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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 .

Diagnosing a disease consumes a part of time, needs high technical methods but nowadays the smart technologies have been grown rapidly in the field of healthcare industries and also it improves the routine life of the patients, reduces the amount of work, treatment cost in the health care organization. Diseases prediction is one of the major challenges faced by society nowadays. The recent survey also stated that the death rate is remarkably high in CAD because most of the people are affected by cardiovascular diseases. Prediction and diagnosis of cardiovascular diseases is very essential nowadays to reduce the death rate and diagnosing it preliminary stage itself. In earlier studies, they worked with machine learning techniques to predict the diseases, but they are not given proper attention to identifying the feature with the help of proper feature selection methods. This paper proposed a new-found feature selection technique HRFLC (RANDOM FOREST + ADABOOST + PEARSON COEFFICIENT). This method helps to predict the diseases in a very efficient manner, and it improves the accuracy level in prediction.

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.

Sun-oriented solar power production with sunlight-based vitality plays an important role. In reality, untimely or innovative countries gracefully take a move forward. When oil is used for power era, it contributes to a rise in poisoning and greenhouse gases. This creates natural complications. Another human need for existence can be filled by using numerous sun- oriented developments in vitality, the planet can still prevent worldwide problems which can also serve to decrease pollution at the same time. In far-off areas where communities of citizens do not approach electricity photovoltaic frameworks are added many any administration offices have taken the amounts of action to allow creativity in sun-driven vitality to be used for power production. Single axis monitoring devices are discussed in this article. This analysis supports the findings. In comparison with the static systems, the single axis monitoring system is more accurate. In either scenario, a single axis monitoring system is simpler to accomplish in cost and adaptability viewpoints than a static one.

In present scenario, each and every activity in basic lifestyle of people is becoming smarter with the day-to-day flourishing development of technology such as Internet of Things, Artificial Intelligence, Machine Learning and Blockchain. Among the smart improvements, smart city holds the highest rank due to the initiatives provided by the administration. Smart city mission has influenced the citizen’s life with respect to the automation on fundamental needs. Energy resources are considered as one of the important requirements for the survival of people. With the advent of Internet of Things, smart grid systems are designed in order to meet the sustainable demands. It also provides information about the energy resources consumed and alert the consumers about the demand prevails on the resources. Technological improvement is more essential for a country, only if it can be considered and adaptable for sustainable development. Therefore these papers outline the Internet of Things based smart grid which helps in the energy conservation process to meet the future demands. Energy management processes has to be well-versed among the citizens as it helps in understanding the possible ways on storage of energy resources. However, when energy storage is concerned, security issues are the counterpart which has to be focused at higher level. Therefore, blockchain techniques applied for energy storage systems are also outlined along with the applications and future directions of smart grid.

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.

The recent COVID-19 pandemic pandemic has highlighted the importance of food safety and supply chain governance. In other words, we need to ensure traceability along the supply chain, support high-frequency transactions and effective data collections, etc. Thus, we posit the potential of using lightning network, which is a decentralized traceable paradigm for achieving high-frequency transactions in blockchain-based systems. In addition, we also utilize edge computing to help facilitate data collection. However, a key challenge in securing food supplies is determining the optimal global transaction path in lightning network, while achieving efficiency and meeting the dynamic nature of food supply management. Thus, we propose a blockchain-edge scheme that utilizes our proposed dynamic programming to produce optimal solutions to selecting global transaction paths. Specifically, our scheme optimizes routing fees under existing constraints (e.g., transmission cost, computing resource consumption, and lightning network balance). Findings from our evaluations demonstrate that the utility of our proposed approach in facilitating food safety management.

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.

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.

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%.

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.

People will always consciously or unconsciously compare their cost or benefits of actions with those of others and make judgments about fairness. Aiming at consensus reaching process where decision makers have fairness concern behaviors, this paper proposes a limited cost consensus model with fairness concern. To do this, we define the fairness utility function and fairness utility level based on fairness preference theory and explore some properties. In view of this, we propose a limited cost consensus approach with fairness concern of decision makers, which can obtain a stable and balanced consensus. Through emission reduction consensus problem of Chinese manufacturing enterprises, comparative analysis and sensitivity analysis are used to explain the proposed model. Conclusions about fairness preferences, consensus cost budget, and unit compensation cost can provide significant managerial references for real-world economic and management activities.

