Search

As a system reliability analysis technology, failure mode and effect analysis (FMEA) has been widely used in various industries to reduce the failure risk of products, systems, and services. In recent years, many researchers have found that this technology has limitations in failure mode assessment and failure mode prioritization in complex uncertain environment. In order to solve this problem, this article proposes a new FMEA model, which combines hesitant Pythagorean fuzzy sets (HPFSs) and ORESTE method. Specifically, HPFSs are used to describe the risk assessment information of experts. The ORESTE method is extended to determine the risk priority of the failure modes in FMEA. Moreover, the PIR structure of ORESTE is improved to reduce the subjective influence of experts. Finally, an example of risk assessment of intelligent aquaculture system is given to verify the applicability and effectiveness of the proposed FMEA model. The results show that the proposed FMEA model can not only accurately describe the hesitation and uncertainty of experts’ risk assessment information, but also obtain more suitable risk ranking of failure modes, based on which managers can further take corresponding preventive measures.

Robust scientific knowledge is contingent upon replication of original findings. However, replicating researchers are constrained by resources, and will almost always have to choose one replication effort to focus on from a set of potential candidates. To select a candidate efficiently in these cases, we need methods for deciding which out of all candidates considered would be the most useful to replicate, given some overall goal researchers wish to achieve. In this article we assume that the overall goal researchers wish to achieve is to maximize the utility gained by conducting the replication study. We then propose a general rule for study selection in replication research based on the replication value of the set of claims considered for replication. The replication value of a claim is defined as the maximum expected utility we could gain by conducting a replication of the claim, and is a function of (a) the value of being certain about the claim, and (b) uncertainty about the claim based on current evidence. We formalize this definition in terms of a causal decision model, utilizing concepts from decision theory and causal graph modeling. We discuss the validity of using replication value as a measure of expected utility gain, and we suggest approaches for deriving quantitative estimates of replication value. Our goal in this article is not to define concrete guidelines for study selection, but to provide the necessary theoretical foundations on which such concrete guidelines could be built.Translational Abstract Replication-redoing a study using the same procedures-is an important part of checking the robustness of claims in the psychological literature. The practice of replicating original studies has been woefully devalued for many years, but this is now changing. Recent calls for improving the quality of research in psychology has generated a surge of interest in funding, conducting, and publishing replication studies. Because many studies have never been replicated, and researchers have limited time and money to perform replication studies, researchers must decide which studies are the most important to replicate. This way scientists learn the most, given limited resources. In this article, we lay out what it means to think about what is the most important thing to replicate, and we propose a general decision rule for picking a study to replicate. That rule depends on a concept we call replication value. Replication value is a function of the importance of the study, and how uncertain we are about the findings. In this article we explain how researchers can think precisely about the value of replication studies. We then discuss when and how it makes sense to use replication value as a measure of how valuable a replication study would be, and we discuss factors that funders, journals, or scientists could consider when determining how valuable a replication study is.

Digital transformation provides an unprecedented opportunity for the exploration, integration, and utilization of scientific and technological resources (STR). In the wave of digital transformation, countries around the world have set up their own STR sharing platforms, which can match a variety of STR services and needs in a lower cost and more convenient way, and create interaction between the suppliers and demanders. These sharing platforms provide possibilities to realize the sharing economy in the field of scientific and technological innovation. However, it is also necessary to design the corresponding mechanisms to improve the sharing rate and sharing value. Therefore, in this article, we construct a new framework of operation mechanism for China's STR allocation system and design its three submechanisms. Then, a system dynamics model of the operation mechanism is designed, and the experiments are carried out. The simulation results show that the mechanism proposed in this article can effectively improve the sharing rate and sharing value. Finally, some policy suggestions are put forward, which will help the government to improve management mode and optimize resource allocation.

Problem definition: Fulfillment flexibility, the ability of distribution centers (DCs) to fulfill demand originating from other DCs, can help e-retailers reduce lost sales and improve service quality. Because the cost of full flexibility is prohibitive, we seek to understand the value of partially flexible fulfillment networks under simple and effective fulfillment policies. Academic/practical relevance: We propose a general method for understanding the practical value of (partial) fulfillment flexibility using a data-driven model, theoretical analysis, and numerical simulations. Our method applies to settings with local fulfillment (i.e., order fulfillment from the originating DC) prioritization and possible customer abandonment, two features that are new to the fulfillment literature. We then apply this method for a large e-retailer. We also introduce a new class of spillover limit fulfillment policies with attractive theoretical and practical features. Methodology: Our analysis uses dynamic and stochastic optimization, applied probability, and numerical simulations. Results: We derive optimal fulfillment policies in stylized settings, as well as bounds on the performance under an optimal policy using theoretical analysis, to provide guidelines on which policies to test in numerical simulations. We then use simulations to estimate for our industrial partner that a proposed fulfillment network with additional flexibility equates to a profit improvement on the order of tens of millions of U.S. dollars. Managerial implications: We provide an approach for e-retailers to understand when fulfillment flexibility is most valuable. We find that fulfillment flexibility provides the most benefit for our collaborator when gross profits are high relative to fulfillment costs or centrally held inventory is low. Also, we identify the risks of myopic fulfillment with additional flexibility and demonstrate that an effective spillover limit policy mitigates these risks. History: This paper has been accepted as part of the 2019 Manufacturing & Service Operations Management Practice-Based Research Competition. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2021.0981 .

This study aimed to develop and evaluate the performance of a service system platform based on the Internet of Things (IoT) for monitoring nutritional deficiencies in plants and providing fertilizer recommendations. There are two distinct differences between this work and previous ones; namely, this service system platform has been developed based on IoT using a system engineering approach and its performance has been evaluated using dependability. We have successfully developed and integrated a service system platform and chlorophyll meter that is based on IoT. We have also successfully tested the performance of the service system platform using the JMeter software. The dependability value measured from the five tested variables (reliability, availability, integrity, maintainability, and safety) showed a value of 0.97 which represents a very good level of system confidence in not failing to deliver services to users under normal operational conditions. From a future perspective, this platform can be used as an alternative service to monitor nutrient deficiencies in plants and provide fertilization recommendations to increase yields, reduce fertilizer costs, and prevent the use of excessive fertilizers, which can cause environmental pollution.

