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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]

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

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

The 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]

We describe the formulation and development of a supply-chain optimisation model for Fletcher Challenge Paper Australasia (FCPA). This model, known as Paper Industry Value Optimisation Tool (PIVOT), is a large mixed integer program that finds an optimal allocation of supplier to mill, product to paper machine, and paper machine to customer, while at the same time modelling many of the supply chain details and nuances which are peculiar to FCPA. PIVOT has assisted FCPA in solving a number of strategic and tactical decision problems, and provided significant economic benefits for the company. [ABSTRACT FROM AUTHOR]

The impacts of increased paper recycling on the U.S. pulp and paper sector are investigated, using the North American Pulp And Paper (NAPAP) model. This dynamic spatial equilibrium model forecasts the amount of pulp, paper and paperboard exchanged in a multi-region market, and the corresponding prices. The core of the model is a recursive price-endogenous linear programming system that simulates the behavior of a competitive industry. The model has been used to make forecasts of key variables describing the sector from 1986 to 2012, based on three recycling policy scenarios. Waste reduction policies that succeed in reducing demand for paper would have the greatest impact on the amount of wood used. But the minimum recycled content policies envisaged currently would have no more effect than what will come about due to unregulated market forces. [ABSTRACT FROM AUTHOR]

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

An important production programming problem arises in paper industries coupling multiple machine scheduling with cutting stocks. Concerning machine scheduling: how can the production of the quantity of large rolls of paper of different types be determined. These rolls are cut to meet demand of items. Scheduling that minimizes setups and production costs may produce rolls which may increase waste in the cutting process. On the other hand, the best number of rolls in the point of view of minimizing waste may lead to high setup costs. In this paper, coupled modeling and heuristic methods are proposed. Computational experiments are presented. [ABSTRACT FROM AUTHOR]

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

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

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

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

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

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

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

This paper investigates the problem of obtaining the best arrangement for cutting stock patterns in the presence of defective material in the paper, which is a common problem in the industry. This problem is modeled as a multiple subset sum problem and its structure is exploited in order to obtain a practical solution procedure. The solution methodology employs the subset sum problem as a subprocedure. Computational results are reported. [ABSTRACT FROM AUTHOR]

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

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

The anti-epidemic supply chain plays an important role in the prevention and control of the COVID-19 pandemic. Prior research has focused on studying the facility location, inventory management, and route optimization of the supply chain by using certain parameters and models. Nevertheless, uncertainty, as a vital influence factor, greatly affects the supply chain. As such, the uncertainty that comes with technological innovation has a heightened influence on the supply chain. Few studies have explicitly investigated the influence of technological innovation on the anti-epidemic supply chain under the COVID-19 pandemic. Hence, the current research aims to investigate the influences of the uncertainty caused by technological innovation on the supply chain from demand and supply, shortage penalty, and budget. This paper presents a three-level model of the anti-epidemic supply chain under technological innovation and employs an interval data robust optimization to tackle the uncertainties of the model. The findings are obtained as follows. Firstly, the shortage penalty will increase the costs of the objective function but effectively improve demand satisfaction. Secondly, if the shortage penalty is sufficiently large, the minimum demand satisfaction rate can ensure a fair distribution of materials among the affected areas. Thirdly, technological innovation can reduce costs. The technological innovation related to the transportation costs of the anti-epidemic material distribution center has a greater influence on the optimal value. Meanwhile, the technological innovation related to the transportation costs of the supplier has the least influence. Fourthly, both supply and demand uncertainty can influence costs, but demand uncertainty has a greater influence. Fifthly, the multi-scenario budgeting approach can decrease the calculation complexity. These findings provide theoretical support for anti-epidemic dispatchers to adjust the conservativeness of uncertain parameters under the influence of technological innovation. [ABSTRACT FROM AUTHOR]