Influenza vaccine supply chains are subject to yield and demand uncertainty, which leads to the mismatch between supply and demand. This paper studies the production and procurement decisions in the influenza vaccine supply chain consisting of a manufacturer facing uncertain production yield and a social planner who faces uncertain demand and is responsible for social infection costs. We develop a novel model framework considering the risks resulting from uncertainties on both supply and demand sides that the manufacturer may delay part of delivery and the social planner may order under or over demand. Based on the framework, we first discuss the production and procurement decision-making problem in a centralized and a decentralized system. By comparing the two systems, we present the existence and extent of the inefficiency in the influenza vaccine supply chain. Further, we propose a procurement strategy that provides the social planner with two ordering opportunities and explore the conditions under which the two members would be willing to accept the strategy. The results show that such a strategy is effective on improving the influenza vaccine supply chain coordination. Additionally, considering large-scale outbreaks of influenza epidemics, we extend the models with a piecewise linear infection probability function to those with a generic one and describe the characteristics of the extended models and their solutions.

The main goal of project control is to measure the actual project progress such that the deviations from the plan can be identified and corrective actions can be taken to bring the project back on track. However, in resource-constrained projects, disrupted activities affect their successors due to precedence relations and the other activities due to resource constraints, both of which will result in deviations during project progress. Since the project control approaches solely focus on the deviations based on the network analysis, they do not accurately reflect the progress of resource-constrained projects. This paper extends project control approaches for resource-constrained projects to measure and evaluate whether the project progress is acceptable. Moreover, we design three scenarios considering possible resource conflicts to take corrective actions when needed. In the computational experiment, this project control process is applied to a large set of projects with different characteristics and further validated on real-life project data. The results show that the proposed scenarios and different project control approaches are efficient and reliable, but their use depends on project network structure and resource scarceness.

Improved supply chain optimization strategies can play a major role in addressing global food security and safety in years to come. In particular, tighter safety regulations, changing consumer quality requirements and more stringent market competition call upon integrated supply chain decision-making frameworks that explicitly consider product quality control. This effort requires metrics of quality that accurately reflect product physico-chemical properties, as well as consumer purchasing preferences. However, a critical challenge linked to embedding the complex dynamics of the evolution of product quality in time within supply chain models is the large-scale nature of the ensuing optimization problems, which are computationally intractable even for moderate-size, single-item systems. In the present work, we introduce a computationally efficient optimal production and distribution planning framework for perishable products having multiple quality attributes that evolve in time as a function of environmental conditions during shipment and storage. We also propose a model reduction strategy and a decomposition framework that enhance the scalability of our approach. We perform extensive numerical simulations using different network instances to validate our theoretical findings, as well as to demonstrate the advantages of the proposed supply chain management scheme.

As we are continuously depending on information technology applications by adopting electronic channels and software applications for our business, online transaction and communication, software security is increasingly becoming a necessity and more advanced concern. Both the functional and non-functional requirements are important and provide the necessary needs at the early phases of the software development process, specifically in the requirement phase. The aim of this research is to identify security threats early in the software development process to help the requirement engineer elicit appropriate security requirements in a more systematic manner throughout the requirement engineering process to ensure a secure and quality software development. This article proposes the STORE methodology for security requirement elicitation based on security threats analysis, which includes the identification of four points: PoA, PoB, PoC and PoD for effective security attack analysis. Further, the proposed STORE methodology is also validated by a case study of an ERP System. We also compare our STORE methodology with two existing techniques, namely, SQUARE and MOSRE. We have shown that more effective and efficient security requirements can be elicited by the STORE methodology and that it helps the security requirement engineer to elicit security requirements in a more organized manner.

