVID-19 from the perspectives of governments, healthcare and non-profit-making organizations, and businesses. We believe that the findings of this paper lay the solid foundation to stimulate further OR studies to combat COVID-19.

Supply chain viability: conceptualization, measurement, and nomological validation.

Abstract: Supply chain viability (SCV) is an emerging concept of growing importance in operations management. This paper aims to conceptualize, develop, and validate a measurement scale for SCV. SCV is first defined and operationalized as a construct, followed by content validation and item measure development. Data have been collected through three independent samplings comprising a total of 558 respondents. Both exploratory and confirmatory factor analyses are used in a step-wise manner for scale development. Reliability and validity are evaluated. A nomological model is theorized and tested to evaluate nomological validity. For the first time, our study frames SCV as a novel and distinct construct. The findings show that SCV is a hierarchical and multidimensional construct, reflected in organizational structures, organizational resources, dynamic design capabilities, and operational aspects. The findings reveal that a central characteristic of SCV is the dynamic reconfiguration of SC structures in an adaptive manner to ensure survival in the long-term perspective. This research conceptualizes and provides specific, validated dimensions and item measures for SCV. Practitioner directed guidance and suggestions are offered for improving SCV during the COVID-19 pandemic and future severe disruptions.

Forecasting gold price with the XGBoost algorithm and SHAP interaction values

Abstract: Financial institutions, investors, mining companies and related firms need an effective accurate forecasting model to examine gold price fluctuations in order to make correct decisions. This paper proposes an innovative approach to accurately forecast gold price movements and to interpret predictions. First, it compares six machine learning models. These models include two very recent methods: the eXtreme Gradient Boosting (XGBoost) and CatBoost. The empirical findings indicate the superiority of XGBoost over other advanced machine learning models. Second, it proposes Shapley additive explanations (SHAP) in order to help policy makers to interpret the predictions of complex machine learning models and to examine the importance of various features that affect gold prices. Our results illustrate that the utilization of XGBoost along with SHAP approach could provide a significant boost in increasing the gold price forecasting performance.

A structural mathematical model on two echelon supply chain system

Abstract: As the people are becoming conscious about protection of the environment from pollutant caused by human beings, businesses are adopting green technology to procure green products to save the environment from pollution. Consequently, it is a challenging task at the firm manager to capture the market providing best green quality at fair price in a given economy. The paper plans to discuss two situations in two models. In model 1, the optimal green quality and sales prices of the manufacturer and the retailer in both decentralize and centralize systems of a two-echelon supply chain system are investigated. The profit functions of the manufacturer and the retailer include procurement costs, selling prices and cost for green level development and then it is analyzed by calculus method to obtain the optimal values of the decision variables. The model 2 focuses on price competition of two substitute products where demand of the end customers depends on price and quality of green product. Both the firms of green and regular products manufacturer are corporate social responsible. In this model, profit functions of the firms 1 & 2 are formulated separately considering revenues from sales items, cost of green quality and contribution for activities of social responsibility. The main objective is to find out optimal prices and green quality in order to maximize the profit functions of individual and integrated systems. The proposed models are analyzed mathematically and numerical examples are illustrated to justify the feasibility of the model in reality.

Blockchain technology’s impact on supply chain integration and sustainable supply chain performance: Evidence from the automotive industry

Abstract: The study investigates the relationship between the information and communication-enabled supply chain integration (SCI) and sustainable supply chain performance (SSCP). Moreover, to the best of our knowledge, there is no empirical evidence on the impact of blockchain technologies (BT) on the SSCP. Therefore, the primary aim of this study is to assess the relationship between BT and SSCP. More specifically, the study was conducted to examine the direct influence of BT on SCI and SSCP and the interactive effect of BT and SCI on SSCP. Based on the dynamic capability theoretical lens, the present study conceptualizes the use of BT as a specific IT resource to collaborate and reconfigure the ties with the upstream and downstream supply chain members to achieve SSCP. The results of the study support the hypothesis stating that BT positively influences the SSCP. The results recognize the role of SCI as a significant mediating variable between the BT and SSCP. The result indicates the strong influence of SCI with full mediation effect on the relationship between the BT and SSCP.

Designing a sustainable closed-loop pharmaceutical supply chain in a competitive market considering demand uncertainty, manufacturer's brand and waste management.