Drawing on insights from organizational theory, in this article, we examine the paradoxical tensions that reside between quality and safety and impact construction organizations and their projects. Rather than adopting the dualistic, reductionist, and ineffective “either/or” framing of quality and safety, we suggest confronting them as a duality. The corollary is balancing the competing demands of quality and safety, thus putting managers in a position to optimally reduce rework and improve project safety at the same time. Managers also need to focus on balancing the paradoxical tensions of error prevention and error management that can influence a project's performance. However, the offsetting of these tensions has been overlooked in practice. In this article, we proffer that if construction organizations are to make headway to improve quality and safety simultaneously, then managers should espouse ambidexterity, move from the “tyranny” of quality or safety to “genius” of quality and safety and, thus, synergize these two competing demands while managing the paradoxical tensions of error prevention and error management.

Supply-chains (SC) present performance bottlenecks that contribute to a high level of costs, infiltration of product quality, and impact productivity. Examples of such inhibitors include the bullwhip effect, new product lines, high inventory, and restrictive data flows. These bottlenecks can force manufacturers to source more raw materials and increase production significantly. Also, restrictive data flow in a complex global SC network generally slows down the movement of goods and services. The use of Distributed Ledger Technologies (DLT) in supply chain management (SCM) demonstrates the potentials to to reduce these bottlenecks through transparency, decentralization, and optimizations in data management. These technologies promise to enhance the trustworthiness of entities within the supply chain, ensure the accuracy of data-driven operations, and enable existing SCM processes to migrate from a linear to a fully circular economy. This paper presents a comprehensive review of 111 articles published in the public domain in the use and efficacy of DLT in SC. It acts as a roadmap for current and future researchers who focus on SC Security Management to better understand the integration of digital technologies such as DLT. We clustered these articles using standard descriptors linked to trustworthiness, namely, immutability, transparency, traceability, and integrity.

Due to dense social contacts, metropolises are considered as epicenters of contagious infectious diseases. Developing models for infection transmission and mitigation in metropolises is a challenging problem because of spatiotemporal variations in their population structures. Employing the fact that intra-urban mobility is mainly shaped by the transportation infrastructure and reflected in the traffic flux information, a novel multi-scale reaction-diffusion process is developed to model infection transmission in a metropolis. To mitigate the infection, the impact of three non-pharmaceutical preventive interventions (movement restrictions, social distancing, and proactive testing/screening) is analytically investigated on the reaction-diffusion process. We show that imposing movement restrictions between sub-urban areas is only beneficial when they have different reproduction numbers. The social distancing reduces the infection peak in sub-urban areas, but its impact on the entire metropolis is various: The social distancing reduces a metropolis's infection peak if its implementation disperses the reproduction number of sub-urban areas. We show that allocation schemes of proactive testing/screening teams to sub-urban areas have intractable impacts on the infection peak. This demonstrates the necessity of employing optimization techniques to design the best allocation scheme. Finally, three mathematical models and their solution approaches are developed to optimize the implementation of the interventions with the least economic burden. The performance of the optimization models is compared with two simpler implementation schemes (uniform and pro rata implementations of interventions) using the information of Sioux Falls metropolitan area in the U.S.

Post COVID-19 vaccine development, nations are now getting ready to face another challenge: how to effectively distribute vaccines amongst the masses to quickly achieve herd immunity against the infection. According to some experts, herd immunity for COVID-19 can be achieved by inoculating 67% of the population. India may find it difficult to achieve this service level target, owing to several infrastructural deficiencies in its vaccine supply chain. Effect of these deficiencies is to cause frequent lead time disruptions. In this context, we develop a novel modelling approach to identify few nodes, which require additional inventory allocations (strategic inventory reserves) to ensure minimum service level (67%) under the possibility of lead time disruptions. Later, through an illustrative case study on distribution of Japanese Encephalitis vaccine, we identify conditions under which strategic inventory reserve policy cannot be practically implemented to meet service level targets. Nodes fulfilling these conditions are termed as critical nodes and must be overhauled structurally to make the implementation of strategic inventory policy practically viable again. Structural overhauling may entail installation of better cold storage facilities, purchasing more quality transport vans, improving reliability of transport network, and skills of cold storage manager by training. Ideally, conditions for identifying critical nodes for COVID-19 vaccine distribution must be derived separately by substituting COVID-19 specific parametric values in our model. In the absence of the required data for COVID-19 scenario, JE specific criteria can be used heuristically to identify critical nodes and structurally overhaul them later for efficiently achieving service level targets.

Cyber risk affects all organizations. Cyber risk management has generally been based on the heuristics and the availability of protective tools, such as firewalls and passwords. Only recently have there been quantitative analyses of these tools’ costs and benefits. This article presents a probabilistic method based on the existing data in an organization and on their extension to assess the probabilities of new attack scenarios. The objective is to set priorities among risk management measures and to optimize the allocation of limited resources. The model is illustrated first by a statistical analysis of 60 000 incidents, such as lost or stolen laptops, over six years in a specific organization. This analysis is then expanded to the probabilistic domain to cover threats that have not occurred yet. This requires a systematic construction of new attack scenarios and an assessment both of their probability of success and of subsequent losses. The conjunction of statistics and probabilities of more extreme scenarios yields full risk curves. These curves represent the overall cyber risk for the organization and its insurers and unable assessment of the benefits of a spectrum of protective options.

Waste electrical and electronics equipment (WEEE) recyclers have become a boon for countries as they assist to reduce carbon emissions by recycling the WEEE in the most ecofriendly way. The evaluation and choice of optimal WEEE recycling partner is a significant and multifaceted decision for the managerial experts because of the involvement of several qualitative and quantitative criteria. Thus, the aim of the article is to propose a novel methodology by integrating a combined compromise solution approach and similarity measure within the context of single-valued neutrosophic sets (SVNSs) and, then, used to solve the decision-making problem. In this approach, the criteria weights are evaluated by a new procedure based on a similarity measure. For this, a novel similarity measure is introduced for SVNSs and also presented its efficiency over existing similarity measures. To investigate the efficiency and practicality of the introduced approach, a case study of WEEE recycling partner selection is taken under the SVNSs environment. Finally, comparison and sensitivity analysis are made to check the robustness and stability of the developed methodology. The outcomes of this article exemplify that the developed approach is more suitable and well consistent with existing approaches.