The success of requirement prioritization process largely depends upon how well different constraints and influential factors are handled by stakeholders and developers while prioritization. The main goal of this research is to present a semi-automated dependency based collaborative requirement prioritization approach (CDBR), which uses linguistic values, execute-before-after (EBA) relation among requirements and machine learning algorithm to minimize the difference of opinion between stakeholder and developers for effective collaboration and for better approximation of final prioritization results, acceptable to both. The presented approach targets three major constraints rarely addressed in existing work, namely dependencies among requirements, communication among stakeholder and developers and the issue of scalability. Results of performance assessment conducted on several different requirement sets and on a case study by comparing CDBR with other state of the art approaches namely, AHP and IGA. The results are accurate and comparable in terms of effectiveness, efficiency, scalability and disagreement concerns among stakeholder and developers which in turn provides robustness to decision making process of awarding more importance to some requirements over others. CDBR overpowers AHP and IGA in terms of efficiency and processing time respectively.

Micro/nano topographic structures have shown great utility in many biomedical areas including cell therapies, tissue engineering, and implantable devices. Computer-assisted informatics methods hold great promise for the design of topographic structures with targeted properties for a specific medical application. To benefit from these methods, researchers and engineers require a highly reusable “one structural parameter – one set of cell responses” database. However, existing confounding factors in topographic cell culture devices seriously impede the acquisition of this kind of data. Through carefully dissecting the confounding factors and their possible reasons for emergence, we developed corresponding guideline requirements for topographic cell culture device development to remove or control the influence of such factors. Based on these requirements, we then suggested potential strategies to meet them. In this work, we also experimentally demonstrated a topographic cell culture device with controlled confounding factors based on these guideline requirements and corresponding strategies. A “guideline for the development of topographic cell culture devices” was summarized to instruct researchers to develop topographic cell culture devices with the confounding factors removed or well controlled. This guideline aims to promote the establishment of a highly reusable “one structural parameter – one set of cell responses” database that could facilitate the application of informatics methods, such as artificial intelligence, in the rational design of future biotopographic structures with high efficacy., Graphical abstract Image 1

Incentive mechanisms are essential for stimulating adequate worker participation to achieve good truth discovery performance in mobile crowdsensing (MCS) systems. However, most of existing incentive mechanisms only consider compensating workers' sensing cost, while the cost incurred by potential privacy leakage has been largely neglected. Moreover, none of existing privacy-preserving incentive mechanisms has incorporated workers' different privacy preferences to provide personalized payments for them. In this paper, we propose a contract-based personalized privacy-preserving incentive mechanism for truth discovery in MCS systems, named Paris-TD, which provides personalized payments for workers as a compensation for privacy cost while achieving accurate truth discovery. The basic idea is that the platform offers a set of different contracts to workers with different privacy preferences, and each worker chooses to sign a contract which specifies a privacy-preserving degree (PPD) and the corresponding payment the worker will receive if she submits perturbed data with that PPD. Specifically, we respectively design a set of optimal contracts analytically under both full and incomplete information models, which maximize the truth discovery accuracy under a given budget, while satisfying the individual rationality and incentive compatibility properties. The feasibility and effectiveness of Paris-TD are validated through experiments on both synthetic and real-world datasets.

Neuromarketing has brought a revolution in the field of marketing. The adoption of Neuromarketing has resulted in better understanding of consumer behaviour. The thrust for increasing business and branding through brains has a great impact to marketing. Neuromarketing, being an interdisciplinary research area has emerged as a solution for achieving better understanding of consumer behaviour. For sustainable future and increased business, neuromarketing techniques need sincere attention. Although it is an active research area and there are many solutions for achieving efficiency, existing approaches ignore the integrated framework for understanding neurometric data. The real challenge is to maintain a satisfactory performance level without raising costs. The current study provides a wide-ranging study of literature. The literature highlights that there is very less research being carried out using an integrated framework. An optimal framework that simultaneously handles performance and maintains low cost is required as foremost thing for advertisers. In the present research work, efforts will be made to propose towards cost efficient framework for understanding advertisers’ perspective.