Abstract: Pharmaceutical supply chain (PSC) is one of the most important healthcare supply chains and the recent pandemic (COVID-19) has completely proved it. Also, the environmental and social impacts of PSCs are undeniable due to the daily entrance of a large amount of pharmaceutical waste into the environment. However, studies on closed-loop PSCs (CLPSC) are rarely considered real-world requirements such as competition among diverse brands of manufacturers, the dependency of customers’ demand on products’ price and quality, and diverse reverse flows of end-of-life medicines. In this study, a scenario-based Multi-Objective Mixed-Integer Linear Programming model is developed to design a sustainable CLPSC, which investigates the reverse flows of expired medicines as three classes (must be disposed of, can be remanufactured and can be recycled). To study the competitive market and deal with demand uncertainty, a novel scenario-based game theory model is proposed. The demand function for each brand depends on the price and quality provided. Then, a hybrid solution approach is provided by combining the LP-metrics method with a heuristic algorithm. Furthermore, a real case study is investigated to evaluate the application of the model. Finally, sensitivity analysis and managerial insights are provided. The numerical results show that the proposed classification of reverse flows leads to proper waste management, making money, and reducing both disposal costs and raw material usage. Moreover, competition increases PSCs performance and improves the supply of products to pharmacies.

Crypto price discovery through correlation networks

Abstract: We aim to understand the dynamics of crypto asset prices and, specifically, how price information is transmitted among different bitcoin market exchanges, and between bitcoin markets and traditional ones. To this aim, we hierarchically cluster bitcoin prices from different exchanges, as well as classic assets, by enriching the correlation based minimum spanning tree method with a preliminary filtering method based on the random matrix approach. Our main empirical findings are that: (i) bitcoin exchange prices are positively related with each other and, among them, the largest exchanges, such as Bitstamp, drive the prices; (ii) bitcoin exchange prices are not affected by classic asset prices, but their volatilities are, with a negative and lagged effect.

Artificial intelligence for decision support systems in the field of operations research: review and future scope of research

Abstract: Operations research (OR) has been at the core of decision making since World War II, and today, business interactions on different platforms have changed business dynamics, introducing a high degree of uncertainty. To have a sustainable vision of their business, firms need to have a suitable decision-making process at each stage, including minute details. Our study reviews and investigates the existing research in the field of decision support systems (DSSs) and how artificial intelligence (AI) capabilities have been integrated into OR. The findings of our review show how AI has contributed to decision making in the operations research field. This review presents synergies, differences, and overlaps in AI, DSSs, and OR. Furthermore, a clarification of the literature based on the approaches adopted to develop the DSS is presented along with the underlying theories. The classification has been primarily divided into two categories, i.e. theory building and application-based approaches, along with taxonomies based on the AI, DSS, and OR areas. In this review, past studies were calibrated according to prognostic capability, exploitation of large data sets, number of factors considered, development of learning capability, and validation in the decision-making framework. This paper presents gaps and future research opportunities concerning prediction and learning, decision making and optimization in view of intelligent decision making in today’s era of uncertainty. The theoretical and managerial implications are set forth in the discussion section justifying the research questions.

Handling of uncertainty in medical data using machine learning and probability theory techniques: a review of 30 years (1991–2020)

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

Herding and feedback trading in cryptocurrency markets

Abstract: This paper examines the extent to which herding and feedback trading behaviors drive price dynamics across nine major cryptocurrencies. Using sample price data from bitcoin, ethereum, XRP, bitcoin cash, EOS, litecoin, stellar, cardano and IOTA, respectively, we document heterogeneity in the types of feedback trading strategies investors utilize across markets. Whereas some cryptocurrency markets show evidence of herding, or, 'trend chasing', behaviors, in other markets we show evidence of contrarian-type behaviors. These findings are important because they elucidate upon, firstly, what forces drive cryptocurrency markets and, secondly, how this type of trading behavior affects autocorrelation patters for cryptocurrencies. Finally, and from our intertemporal asset pricing model, we shed new light on the observed nature of the risk-return tradeoffs for each of our sampled cryptocurrencies.

Technical trading and cryptocurrencies

Abstract: This paper carries out a comprehensive examination of technical trading rules in cryptocurrency markets, using data from two Bitcoin markets and three other popular cryptocurrencies. We employ almost 15,000 technical trading rules from the main five classes of technical trading rules and find significant predictability and profitability for each class of technical trading rule in each cryptocurrency. We find that the breakeven transaction costs are substantially higher than those typically found in cryptocurrency markets. To safeguard against data-snooping, we implement a number of multiple hypothesis procedures which confirms our findings that technical trading rules do offer significant predictive power and profitability to investors. We also show that the technical trading rules offer substantially higher risk-adjusted returns than the simple buy-and-hold strategy, showing protection against lengthy and severe drawdowns associated with cryptocurrency markets. However there is no predictability for Bitcoin in the out-of-sample period, although predictability remains in other cryptocurrency markets.