Digital Contact Tracing (DCT) has been proved to be an effective tool to counteract the new SARS-CoV-2 or Covid-19. Despite this widespread effort to adopt the DCT, less attention has been paid to the organisation of the health logistics system that should support the tracing activities. Actually, the DCT poses a challenge to the logistics of the local health system in terms of number of daily tests to be collected and evaluated, especially when the spreading of the virus is soaring. In this paper we introduce a new optimisation problem called the Daily Swab Test Collection (DSTC) problem, that is the daily problem of collecting swab tests at home in such a way to guarantee a timely testing to people notified by the app to be in contact with a positive case. The problem is formulated as a variant of the team orienteering problem. The contributions of this paper are the following: (i) the new optimisation problem DSTC that complements and improves the DCT approach proposed by Ferretti et al. (Science https://doi.org/10.1126/science.abb6936, 2020), (ii) the DSCT formulation as a variant of the TOP and a literature review highlighting that this variant can have useful application in healthcare management, (iii) new realistic benchmark instances for the DSTC based on the city of Turin, (iv) two new efficient and effective hybrid algorithms capable to deal with realistic instances, (v) the managerial insights of our approach with a special regard on the fairness of the solutions. The main finding is that it possible to optimise the underlying logistics system in such a way to guarantee a timely testing to people recognised by the DCT. [ABSTRACT FROM AUTHOR]

Industry 4.0 represents the most advanced stage of organization of industrial companies, allowing them to respond to an uncertain and changing environment, particularly as accentuated by the recent crisis resulting from COVID-19. Management innovation (MI) contributes to this process of permanent adaptation. The MI implementation phase is a critical step in MI generation that can generate many potential obstacles. This study focuses on these obstacles while considering the different activities (or subprocesses) embedded in this phase and the different actors involved in this complex process. We conducted a longitudinal case study in real time to investigate the implementation of MI internally generated by a multinational industrial company. Our results show that the obstacles encountered during the MI implementation phase may differ depending on the different activities and actors of this phase, thus leading us to question current implementation frameworks. This paper contributes by refining the theoretical model of MI generation and providing a better understanding of the obstacles that occur during the MI implementation phase. From a managerial perspective, this paper highlights key management principles to overcome the obstacles identified. [ABSTRACT FROM AUTHOR]

In this paper, we utilize the Chicago Board Option Exchange (CBOE) implied volatility indices to estimate the time-frequency information transmission among financial markets from 01.08.2008 to 31.10.2019. In doing so, we utilize the rolling window wavelet correlation (RWWC), Diebold & Yilmaz (The Economic Journal 119: 158–171, 2012), and Barunik & Krehlik (Journal of Financial Econometrics 16: 271–296, 2018). Our empirical findings suggest short-term and long-term dynamic connectedness between implied volatility indices of alternative assets. The long-term analysis findings suggest potential hedging and diversification opportunities that can be exploited by taking offsetting positions across volatility indices. The findings confirm heterogeneity between short-term and long-term connectedness results. Our findings also show superior out of sample hedging effectiveness of GVZ. The implications of the findings are further discussed in the paper. [ABSTRACT FROM AUTHOR]

This paper examines the impact of data-driven innovation (DDI) on firm performance, based on an exploratory case study of a manufacturing firm in China's textile and apparel industry. It explores the influence of various contextual variables on the firm's DDI and suggests ways to enhance DDI and thereby firm performance. Extending the literature on DDI, the paper proposes and validates a theoretical framework that incorporates the influence of various contextual factors on firms' DDI. The findings show that (1) individual context is associated with DDI; (2) organizational context is associated with DDI; and (3) DDI is associated with firm performance. This paper extends our understanding of how firm performance can be improved through DDI and shows that DDI should match a firm's contextual environment. [ABSTRACT FROM AUTHOR]