As a contemporary product of Internet of Things, wearable devices that are closely related to consumer health have received widespread attention from various nations and industries. Thus, the use of wearable devices has become a significant scientific problem requiring an urgent solution. In this article, we consider the characteristics of wearable devices, consumers, the market, safety, and the environment to analyze consumer behavior. A multiple attribute decision-making (MADM) model is used in this article, including the decision-making trial and evaluation laboratory (DEMATEL) technique, the DEMATEL-based analytic network process (DANP) method, and the modified Vlse Kriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method. Based on expert data and the index system of criteria for consumer adoption of wearable devices, we evaluate the relationship between criteria and their weight of impact to create an influential network relationship map by using the DEMATEL technique, comprehensively analyze the correlation between consumer behaviors and products, clearly identify the relative gaps between criteria and the optimal level, and recommend improvements through the DANP. Finally, we use three specific products as examples to analyze consumer satisfaction for different criteria based on the modified VIKOR method. The results reveal that there are gaps between product performance and the optimal level, and product promotion strategies are proposed that would narrow the performance gaps of these three wearable devices by prioritizing the factors that required improvement.

In this article, we explore the topic of supply chain resilience (SCR) in an open-system context. Systems theory and cybernetics are important methodologies in the analysis of complex networks. However, relatively little is known about their application in the supply chain (SC) operations. Using systems theory and cybernetics for SCR conceptualization, we argue that a systems-theoretical lens can help to extend the state-of-the-art research to describe SCR based on the concept of balancing vulnerability and recoverability over time. Toward this end, we define and test a new resilience measure for SCR using the Bayesian network approach with a compounding function of vulnerability and recoverability. We further propose managerial actions for implementing an open-system view of SCR. To demonstrate the applicability and usefulness of our method, we measure and analyze the resilience levels of a manufacturer. Our results provide useful decision-making support and can be valuable for SC managers seeking to balance risk mitigation and recovery capabilities. The framework and measure developed can also be used to guide firms seeking to analyze the impact of disruptions on SCs through the consideration of both performance degradation and recovery.

The article of record as published may be found at https://doi.org/10.1109/TEM.2019.2958785 In the early stages of the systems engineering process, an important focus is to create an understanding of the stakeholder needs. This is primarily done to prepare the system specification that forms the basis for the system’s design. By extension, example steps in this process include surveying stakeholders to better capture their intent, deriving and documenting requirements, and then using those requirements for subsequent activities, such as developing a functional baseline and candidate design alternatives. During this process, it is important to consider the full system lifecycle. As such, one major objective of a systems engineer is to translate the stakeholder’s needs into functional and nonfunctional requirements (NFRs). Despite this important role, early system designs are often faulty because important NFRs are poorly prioritized or not prioritized at all. While the prioritization of all requirements can be useful, this work focuses specifically on NFRs. It has been identified that the inability to identify the most useful NFRs can lead to system failure. Furthermore, the lack of NFR prioritization is considered one of the most expensive and difficult errors to correct, as well as one of the ten most significant risks in engineering. Systems need more emphasis on the relationships between the system’s elements, rather than on the individual elements or the whole system. Relationships among elements in a system can illustrate more than just the behavior of each element. The illustration can include the purpose for the system and the implications of changing how the NFRs associated with those elements are prioritized. This emphasis requires quantifiable tools and rigor to inform the decision makers. This research’s objective is to contribute to quantifiable decision-making methods and prioritization of NFRs in three ways: the development of a process to determine unique random index; the use of a continuous ranking scale; and the development of a universal decision-making heuristic to accompany prioritization of NFRs.

Military technologies are typically the product of long-term development efforts. These technologies are often adapted late in the development process for use by other users, including other members of the defense enterprise, foreign militaries, or the commercial sector. The need to adapt a technology can arise from cost over-runs or changing operational requirements. This article describes a value-based methodology for developing a transition plan early in the development process, when the system is still in the conceptual phase. Performing this analysis early in the design phase allows for design choices that will support adapting the system for other users. Moreover, it provides a risk mitigation strategy against cost over-run and changes in operational requirements. The process begins by capturing the functional architecture of the technology and analyzing them against a set of tasks associated with different military positions. From there, the adapted technology is evaluated for its projected adequacy based on the value added and the effort required for adaptation. The end-state of this analysis is a value-based model that identifies a portfolio of possible alternative markets and/or uses, which can be comparatively analyzed. A case study is presented for adapting a military augmented reality system for a different market.

Engineering managers work with system designers, system operators, and stakeholders who value the system's performance and seek to deliver stakeholder value. Engineering resilient systems is an emerging research field since all systems face future uncertainties including new missions, new requirements, unplanned environments, and unplanned events that cause disruptions during operation. The ability of a system to provide stakeholder value depends on the system design and the ability of the system operator to react to the disruption(s). A resilient design offers options to respond to new missions, environments, and disruptions through system automation or operator actions. The engineering resilience literature uses overlapping definitions. It does not adequately distinguish between resilient design principles and the means they provide for operators to respond to mission disruptions and the ability to modify the system for new missions. This article uses the engineered systems resilience literature to provide a clearer lexicon. It then proposes a framework, the principles-means-ends diagram, for engineering managers to communicate with stakeholders, operators, and designers about the benefits of engineered systems’ resilience, the opportunities for designers to use resilience design principles to create more resilient designs, and the roles of the operators in achieving resilience during operations. We propose a principles-means-ends diagram for resilient engineered systems, which provides a holistic view for designers, operators, and stakeholders to visualize how their actions connect to others and encourages communication between them to create a more resilient engineered system.

South Korea established the National Construction Standards Center to efficiently manage the national construction standards and orchestrate various efforts such as the development and export of the unified code system, and the conduction of code reform research for securing safety and ever increasing disaster prevention of buildings and infrastructure. Significant national budgets must be put in place to develop, promote, and continually improve the unified construction code system. In order to confirm the appropriateness of this continuous investment, this article intends to estimate the socioeconomic ripple effect of the coordinated efforts over the next 30 years by developing a hybrid method that combines an analytical hierarchical process and system dynamics (SD). In this article, the interacting behavior of the developed SD model is illustrated parametrically. We found that the center's integrative codification efforts including the development and diffusion of the unified code system has the effect of offsetting the decreasing national construction budget. We also found that the Monte Carlo multivariate simulation-based uncertainty analysis on the developed SD model is well-suited for effectively quantifying and integrating various benefits to empower decision makers; in particular, providing information both on the mean and the most conservative socioeconomic ripple effects over the long term.