A green inventory model with the effect of carbon taxation

Abstract: Nowadays green retail practices are increasing in number. In this paper, an inventory model is formulated with retail investments in green operations including variable holding cost. In the proposed model, the demand rate depends on selling-price as well as green concern level. The main objective of the study is to find optimal replenishment time and optimal green concern level by considering profit maximization. Here, the effect of greening-level on purchasing-price and selling-price is shown. By investing in green operations, a retailer creates considerable impact on profitability. As the greening level increases, the profit of a retailer increases. Product with lower deterioration rate would increase the profit. Based on the above consideration, the mathematical model is designed by allowing carbon tax. Then, the model is explored by numerical examples. Mathematica software is used to obtain global maximum solution to the optimal cycle time and optimal greening-level. A sensitivity analysis with respect to major parameters is performed in order to access the stability of the model. The paper concludes with conclusions and an outlook of possible future directions is depicted.

Gresilient supplier assessment and order allocation planning

Abstract: Companies are under pressure to re-engineer their supply chains to ‘go green’ while simultaneously improving their resilience to cope with unexpected disruptions where the supplier selection decision plays a strategic role. We present a new approach to supplier evaluation and allocating the optimal order quantity from each supplier with respect to green and resilience (Gresilience) characteristics. An integrated framework that considers traditional business, green and resilience criteria and sub-criteria was developed, followed by a calculation of importance weight of criteria and sub-criteria using analytical hierarchy process (AHP). We evaluate suppliers using the technique for order of preference by similarity to ideal solution (TOPSIS). The obtained weights from AHP and TOPSIS were integrated into a developed multi-objective programming model used as an order allocation planner and the e-constraint method was used to solve the multi-objective optimization problem. TOPSIS was applied to select the final Pareto solution based on its closeness from the ideal solution. The applicability and effectiveness of the proposed approach was illustrated using a real case study through a comparatively meaningful ranking of suppliers. The study provides a helpful aid for managers seeking to improve their supply chain resilience along with ‘go green’ responsibilities.

Multi-objective solid transportation-location problem with variable carbon emission in inventory management: a hybrid approach

Abstract: The most important strategic issue for several industries is where to find facilities so as to discover a transportation path for optimizing the objectives at the same time. This paper acquaints a streamlining model with incorporate the facility location problem, solid transportation problem, and inventory management under multi-objective environment. The aims of the stated formulation are multi-fold: (i) to seek the optimum locations for potential facilities in Euclidean plane; (ii) to find the amount of distributed commodities; and (iii) to reduce the overall transportation cost, transportation time, and inventory cost along with the carbon emission cost. Here, variable carbon emission cost is taken into consideration because of the variable locations of facilities and the amount of distributed products. After that, a new hybrid approach is introduced dependent on an alternating locate-allocate heuristic and the intuitionistic fuzzy programming to get the Pareto-optimal solution of the proposed formulation. In fact, the performances of our findings are discussed with two numerical examples. Sensitivity analysis is executed to check the resiliency of the parameters. Ultimately, managerial insights, conclusions and avenues of future studies are offered at the end of this study.

A sustainable-resilience healthcare network for handling COVID-19 pandemic.

Abstract: In this paper, a new production, allocation, location, inventory holding, distribution, and flow problems for a new sustainable-resilient health care network related to the COVID-19 pandemic under uncertainty is developed that also integrated sustainability aspects and resiliency concepts. Then, a multi-period, multi-product, multi-objective, and multi-echelon mixed-integer linear programming model for the current network is formulated and designed. Formulating a new MILP model to design a sustainable-resilience healthcare network during the COVID-19 pandemic and developing three hybrid meta-heuristic algorithms are among the most important contributions of this research. In order to estimate the values of the required demand for medicines, the simulation approach is employed. To cope with uncertain parameters, stochastic chance-constraint programming is proposed. This paper also proposed three meta-heuristic methods including Multi-Objective Teaching–learning-based optimization (TLBO), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA) to find Pareto solutions. Since heuristic approaches are sensitive to input parameters, the Taguchi approach is suggested to control and tune the parameters. A comparison is performed by using eight assessment metrics to validate the quality of the obtained Pareto frontier by the heuristic methods on the experiment problems. To validate the current model, a set of sensitivity analysis on important parameters and a real case study in the United States are provided. Based on the empirical experimental results, computational time and eight assessment metrics proposed methodology seems to work well for the considered problems. The results show that by raising the transportation costs, the total cost and the environmental impacts of sustainability increased steadily and the trend of the social responsibility of staff rose gradually between − 20 and 0%, but, dropped suddenly from 0 to + 20%. Also in terms of the on-resiliency of the proposed network, the trends climbed slightly and steadily. Applications of this paper can be useful for hospitals, pharmacies, distributors, medicine manufacturers and the Ministry of Health.