This paper aims to understand the impact of big data analytics on the retail supply chain. For doing so, we set our context to select the best big data practices amongst the available alternatives based on retail supply chain performance. We have applied TODIM (an acronym in Portuguese for Interactive Multi-criteria Decision Making) for the selection of the best big data analytics tools among the identified nine practices (data science, neural networks, enterprise resource planning, cloud computing, machine learning, data mining, RFID, Blockchain and IoT and Business intelligence) based on seven supply chain performance criteria (supplier integration, customer integration, cost, capacity utilization, flexibility, demand management, and time and value). One of the intriguing understandings from this paper is that most of the retail firms are in a dilemma between customer loyalty and cost while implementing the big data practices in their organization. This study analyses the dominance of the big data practices at the retail supply chain level. This helps the newly emerging retail firms in evaluating the best big data practice based on the importance and dominance of supply chain performance measures. [ABSTRACT FROM AUTHOR]

With the emergence of digital technologies, we have witnessed a rapid growth of the sharing economy in recent years. In fashion industry, sharing economy concept is also widely applied. Instances can be found all over the world, like Rent the Runway, Golden Tote, Stitch Fix, and Le Tote in the US, Air Closet in Japan, Y Closet, Ms Paris, and Dora's Dream in China, etc. In this paper, motivated by the industrial practices of fashion sharing platforms, we first conduct a literature review on business operations of sharing platforms, including the sharing platform operations, digital and information technologies employed in sharing economy, business models, and sustainability and circularity in business operations. Afterwards, we analyze the challenges faced by fashion sharing platforms by exploring the two leading fashion sharing platforms in the US and in China, respectively. At last, we propose future research directions on fashion sharing platform operations and conclude the paper. [ABSTRACT FROM AUTHOR]

The coronavirus first appeared in China in 2019, and the World Health Organization (WHO) named it COVID-19. Then WHO announced this illness as a worldwide pandemic in March 2020. The number of cases, infections, and fatalities varied considerably worldwide. Because the main characteristic of COVID-19 is its rapid spread, doctors and specialists generally use PCR tests to detect the COVID-19 virus. As an alternative to PCR, X-ray images can help diagnose illness using artificial intelligence (AI). In medicine, AI is commonly employed. Convolutional neural networks (CNN) and deep learning models make it simple to extract information from images. Several options exist when creating a deep CNN. The possibilities include network depth, layer count, layer type, and parameters. In this paper, a novel Xception-based neural network is discovered using the genetic algorithm (GA). GA finds better alternative networks and parameters during iterations. The best network discovered with GA is tested on a COVID-19 X-ray image dataset. The results are compared with other networks and the results of papers in the literature. The novel network of this paper gives more successful results. The accuracy results are 0.996, 0.989, and 0.924 for two-class, three-class, and four-class datasets, respectively. [ABSTRACT FROM AUTHOR]

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

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

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

The preface introduces this special issue and describes in brief a number of OR applications in different scientific fields such as environment, forestry, engineering, finance and economics. The findings of this special issue verify the applicability and usefulness of OR different methodologies as a means of acquiring solutions for confronting ICT challenges in the contemporary digital world. [ABSTRACT FROM AUTHOR]

Industrial relocation (IR) is a business strategy consisting of moving operations locations. The purpose of this paper is to present how to assess, with multi-attribute decision-making (MADM), alternatives for IR. With MADM, IR strategies can be assessed not only based on a single attribute, as costs, or profits. This paper presents the application of MADM in a real case of IR. Four leading methods of MADM were applied: analytic hierarchy process (AHP), multi-attribute utility theory (MAUT), multi-attribute value theory (MAVT), and technique of order preference by similarity to ideal solution (TOPSIS). Results of AHP, MAUT, MAVT, and TOPSIS were quite similar, indicating the decision for the company not to relocate. A joint comparison of results with compatibility indices and correlation coefficients is the major novelty presented by this paper to the field of Operations Research, known as MADM. [ABSTRACT FROM AUTHOR]