Maintenance of military equipment plays a vital role in achieving higher war readiness. The vast number of military equipment and their deployment on distinct locations make their maintenance a challenging task. Decision-makers are always interested in knowing the latest status of equipment, develop effective maintenance strategies, and predict mission reliability as accurately as possible. To suffice this need, maintenance data of military equipment must be maintained with high granularity and accuracy, including maintenance information about every lowest maintainable unit of every military equipment. There is a profound need for a comprehensive maintenance management framework, which addresses several military-specific issues with equipment maintenance and stores the maintenance data in the utmost secure environment. In this article, we attempt to provide a novel blockchain enabled framework to make military maintenance management comprehensive and future ready. This article first provides a representation of the maintenance scenario of military equipment and later discusses the several choices made available within the blockchain technology, along with the architecture of the proposed blockchain. The proposed framework uses smart contracts to make monitoring and validation more uncompromising and with minimum human intervention. Some pragmatic analytics are presented in this article, along with the inherent blockchain advantages.

The occurrence of a sudden, calamitous, and unfortunate event requires a quick response that can reduce damage and save lives, fulfilling the basic humanitarian needs of the affected population. National or international organizations and agencies may be initiated to provide assistance but local agencies need to act as soon as possible, because they are familiar with the geographical location as well as the local conditions of the affected areas. Effective coordination of disaster assistance is often crucial, particularly when resources are limited in the local agencies and the demands are more. In such conditions, it becomes a challenging job for the agencies to distribute resources so that the resources are optimally used. This article is initiated to address the challenging fact by introducing the concept of priority measure for demands. In this regard, ranking of the affected areas based on resource requirement for a disaster scenario is evaluated through a multicriteria decision-making process. A formula is developed to find the numerical value of priority factor collecting the responses of requirement of relief materials using information crowdsourcing. To this contingent, a three-echelon emergency solid transportation problem is formulated, in which the demand with the most priority is fulfilled by the local agencies in echelon-I, and the remaining demand is fulfilled by the other national or international agencies in echelon-II along with a restoration of local agencies in echelon-III. A numerical example is presented for the sake of model performance and to test the convergence using LINGO and CPLEX optimization solvers. The model is also designed using a genetic algorithm to obtain the optimal solution. Moreover, a brief analysis of the model is presented by considering 11 different numerical instances to enhance the efficacy of the model in real-life applications.

Closed-loop supply chain (CLSC) implementation has become imperative for organizations owing to resource scarcity and generation of toxic waste. However, CLSC implementation can be very challenging due to several barriers, especially in an emerging manufacturing hub such as India. This article identifies and analyzes the prominent barriers to CLSC implementation in the Indian automotive sector. Further, this article also examines causal relationships between the barriers. A total of 22 barriers to CLSC implementation are identified from the literature and subsequently, validated by industry experts. The finalized barriers are then evaluated by analytical hierarchy process and grey–decision making trial and evaluation laboratory techniques. This article provides managers and policy makers with insights on the most prominent and causal barriers, and recommends strategies to overcome these barriers. The findings will help managers to frame short-term and long-term strategies to mitigate barriers to CLSC and contribute toward successful implementation of CLSC.

В работе рассматриваются модели потоков данных и этапы цифровой обработки сигналов в мобильном устройстве оперативного определения жизнеугрожающих состояний сердечно-сосудистой системы. Устройство считывает сигналы ЭКГ пациента и анализирует различные виды аритмий сердца. При обнаружении жизнеугрожающего состояния устройство формирует сообщение пациенту и передает данные ЭКГ на компьютер врача. Особенностью данного устройства является его персонификация и применение надежного выявления комплексов QRS за счет использования алгоритма анализа формы сигнала, основанного на методе Dynamic time warping. Благодарности: работа выполнена при финансовой поддержке гранта РФФИ, проект № 18-413-310002

The primary objective of the case study is to help students understand, model, and solve capacity planning problems (i) when the production yield is uncertain and (ii) if the yield of first production is low, the product can be produced a second time. The dimension of random yield emphasizes supply chain management challenges beyond the traditional newsvendor problem, which has a nice closed-form solution. The dimension of operational flexibility underscores the importance of modeling skills, such as influence diagrams, that can be used to brainstorm, model, and solve new problems. Extensive use of the case in graduate supply chain management courses shows that in the absence of a mathematical model, students systematically deviate from optimal capacity usage. Usually, the presence of backup flexibility reduces first-period production. However, students’ responses often suggest cutting first production too much. When it is available, students consistently overuse the flexibility of second-period production. Overall, students believed the case was challenging and that it provides a valuable learning experience.

We conduct a risk analysis of non-core insurance business of selling protection to financial firms against investment losses due to a shock. A static structural model is constructed, composed of a network of firms who cross-hold each other, a financial market consisting of multiple primitive assets that are vulnerable to a shock, and an insurer who resides external to the network and assesses the opportunity to sell protection to the financial firms. Assume that each firm in the network is rational and able to decide how much protection to purchase to optimize its portfolio according to the mean-variance principle. As a result, the shock may impact on the insurer but indirectly through the network. In view of the robust-yet-fragile nature of financial networks that has been discovered, both empirically and theoretically, by various recent works, one expects that the network integration and the shock play an intertwined role in the insurance risk. Our study forms a theoretical confirmation of this surmise: Depending on the shock size, there are three mutually exclusive scenarios in which an increase in the network integration can either reduce or amplify the impact of the shock on the insurance risk.