Analyzing blockchain adoption barriers in manufacturing supply chains by the neutrosophic analytic hierarchy process

Abstract: 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.

A survey of decision making and optimization under uncertainty

Abstract: Recent advances in decision making have incorporated both risk and ambiguity in decision theory and optimization methods. These methods implement a variety of uncertainty representations from probabilistic and non-probabilistic foundations, including traditional probability theory, sets of probability measures, uncertainty sets, ambiguity sets, possibility theory, evidence theory, fuzzy measures, and imprecise probability. The choice of uncertainty representation impacts the expressiveness and tractability of the decision models. We survey recent approaches for representing uncertainty in both decision making and optimization to clarify the trade-offs among the alternative representations. Robust and distributionally robust optimization are surveyed, with particular attention to standard form ambiguity sets. Applications of uncertainty and decision models are also reviewed, with a focus on recent optimization applications. These applications highlight common practices and potential research gaps. The intersection of behavioral decision making and robust optimization is a promising area for future research and there is also opportunity for further advances in distributionally robust optimization in sequential and multi-agent settings.

Exploring the relationship of ESG score and firm value using cross-lagged panel analyses: case of the Indian energy sector

Abstract: Business integration with the internal and external world is gaining momentum in the light of Environment, Social, and Governance factors (ESG score) linkage to corporate financial performance (CFP). However, the impact of the ESG–CFP relationship varies across economies, industries, and institutional frameworks due to varying legal, social structures and expectations from stakeholders. The present study aims to test the bidirectional causality and autoregression effects between ESG disclosures and the firm value of Indian energy sector companies’ data using a four-wave cross-lagged panel structural equation modeling. Results indicate that the relationship is not bidirectional in the overall and individual elementsof ESG to firm value. We find AR effects to be stable, and there is a negative association found in the first two lags and a positive association found in the last lag. Research findings are beneficial for investors, fund managers, policymakers, and energy company managers. We further provide direction to executives on ESG investment and practices and lag period to reap the benefit of such investments through firm value.

Designing a hybrid reinforcement learning based algorithm with application in prediction of the COVID-19 pandemic in Quebec.

Abstract: World Health Organization (WHO) stated COVID-19 as a pandemic in March 2020. Since then, 26,795,847 cases have been reported worldwide, and 878,963 lost their lives due to the illness by September 3, 2020. Prediction of the COVID-19 pandemic will enable policymakers to optimize the use of healthcare system capacity and resource allocation to minimize the fatality rate. In this research, we design a novel hybrid reinforcement learning-based algorithm capable of solving complex optimization problems. We apply our algorithm to several well-known benchmarks and show that the proposed methodology provides quality solutions for most complex benchmarks. Besides, we show the dominance of the offered method over state-of-the-art methods through several measures. Moreover, to demonstrate the suggested method's efficiency in optimizing real-world problems, we implement our approach to the most recent data from Quebec, Canada, to predict the COVID-19 outbreak. Our algorithm, combined with the most recent mathematical model for COVID-19 pandemic prediction, accurately reflected the future trend of the pandemic with a mean square error of 6.29E-06. Furthermore, we generate several scenarios for deepening our insight into pandemic growth. We determine essential factors and deliver various managerial insights to help policymakers making decisions regarding future social measures.

Detecting bubbles in Bitcoin price dynamics via market exuberance

Abstract: Empirical evidence suggests the presence of bubble effects on Bitcoin price dynamics during its lifetime, starting in 2009. Previous research, mostly empirical, focused on statistical tests in order to detect a bubble behavior at some point in time. Few exceptions suggested specific time series models capable to describe such phenomena. We contribute this stream of literature by considering a continuous time stochastic model for Bitcoin dynamics, depending on a market attention factor, which is proven to boost in a bubble under suitable conditions. Here, we define a bubble following the theory of mathematical bubbles introduced by Philip E. Protter and coauthors. Specifically, we prove that the presence of a bubble is related to the correlation between the market attention factor on Bitcoin and Bitcoin returns being above a threshold, i.e. when marked attention affects Bitcoin prices and converse, creating a vicious loop. This phenomenon has been labelled market exuberance by Robert J. Shiller, recipient of the 2013 Nobel prize in Economic Sciences. The model is fitted on historical data of Bitcoin prices, by considering either the total trading volume or the Google Search Volume Index as proxies for the attention measure. According to our numerical results, a bubble effect is evidenced in the early years of Bitcoin introduction, namely 2012–2013, as well as in the recent race of 2017.