I Seddigh et al. i in their work I "Approaching towards sustainable supply chain under the spotlight of business intelligence" i develop a theoretical model to explain how business intelligence capabilities influence the company's supply chain sustainability. They apply SmartPLS 3.0, which uses partial least squares structural equation modelling (PLS-SEM), to test the correlation between supply chain risks and supply chain performance, investigate the mediating effect of supply chain risk management on the correlation between supply chain risks and supply chain performance. The COVID-19 pandemic has created a lot of disturbances in supply chains around the globe leading to disturbances in the availability of logistics infrastructure (e.g., ports closure, boarders' control, etc.) and workforce absence. They propose a novel robust-heuristic optimization approach which support the supply chain managers in decision-making in selecting of carbon emission policies in supply chains with different vehicle types, demand and economic uncertainty in large scale-problems. [Extracted from the article]

The production and manufacturing sector is one of the primary factors that affects the environment, which has been a very important topic of recent studies. Many approaches are employed to reduce the impact of production on the environment. More recently, carbon-abatement technology and activities have been introduced into the production processes to reduce carbon emissions, such as the implementation of emission trading programs in many industrial sectors, including the paper and pulp sector. Nevertheless, the costs of abatement activities will result in a certain level of sacrifice in productivity growth, when the inputs are reallocated from good output production to abatement activities to maintain bad output under the regulatory limit. However, how and the extent to which such technology will affect productivity remain unclear. Therefore, it is worth investigating the opportunity cost of introducing such technology. In this paper, we offer new empirical evidence by studying panel data on 17 EU member states from 1995 to 2006. Productivity changes are calculated using a data envelopment directional distance function with and without adapting the carbon-abatement technology in the paper and pulp production. The results support our concern about the potential opportunity cost of introducing carbon-abatement technology, which leads to a decline in productivity growth. In addition, industrial production is not operating efficiently; on average it moves further away from the efficient production frontier over time. [ABSTRACT FROM AUTHOR]

In the recent strategic queueing literature, there is a large number of papers that study the join-or-balk dilemma in queueing systems with server's on-off periods, modeling vacations and failures. These studies consider the customers as discrete units and adopt the assumption that reneging is not permitted. In the present paper, we depart from this framework and study the effect of the reneging option in such systems. We consider the fluid on-off model of the basic queue with vacations/failures and study reneging vs. no-reneging when customers are strategic. We derive the equilibrium customer strategies and the corresponding performance measures of the system, and we use them to study the equilibrium throughput and social welfare. The main finding is that the existence of the reneging option is very beneficial for overloaded systems, i.e., for such systems balking alone is not sufficient to achieve good outcomes. On the contrary, for underloaded systems the reneging option is not particularly valuable. [ABSTRACT FROM AUTHOR]

With the rapid growth of the global digital economy, supply chain finance has entered the stage of platform development in view of the history, policy environment, market status and other factors. Supply chain finance relies on multiple supply chain stakeholders to carry out financial business, which needs to solve a variety of financial risk control and pricing issues. The financing model in supply chain finance can be differentiate from the traditional credit model. The service mode and leading mode of supply chain finance need to be adjusted in accordance with the changes of the industrial operation. Further, the business mode of supply chain finance shows a diversification trend, which includes traditional supply chain financial models such as accounts receivable, advance payment, inventory financing, and supply chain credit financing models. In this paper, we investigate the similarities between supply chain finance and options, and further introduce American call options to supply chain financial products under the mode of small and medium-sized enterprises' (SMEs) accounts receivable financing. The price of supply chain financial products is derived through the trinomial tree option pricing model, which determines the corporate financing interest rates. The rationality of the proposed pricing model is validated in comparison with the medium and long-term load bank interest rates. The contribution of the paper is in providing SMEs with supply chain financial products under the accounts receivable model to resolve financing difficulties and the pricing the products through the trinomial budget pricing model. [ABSTRACT FROM AUTHOR]