In the digital age, firms continuously look for leading technologies and make products of superior quality to satisfy new and sophisticated consumers’ needs. Although this approach strives to sell a product more appealing to customers, it also renders the product more complex and difficult to use, generating the feature fatigue effect. This is characterized by feelings frustration experienced by consumers when purchasing high-tech products with many appealing features, most of which turn out to be undesired, useless, and difficult to use. We study the impact of the feature fatigue effect on a manufacturing firm’s policies in designing its product–service portfolio. We consider two periods in order to capture the effect of customers’ word-of-mouth originating from feature fatigue, which is shared with other potential customers. Therefore, we assess not only the impact of feature fatigue but also the extent to which this effect can be alleviated by after-sales service. We capture feature fatigue by first considering a base model without this effect and then comparing the manufacturer’s decisions regarding quality, pricing, and service when it has either clear or noisy effect. Our findings reveal that feature fatigue drives manufacturers to under invest in quality and to replace penetration pricing policies with skimming policies. In addition, we demonstrate that after-sales service cannot fully mitigate the detrimental effect of feature fatigue on a manufacturer’s profits.

Many companies are considering “social recommendation” for their businesses, e.g., “rms are offering rewards to customers who recommend the firms” products/services in online social networks (OSNs). However, the pros and cons of such social recommendation scheme are still unclear. Thus, it is difficult for firms to design rewarding schemes, and for OSN platforms to design regulating policies. Via empirical analysis of data, we first identify key factors that affect the spreading of a firm's product in OSNs. These findings enable us to develop an accurate (i.e., with a high validation accuracy) mathematical model on social recommendations. Our model captures how users decide whether to recommend an item, which is a key factor but often ignored by previous social recommendation models. We also design algorithms to infer model parameters. Using our model, we uncover conditions when social recommendation improves a firm's profit and users’ utilities, as well as when it cannot improve the profit or hurts users’ utilities. These conditions help the design of both rewarding schemes and regulating policies. Last but not least, we extend our model to an dynamic setting, so that a firm can improve its profit by dynamically optimizing its rewarding schemes.

Improving reliability and eliminating the potential design risk are crucial for product design. Failure mode and effect analysis (FMEA), as an effective reliability assurance tool, has been extensively used in product design. However, causalities among failure modes, interactions among risk factors, and correlations among risk evaluations were not jointly considered in existing FMEA methods. On the other hand, the cost and time caused by the occurrence of failure modes were seldom incorporated into risk factors. Due to the lack of accurate values and/or information loss of customers and experts, these risk factors inevitably contain uncertainty that cannot be overlooked in product design. In this article, a novel FMEA-based approach is proposed to facilitate risk analysis of product design under uncertainty. In this approach, the stable grey causality vector state and the grey interaction vector are respectively established to characterize causalities and interactions. To reduce the uncertainty contained in cost and time, the extended information axiom is put forth, and then, their grey information contents can be obtained and are to be incorporated into the design. Subsequently, we extend Choquet integral to prioritize the potential failure modes and identify the optimal design scheme while coping with correlations. The proposed approach is validated by an example of a substrate design.

This experimental study analyzes how a key factor, information load, influences decision making in escalation situations, i.e., in situations in which decision makers reinvest further resources in a losing course of action, even when accounting information indicates that the project is performing poorly and should be discontinued. This study synthesizes prior escalation research with information overload and investigates how different levels of information load influence the escalation of commitment. Our findings reveal a U-shaped effect of information load: When decision makers face negative feedback, a higher information load mitigates the escalation tendency up to a certain point. However, beyond this point, more information reinforces the escalation tendency. Moreover, we find that the type of feedback affects self-justification, and we find a negative and significant interaction between information load and self-justification in negative-feedback cases. Thus, studies investigating escalation of commitment should control for self-justification and information load when utilizing high levels of information load. Finally, in the positive-feedback condition, higher information load encourages decision makers to continue promising courses of action, i.e., increases decision-making performance., Projekt DEAL

Risk Based Inspection (RBI) has been implemented mainly in oil and gas industry to manage the risk of aging facilities. The RBI plan has also been introduced for new facilities and become part of the design requirements, making it the right time for PT. XYZ to improve their RBI implementation to support the Facility Risk Integrity Management System. This is a semi-quantitative study on primary data collected through focus group discussion using the RBI evaluation parameters based on API 580, API 581, and field observation and secondary data from previous RBI reports, maintenance program, and inspection program. Finally, an evaluation was conducted to assess whether the RBI management strategy has been integrated to the safety and asset management; inspection, testing, and monitoring; and operation strategies. This gap analysis aims to evaluate the effectiveness of the ongoing RBI implementation at PT. XYZ in general while specifically identify the part of RBI and related Asset Integrity Management already implemented and those that still need further improvement. Results show an overall score of 328 of 470, showing a good implementation of RBI. The largest gaps identified are RBI on specific equipment (score=3.0), documented RBI management policy and strategy (score=3.8), risk target and risk acceptable level (score=4.0), and specific damage mechanism components (score=5.3).

The COVID-19 pandemic has led to the unprecedented challenge of devising massive vaccination rollouts, toward slowing down and eventually extinguishing the diffusion of the virus. The two-dose vaccination procedure, speed requirements, and the scarcity of doses, suitable spaces, and personnel, make the optimal design of such rollouts a complex problem. Mathematical modeling, which has already proved to be determinant in the early phases of the pandemic, can again be a powerful tool to assist public health authorities in optimally planning the vaccination rollout. Here, we propose a novel epidemic model tailored to COVID-19, which includes the effect of nonpharmaceutical interventions and a concurrent two-dose vaccination campaign. Then, we leverage nonlinear model predictive control to devise optimal scheduling of first and second doses, accounting both for the healthcare needs and for the socio-economic costs associated with the epidemics. We calibrate our model to the 2021 COVID-19 vaccination campaign in Italy. Specifically, once identified the epidemic parameters from officially reported data, we numerically assess the effectiveness of the obtained optimal vaccination rollouts for the two most used vaccines. Determining the optimal vaccination strategy is nontrivial, as it depends on the efficacy and duration of the first-dose partial immunization, whereby the prioritization of first doses and the delay of second doses may be effective for vaccines with sufficiently strong first-dose immunization. Our model and optimization approach provide a flexible tool that can be adopted to help devise the current COVID-19 vaccination campaign, and increase preparedness for future epidemics.