Supply Chain Finance (SCF), an important way of integrating industry and finance that has emerged in recent decades, has attracted the interest of both industry and academia. A large number of conceptual studies on SCF support the theory that SCF benefits supply chains by alleviating financing problems and maintaining stability. The focal firms which are the main SCF practices provider help alleviating the financing problem of their partners by weakening part of the working capital management. However, SCF-oriented practice on the focal firm and traditional corporate finance theory which earing profit by improving working capital management are not aligned. This paper attempts to provide empirical evidence to explain this phenomenon on the SCF-oriented perspective. Secondary panel data analysis is employed. The impact of focal firms providing SCF services is used to represent SCF practices in the supply chain since focal firms dominate SCF activities. Cash Conversion Cycle (time perspective), trade credit and prepayment (volume perspective) of the focal firms are independent variables of the analysis. Three type of performances, financial, risk and operations, are dependent variables of the analysis. This study found that a focal firm providing SCF can conditionally improve their firm's financial performance, risk levels and operations management. Results from different industrial analyses suggest that tertiary industries are best suited to implementing SCF activities. Providing SCF also benefits the firm-level performance of state-owned enterprises. This is the first paper that comprehensively analyses the effects of SCF implementation with different firm characteristics from both a time perspective and a volume perspective. [ABSTRACT FROM AUTHOR]

This paper explores the mispricing relative to the capital asset pricing model through an equilibrium model. We find that both the strong risk preference dependent on good market states and strong risk aversion dependent on bad market states can produce high mispricing. Choosing the China stock market, the largest emerging market dominated by individual investors and known for its volatile nature in a short history as our sample, the empirical results also support our theoretical findings. Overall, our paper sheds light on the mispricing caused by the investor's risk preference reference-dependent on market states. [ABSTRACT FROM AUTHOR]

Presents the preface to the September 2000 issue of "Annals of Operations Research."

Police patrol is an effective crime prevention tool and boosts public confidence in urban security. Many interesting decision making problems appear in route design, resource allocation and jurisdiction planning. Many cities across the world have adopted a structured and intelligent method of police patrol due to the presence of a variety of operational and resource constraints. In this paper, we present a comprehensive review of the state-of-the-art in this domain, especially from the practice of operations research (OR) point of view. This is the first-of-its-kind review on police patrol presenting a classification scheme based on the type of problem, objective and modelling approach. In this novel scheme, one can track any paper almost readily to find the specific contribution. The applicability of OR in this domain is set to grow significantly as the governments formulate policies related to smart city planning and urban security. This study reveals many practical challenges in police patrolling for future research. [ABSTRACT FROM AUTHOR]

Since at the two conferences many presentations addressed a variety of different multiobjective optimisation problems, we decided to focus this special issue distinctively on recent developments in multiobjective optimisation falling within the a posteriori paradigm of multiple criteria decision making (MCDM). We observe that multiobjective optimisation under uncertainty and multiobjective optimisation in practical applications are the dominating research themes throughout this volume because a majority of studies address either one or both of them simultaneously. The type of optimisation problems addressed ranges from scheduling problems with two objectives to mixed integer linear optimisation problems and nonlinear optimization problems, both with only continuous and with continuous as well as binary variables. [Extracted from the article]

The smart supply chain innovation (SSCI) has become the key way for enterprises to enhance their competitiveness. Therefore, it is very important for the current supply chain enterprises to choose a reasonable innovation path. Through the literature review method, this paper summarizes the four main evaluation indicators of path selection for smart supply chain innovation, which are technical indicators, organizational environment indicators, operational efficiency indicators, risk prevention and control indicators. According to the characteristics of evaluation index, the improved Fuzzy Entropy-Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method is proposed. This method considers the situation that the original data contains both interval value and fixed value data. Firstly, the standardized decision matrix is constructed, and then the optimal index weight is established by Fuzzy Entropy method. Secondly, this paper calculates the weighted decision matrix by using the method of sub item weighting of hierarchical indicators. Finally, the improved TOPSIS method is used to determine the relative closeness of each scheme. According to the weighted decision matrix, the innovation path index of the smart supply chain (SSC) is further calculated. We use the actual case of a company to analyze the evaluation method for three different SSCI paths. This paper provides a reference for the path selection of SSCI from the aspects of theory and practice. [ABSTRACT FROM AUTHOR]

Organisations need to develop long-term strategies to ensure they incorporate innovation for environmental sustainability (IES) to remain competitive in the market. This can be challenging given the high level of uncertainty regarding the future (e.g., following the COVID pandemic). Supplier selection is an important decision that organisations make and can be designed to support IES. While the literature provides various criteria in the field of IES strategies, it does not identify the criteria which can be utilised to assist organisations in their supplier selection decisions. Moreover, the literature in this field does not consider uncertainty related to the occurrence of possible future events which may influence the importance of these criteria. To address this gap, this paper develops a novel criteria decision framework to assist supplier evaluation in organisations, taking into consideration different events that may occur in the future. The framework that combines three decision-making methods: the stratified multi-criteria decision-making method, best worst method, and technique for order of preference by similarity to ideal solution. The framework, proposed in this paper, can also be adopted to enable effective and sustainable decision making under uncertainty in various fields. [ABSTRACT FROM AUTHOR]

Transportation is a broad multidisciplinary science concerned with the study of transportation network design, vehicle application engineering, as well as transportation operations and management. Due to the breadth of the topic and the increasing complexity of the decision processes involved in managing transportation operations, transportation has become a fertile field of study for operations research. Inspired by this, we have compiled this special issue of the Annals of Operations Research comprising of 24 new research contributions and two systematic literature reviews supporting and categorizing the current state of transportation research. [ABSTRACT FROM AUTHOR]

Online platforms provide sellers' products on their websites and earn a commission fee for each unit sold. Recently, many platforms have tried to refer customers to their direct competitors. In this paper, we explain this counter-intuitive practice by developing a game-theoretic model where two competing platforms contracting with one common seller or two competing sellers. We first analyze a benchmark case where platforms and sellers are integrated, finding that competitor referral will aggregate competition and thus neither platform is willing to refer its competitor voluntarily. However, when each platform serves as a marketplace for an independent seller, it is possible that a platform voluntarily refers its competitor. The rationale is that there exists a double marginalization problem when platforms set commission fees and sellers set prices, resulting in low efficiency of product selling. By referring visitors to their competitors, platforms can introduce cannibalization to cope with the double marginalization problem. We also investigate the case when the two platforms serve one common seller, finding that as long as the seller does not charge discriminated prices for the same product, the platforms may also apply referral. This is because the benefits of the two platforms are more aligned when the common seller sets a common price. Thus, online referral which helps the competitor may also help the referring platform itself. This paper also makes serval extensions to check the robustness of the model. [ABSTRACT FROM AUTHOR]

This paper investigates the effects of reselling and agency contracts on the platform's incentive to share information under two scenarios, i.e., the recommendation scenario and non-recommendation scenario. Through analyzing the equilibrium solutions, we derive the following results. First, comparing the recommendation and non-recommendation scenarios, the platform prefers the recommendation scenario under the reselling contract. However, contrary to traditional wisdom, when the recommendation efficiency is relatively more efficient, the platform chooses the non-recommendation scenario in the agency contract; as the recommendation efficiency becomes less efficient, the recommendation scenario is preferred in the agency contract. Second, for the reselling contract, the platform prefers to withhold demand information in the recommendation and non-recommendation scenarios. However, the platform voluntarily shares this information under the agency contract for the two scenarios. Third, the platform's optimal strategy depends on the commission rate, the platform's recommendation efficiency, and the level of information accuracy. Intuitively, a lower commission rate induces the platform to prefer the reselling contract in the recommendation and non-recommendation scenarios. However, a higher commission rate does not always make the platform choose the agency contract. The recommendation efficiency and the level of information accuracy affect the platform's optimal strategy. Furthermore, given the platform's optimal strategy, there are Pareto optimal regions that make the manufacturer better off. That is, the platform and the manufacturer achieve a win–win situation. Finally, in an extension of the model, this paper shows the platform's optimal strategy when a hybrid contract is used, which also demonstrates the robustness of the results. [ABSTRACT FROM AUTHOR]