Measurement of traffic flow stability and resilience is a critical step toward evaluating the performance of transportation systems and implementing appropriate management strategies. Quantifying changes in the stability and resilience of transportation systems, however, is hampered by the complexity of real traffic dynamics and the diversity of infrastructures. Here, we demonstrate that changes in traffic flow stability and resilience are signaled by generic features, known as early warning signals in the theory of critical slowing down, observed before traffic instabilities occur. This finding is incorporated in an operational data-driven algorithm to evaluate the risk of traffic jams on highways. Theoretical findings and tests on simulated and empirical case studies support the premise of this approach and identify candidate statistical measures that are sensitive to changes in the stability and resilience of transportation systems. Our use of universal measures advances the monitoring capability, prediction and control of complex transportation systems.

Fusion energy has the potential to address long-term energy requirements and climate change. However, fusion energy is characterized by a “development divide” between the International Thermonuclear Experimental Reactor (ITER) consortium members and the non-ITER “Global South” states. It is beset with problems, which can be divided into geopolitical, geo-economic, geo-sociocultural, and geo-technological (GEO-PEST). Geopolitical problems include cooperation on fusion energy development between ITER members (of the Global North) and the Global South on fusion development. Geo-economic problems include the cost of funding ITER versus newly emerging private-sector fusion companies. Geo-sociocultural problems include the requirement to maintain the peacebuilding tradition of nuclear energy (embodied by the Cold War-era “Atoms for Peace” initiative), as well as public perceptions of radiation. Geo-technological problems include tokamak lock-in, fuel type, the viability of “compact reactors,” and disruptive technology events. In this article, we outline these problems and discuss how to address them in a timely fashion via an external independent review mechanism, modeled on the International Energy Agency's “Global Commission for Urgent Action on Energy Efficiency,” established in 2019 and due to deliver recommendations in 2020.

Existence of delays is an inseparable part of projects and subject of disagreements among stakeholders in all countries. There are four valid techniques in the Society of Construction Law global protocol about project delays calculation that without having the ability to control the risk of delays incidence only calculate the prolongation of contract prolongation and consider it as project delays. Researcher believes: first, risk of delays should be managed, minimized, shared, transferred, or accepted, but it cannot be ignored; therefore, it must be predicted, covered, managed, and optimized; and second, prolongation time of project is not necessarily equal to increased delay amount, and many other factors that themselves are the basis of emergence of project risks are effective in calculation of delays. Therefore, it is necessary to identify these factors and develop the strategy for their control and management before starting the project. So, in this article, two researcher-made combinative techniques are presented in order to resolve this deficiency: first, introduction of importance degree factor model instead of weight factor; and second, introduction of a model in the field of claim management in order to controlling risk of delays, analyzing result-oriented delays, and determining the share of stakeholders’ failures, based on variance and importance degree.

With the recent growing number of cyberattacks and the constant lack of effective defense methods, cyber risks become ubiquitous in enterprise networks, manufacturing plants, and government computer systems. Cyber-insurance provides a valuable approach to transfer the cyber risks to insurance companies and further improve the security status of the insured. The designation of effective cyber-insurance contracts requires the considerations from both the insurance market and the dynamic properties of the cyber risks. To capture the interactions between the users and the insurers, we present a dynamic moral-hazard type of principal-agent model incorporated with Markov decision processes, which are used to capture the dynamics and correlations of the cyber risks as well as the user's decisions on the protections. We study and fully analyze a case with a two-state two-action user under linear coverage insurance, and we further show the risk compensation, Peltzman effect, linear insurance contract principle, and zero-operating profit principle in this case. Numerical experiments are provided to verify our conclusions and further extend to cases of a four-state three-action user under linear coverage insurance and threshold coverage insurance., Comment: We have made several corrections

The Hyperloop is a concept of a ground transportation system consisting of capsules (called pods) traveling at very high-speeds in near-vacuum tubes. The hyperloop aims to be a fast, cheap, and sustainable alternative to short-haul flights and high-speed rail. The small pod size requires very high frequencies to respond to future high levels of passenger and cargo demands. Media and representatives of the emerging Hyperloop industry acclaim the Hyperloop as a very capacity-effective transport system, however there is no clear scientific evidence proving that. A theoretical investigation is therefore necessary to understand which capacity the Hyperloop could safely provide and whether that could satisfy the future transport demand. This paper provides a comparative analysis of the capacity that the Hyperloop can offer for several operational scenarios and different signalling systems, including Moving-Block and the advanced concept of Virtual Coupling. Results show that Moving-Block could achieve required transport capacity levels only if pods could use high deceleration rates likely to be unsafe and uncomfortable to passengers. Virtual Coupling is instead observed to be a more satisfactory operational concept that could address transport demand while respecting safety and comfort standards if reliable pod platooning technologies are available.

Hazardous materials (hazmat) shipments are integral to any modern society, and railroad is the primary transportation mode in North America. Given the catastrophic nature of rail hazmat incidents, every effort should be made to mitigate risk. This study investigates a novel risk mitigation tool, i.e., subsidy, to induce railroad operators to take alternate routes that are away from high-risk portions of the network such as population centers. To this end, a bi-level optimization program is developed that captures both the risk perspective of the leader (i.e., government) and the cost concerns of the follower (i.e., railroad operator). Model complexity necessitated the development of a customized solution technique, and the resulting methodology was used to study problem instances generated on the realistic infrastructure of a railroad operator in Midwest United States. Analyses underscore the significance of using subsidy as a risk mitigation tool and in ensuring a fairer distribution of risk, in demonstrating how significant improvement in risk can be achieved by offering modest subsidy, and in defining the threshold level of the subsidy budget beyond which no improvement should be expected.

The coronavirus disease 2019 (COVID-19) epidemic has spread worldwide, posing a great threat to human beings. The stay-home quarantine is an effective way to reduce physical contacts and the associated COVID-19 transmission risk, which requires the support of efficient living materials (such as meats, vegetables, grain, and oil) delivery. Notably, the presence of potential infected individuals increases the COVID-19 transmission risk during the delivery. The deliveryman may be the medium through which the virus spreads among urban residents. However, traditional delivery route optimization methods don't take the virus transmission risk into account. Here, we propose a novel living material delivery route approach considering the possible COVID-19 transmission during the delivery. A complex network-based virus transmission model is developed to simulate the possible COVID-19 infection between urban residents and the deliverymen. A bi-objective model considering the COVID-19 transmission risk and the total route length is proposed and solved by the hybrid meta-heuristics integrating the adaptive large neighborhood search and simulated annealing. The experiment was conducted in Wuhan, China to assess the performance of the proposed approach. The results demonstrate that 935 vehicles will totally travel 56,424.55 km to deliver necessary living materials to 3,154 neighborhoods, with total risk [Formula: see text]. The presented approach reduces the risk of COVID-19 transmission by 67.55% compared to traditional distance-based optimization methods. The presented approach can facilitate a well response to the COVID-19 in the transportation sector.