In this paper, we are concerned with the optimal order quantities for a risk-averse newsvendor with backlogging, where it is assumed that all or part of the unsatisfied demands can be fullfilled by backordering. The optimal order quantities are derived under the famous conditional value-at-risk (CVaR) criterion and mean-CVaR criterion on controlling the profit loss due to the uncertainty of market demands. With the optimal order quantities to the proposed models, several important monotone properties are obtained and their relationships with the existing results are discussed. Moreover, it is shown in this paper that low risk means low expected profit while high expected profit comes with high risk. Finally, we present some managerial insights for the risk-averse newsvendor model under backlogging. [ABSTRACT FROM AUTHOR]

In this paper, we address the controversies of epidemic control planning by developing a novel Simulation-Deep Reinforcement Learning (SiRL) model. COVID-19 reminded constituents over the world that government decision-making could change their lives. During the COVID-19 pandemic, governments were concerned with reducing fatalities as the virus spread but at the same time also maintaining a flowing economy. In this paper, we address epidemic decision-making regarding the interventions necessary given of the epidemic based on the purpose of the decision-maker. Further, we intend to compare different vaccination strategies, such as age-based and random vaccination, to shine a light on who should get priority in the vaccination process. To address these issues, we propose a simulation-deep reinforcement learning (DRL) framework. This framework is composed of an agent-based simulation model and a governor DRL agent that can enforce interventions in the agent-based simulation environment. Computational results show that our DRL agent can learn effective strategies and suggest optimal actions given a specific epidemic situation based on a multi-objective reward structure. We compare our DRL agent's decisions to government interventions at different periods of time during the COVID-19 pandemic. Our results suggest that more could have been done to control the epidemic. In addition, if a random vaccination strategy that allows super-spreaders to get vaccinated early were used, infections would have been reduced by 32% at the expense of 4% more deaths. We also show that a behavioral change of fully quarantining 10% of the risky individuals and using a random vaccination strategy leads to a reduction of the death toll by 14% and 27% compared to the age-based vaccination strategy that was implemented and the New Jersey reported data, respectively. We have also demonstrated the flexibility of our approach to be applied to other locations by validating and applying our model to the COVID-19 case in the state of Kansas. [ABSTRACT FROM AUTHOR]

In this paper, a new responsive-green-cold vaccine supply chain network during the COVID-19 pandemic is developed for the first time. According to the proposed network, a new multi-objective, multi-period, multi-echelon mathematical model for the distribution-allocation-location problem is designed. Another important novelty in this paper is that it considers an Internet-of-Things application in the COVID-19 condition in the suggested model to enhance the accuracy, speed, and justice of vaccine injection with existing priorities. Waste management, environmental effects, coverage demand, and delivery time of COVID-19 vaccine simultaneously are therefore considered for the first time. The LP-metric method and meta-heuristic algorithms called Gray Wolf Optimization (GWO), and Variable Neighborhood Search (VNS) algorithms are then used to solve the developed model. The other significant contribution, based on two presented meta-heuristic algorithms, is a new heuristic method called modified GWO (MGWO), and is developed for the first time to solve the model. Therefore, a set of test problems in different sizes is provided. Hence, to evaluate the proposed algorithms, assessment metrics including (1) percentage of domination, (2) the number of Pareto solutions, (3) data envelopment analysis, and (4) diversification metrics and the performance of the convergence are considered. Moreover, the Taguchi method is used to tune the algorithm's parameters. Accordingly, to illustrate the efficiency of the model developed, a real case study in Iran is suggested. Finally, the results of this research show MGO offers higher quality and better performance than other proposed algorithms based on assessment metrics, computational time, and convergence. [ABSTRACT FROM AUTHOR]