Pursuing long-term climate stabilisation scenarios subjects power, heat, industry, and transport sectors to undergo fundamental transitions towards fully renewable energy systems. Cost-efficient strategies involve the integration of energy vectors, introducing additional cross-sectoral electricity demand and flexibility for traditional power sectors. When performing cross-sectoral capacity expansion planning (CEP) analyses of low-carbon energy systems for decision making support, the underlying mathematical optimisation problems easily become intractable. To overcome the computational challenges of optimising real-world instances of future energy systems with adequate representations of temporal, spatial, and technological detail, this work investigates different modelling approaches for flexible heat pump systems at the power and heat sector interface of the long-term energy system model SCOPE SD. A comparative case study carried out for a net-neutral decarbonisation scenario in Europe and Germany highlights the implications of using model aggregation techniques. It is shown that the proposed modelling approaches can present computational benefits at only limited cost of result accuracy. However, poorly implemented grouping strategies can lead to high result inaccuracies and even a lack of performance improvements.

Code remains largely hand-made by humans and, as such, writing code is prone to error. Many previous studies have focused on the technical reasons for these errors and provided developers with increasingly sophisticated tools. Few studies have looked in detail at why code errors have been made from a human perspective. We use Human Error Theory to frame our exploratory study and use semi-structured interviews to uncover a preliminary understanding of the errors developers make while coding. We look particularly at the Skill-Based (SB) errors reported by 27 professional software developers. We found that the complexity of the development environment is one of the most frequently reported reasons for errors. Maintaining concentration and focus on a particular task also underpins many developer errors. We found that developers struggle with effective mitigation strategies for their errors, reporting strategies largely based on improving their own willpower to concentrate better on coding tasks. We discuss how using Reason's Swiss Cheese model may help reduce errors during software development. This model ensures that layers of tool, process and management mitigation are in place to prevent developer errors from causing system failures.

Designing feasible control strategies for opinion dynamics in complex social systems has never been an easy task. It requires a control protocol which 1) is not enforced on all individuals in the society, and 2) does not exclusively rely on specific opinion values shared by the social system. Thanks to the recent studies on noise-induced consensus in opinion dynamics, the noise-based intervention strategy has emerged as the only one meeting both of the above requirements, yet its underlying general theory is still lacking. In this paper, we perform rigorous theoretical analysis and simulations of a noise-based control strategy for opinion formation in which only a fraction of individuals is affected by randomly generated noise. We found that irrespective of the number of noise-driven individuals, including the case of only one single noise-affected individual, the system can attain a quasi-consensus in finite time, and the critical noise strength can be obtained. Our results highlight the efficiency of noise-driven mechanisms for the control of complex social dynamics., arXiv admin note: text overlap with arXiv:1711.01432

The era of the industrial revolution 4.0 was an era marked by the transition of information and communication technology that was able to create new technology-based investments. Internet of things (IoT), Big Data, and Cloud Computing are the foundations that underlie this 4.0 industrial revolution. Cloud Computing is a service that provides network storage space and computer resources using an internet connection as a medium of access. Cloud Service Providers (CSP) offer attractive services, making more and more companies want to migrate to the cloud. Sometimes the migration process to cloud computing faces problems or even failures, and this is certainly a risk for cloud service users. This study will identify the types of risks and risk components in cloud migration using the Systematic Literature Review (SLR) method. The databases used in selecting articles that match the criteria include: Emerald Online, IEEE Xplore, ScienceDirect, SpringerLink, and between 2015 and 2020. The results of this study, there were 74 articles selected according to the criteria and reviewed. The output of this study shows that there are 7 types of risk in cloud migration, namely information security risk, risk of losing data access, risk of using virtual machines, errors in choosing CSPs, risk of compliance with various laws and regulations, financial risk, and management failure, the weights of 25%, 21%, 18%, 14%, 11%, 7%, and 4% respectively, as well as 5 risk components, namely threats, impacts, risk factors, vulnerabilities, and damage with a weight of 33%, 27%, 20%, 13%, and 7%.

Under the dispute between China and the United States, the international field of public opinion is dominated by increasing tensions, and the network rumors triggered by the COVID-19 epidemic are intensifying. In view of the above-mentioned context, this paper focuses on the development and the evolution process of public opinions. Since the evolution of public opinion is often accompanied by the spread and diffusion of information, this paper combines the process of information diffusion with the development process of polarization behavior, and brings in the dynamic network and the timeliness factor of public opinion dissemination, so as to better explore the polarization process of public opinion under the dynamic network. Then, this paper focuses on the analysis of the parameters of the model and through the dynamic adjustment of parameters, finding out the main factors that affect the trend and development of network public opinion. In addition, this paper introduces an actual case, and takes the actual case data as the support to demonstrate the reliability and practical application value of the model. Finally, based on the simulation results and analysis of actual cases, this paper puts forward the corresponding preventive measures to alleviate the polarization behavior of the group.

Buried natural gas pipelines are vulnerable to external corrosion because they are encased in a soil environment for a long time. Identifying the causes of external corrosion and taking specific maintenance measures is essential. In this work, a risk analysis and maintenance decision-making model for natural gas pipelines with external corrosion is proposed based on a Bayesian network. A fault tree model is first employed to identify the causes of external corrosion. The Bayesian network for risk analysis is determined accordingly. The maintenance strategies are then inserted into the Bayesian network to show a reduction of the risk. The costs of maintenance strategies and the reduced risk after maintenance are combined in an optimization function to build a decision-making model. Because of the limitations of historical data, some of the parameters in the Bayesian network are obtained from a probabilistic estimation model, which combines expert experience and fuzzy set theory. Finally, a case study is carried out to verify the feasibility of the maintenance decision model. This indicates that the method proposed in this work can be used to provide effective maintenance schemes for different pipeline external corrosion scenarios and to reduce the possible losses caused by external corrosion.

In the aftermath of large-scale disasters, the exploitation of often up to thousands of spontaneous volunteers is crucial to meet the need for surge capacity which cannot be met by official responders. However, the coordination of spontaneous volunteers differs in several regards from that of professional and paid relief workers. Based on empirical requirements identified in interviews with the manager of a professional fire department, we suggest a multi-objective mixed-integer linear optimization problem with lexicographically ordered objective functions, which we refer to as spontaneous volunteer coordination problem (SVCP). Acknowledging that disaster situations are unavoidably linked to uncertainty, we consider uncertainty with a sequence of (deterministic) SVCP instances, where each instance depends on the solutions of previous SVCP instances. We conduct comprehensive computational experiments based on real-world data of a flood disaster that the fire department faced. From our computational results, we derive detailed implications for the fire department on how to use our decision support model. We also derive recommendations for all relief organizations which aim at adopting or adapting our model for the coordination of spontaneous volunteers in a broad set of disasters. Our implications include several recommendations for relief organizations in terms of performing extensive computational tests in order to parameterize and instantiate the generic model before its use during the disaster response phase; thereby we also address tasks to be executed during the preparedness phase of a disaster.

The lack of national security standardization bodies can have adverse impact on the adoption of international security standards and best practices. To assure security confidence among various organizations and to promote systematic adoption of standards and best standards, a practical framework that can support comparative measures is needed. . This paper presents GoSafe, a novel practical cybersecurity assessment framework that is tailored to the ISO 2700x standard requirements for the development of Information Security Management System (ISMS). GoSafe can be used for both self-assessment and auditing/scoring tool by national cybersecurity authorities. Using GoSafe, organizations can evaluate their existing information security management systems against local and international standards by utilizing built-in pre-audit tools. As such, GoSafe will help organizations evaluate and enhance their readiness for evolving risks and threats. In GoSafe framework, a novel mathematical model was also designed and implemented for the scoring/rating tool, namely, the national cyber security index (aeNCI). The aeNCI employs multiple parameters to determine the maturity of existing cybersecurity programs at national organizations and generate a classification and comparison reports. The efficacy of GoSafe proposed framework is demonstrated using a practical case study. The results enabled the stakeholder to verify the security configuration of their systems and identify potential attack/risk vectors.

Plant factories have a great potential for mitigating the contradiction between the world’s growing population and food scarcity. During the process of its automatic production, machine vision plays a significant role. This technique almost covers every production link from raising seedlings, transplanting, management, and harvesting to fruit grading. To provide references and a starting point for those who are committed to studying this issue. In this paper, the application prospects of machine vision in plant factories were analyzed, and the present researches were summarized from the fields of plant growth monitoring, robot operation assistance, and fruit grading. The results found that although the existing methods have solved some practical problems at low cost, high efficiency and precision, some challenges still are faced by machine vision. Firstly, the changing lighting, complex backgrounds, and color similarity within plant different parts cause the commonly used image segmentation algorithms to fail. The shortage of standard agricultural datasets also keeps deep learning and unsupervised classification algorithms from making progress. Secondly, there are some theoretical knowledge gaps for machine vision application in a particular environment of plant factories, which seriously contains its application effect. Thirdly, the lack of special image acquisition devices and supporting facilities resulted in poor image quality. All these factors hinder machine vision application in plant factories. Nevertheless, it is still a powerful tool and irreplaceable at present. We believed that this technique would promote plant factory development greatly with more robust, efficient, and reliable algorithms are developed in the future.

Improving the resilience of complex engineered and engineering systems (CES) includes planning for complex resilience situations, in which there may be multiple threats, interactions, and disruptions. One challenge in the modeling of CES is the identification of how interactions in a complex situation occur and their combined influence on CES resilience. This article presents a resilience holon that can be used to analyze complex resilience situations. It is made up of 24 elements (defining types of resilience, threats, interactions, and disruptions), which have varying importance to specific situations. Holons can be linked together hierarchically or in a network. An application of the resilience holon to a documented real-world resilience situation, widespread flooding in a city, illustrates its use. Pathways taken by threats and disruptions, as the flood effects cascaded through the city, are shown as connections between holons. The resilience holon could be used to decompose diverse resilience situations involving CES, to identify where critical vulnerability points are and how the whole resilience situation could be improved. The visual nature of the resilience holon could be used in an interactive way, allowing stakeholders to better understand the full resilience picture of CES that they use or operate.

Housing constitutes a basic need for all living beings. Unfortunately, natural hazards, including floods, pose a severe threat to housing infrastructure systems. In turn, this paper develops a framework to quantify the resilience of housing infrastructure systems against flood hazards. The parameters for this resilience are based on the literature and knowledge from experts. This paper gauges the significance of each resilience parameter by using the analytic hierarchy process (AHP) and the fuzzy AHP. The evaluated values are then compared to observe the effectiveness of fuzzy AHP over AHP. The evaluated importance of each parameter will help stakeholders focus on the most important parameters and, in turn, boost the flood resilience of infrastructure. This paper then implements the developed framework in a study area to quantify local flood resilience. This resilience value will help stakeholders in the considered area to understand the resilience of local housing infrastructure.

This work looked at the justification of IT investments in general, to draw important conclusions that could be beneficial to IT project managers and professionals, and then zero in on the angle of benefits realization beyond IT investments.About 30 categories/sets of research outputs or articles out of more than 60 articles reviewed were used for this work. No primary data was employed for this work. Articles were sourced from databases such as Google Scholar, Research Gate, Academia.edu, Google search engine, Elsevier, and so on. The main themes used for the search were IT investments, Justification for IT investments, IT investments benefit determination, value creation beyond IT investments, and so on. The results showed that justification is unique to every firm, it is contextual, and so stakeholders must take into consideration environmental factors, corporate and strategic goals, experience and expertise of stakeholders, and so on, to design its framework and measures to justify IT investments. Zeroing on benefits realization, two things run through all the discussions: benefits realization of IT investments must take into consideration the organization's strategic objectives and that they do not simply emerge, as if by magic. Their realization has to be planned, delivered, reviewed, and exploited to ensure value realization on a more consistent/constant basis.