Human body is continuously in a never-ending chess game against pathogens. When the immune system, our natural defense tool, is weakened, these organisms are able to escape, collapsing the body contingency plan, which results in a pathological state. To overcome this checkmate status, emerging nanomedicines have been successfully employed as one of the best booming tactic for the immune response, manipulating the body defense tools for a specific recognition/elimination of pathological cells via active ingredient delivery. However, the vast majority of these drug delivery systems (DDS) has been often considered exclusively as passive vehicles. Among them, nanoscaled metal-organic frameworks (nanoMOFs) have attracted a great attention due to their versatility, allowing to carry and deliver exceptional drug payloads and to modulate their biological bypass. Nonetheless, their intrinsic immunogenicity character has been never addressed. Considering the immense possibilities that nanoMOFs offer as treatment platform, our purpose is unveiling the MOF immunological fingerprint, including an in-deep evaluation of the cellular oxidation balance, the inflammation & recruitment of immune cells and the precise Th1/Th2 cytokine profile triggered. This performance will make more feasible the design of customized immune-active MOF nanoplatforms according to targeted diseases, becoming the next ace up of the immune system sleeve.

Growing population and depleting resources are forcing the manufacturers to become more competent and sustainable. Customers are becoming more and more informed about the product they consume, and therefore, manufacturers need to be flexible and open to new changes taking place. Big Data Analytics (BDA) is one such technology that has transformed the manufacturing sector. Organizations have now become more flexible with regards to their processes and, at the same time, have also improved their product quality. Here in this paper, we have analyzed various advantages of Big Data that manufacturing organizations can exploit for their betterment. We have also discussed examples of certain leading manufacturers and how they can lead to big data-enabled manufacturing. Lastly, we discuss certain shortcomings that need to be addressed to make big data analytics viable for organizations of all sorts and not just for top manufacturers.

Biosensors for sensitive and specific detection of foodborne and waterborne pathogens are particularly valued for their portability, usability, relatively low cost, and real-time or near real-time response. Their application is widespread in several domains, including environmental monitoring. The main limitation of currently developed biosensors is a lack of sensitivity and specificity in complex matrices. Due to increased interest in biosensor development, we conducted a systematic review, complying with the PRISMA guidelines, covering the period from January 2010 to December 2019. The review is focused on biosensor applications in the identification of foodborne and waterborne microorganisms based on research articles identified in the Pubmed, ScienceDirect, and Scopus search engines. Efforts are still in progress to overcome detection limitations and to provide a rapid detection system which will safeguard water and food quality. The use of biosensors is an essential tool with applicability in the evaluation and monitoring of the environment and food, with great impact in public health.

Global Software Development (GSD) is becoming increasingly prevalent, with software development teams being distributed around the world and working in collaboration with partner companies despite geographic and time differences. The main advantage of GSD which makes it attractive is the greater availability of human resources at lower costs. However, there are several disadvantages which are caused by the distance which separates the development teams. Coordination and communication become more difficult when the software development teams are located in different places, resulting in hidden costs involved in this process. As such, the effort estimation models used for collocated software development are inadequate for estimation in GSD. Thus, effort estimation in GSD is becoming an important area of research. Many researchers have focused on effort estimation in GSD over the last decade. This paper presents the findings of a systematic review of the related literature by summarizing the hidden costs in GSD, and discussing the open research issues in effort estimation in GSD.

In recent years, smart energy has become an important part of smart city construction. For the oil industry, exploring how to apply enabling technologies such as data transmission, internet of things, big data analysis in the energy field can fill the gap of applying intelligent technologies to energy facilities system. For the petroleum field, one of the biggest threats to safe drilling operations during oil and gas exploration is loss circulation. In the study, based on a new assessment framework that integrates intelligent energy into smart cities, an improved fuzzy evaluation method of drilling risk based on cloud method is proposed to realize the transformation of drilling risk from quantitative to qualitative. The theory proposed in this paper considers the subjective engineering experience of experts and the information contained in objective data, which make the judgment results more objective. The cloud theory proposed can clearly show the result of risk assessment, which fully reflects the fuzziness and randomness of the drilling system. The model is embedded in the evaluation framework of smart cities, which realizes the online remote online assessment of risks by city office workers.

Work-related fatigue is a multidimensional phenomenon with significant effects on operational performance. Our work focuses on how the literature of operational research measures and models fatigue and its effects on operational performance, and on how it mitigates those effects. We position the literature of fatigue relative to that of work-rest scheduling, shift scheduling, multitasking, ergonomics, deterioration scheduling, and occupational health and safety. We classify the literature of fatigue across multiple dimensions: the methods by which it is identified and measured; the operational research methodology applied for fatigue prevention or mitigation; the flexibility allowed in work-rest scheduling and in shift scheduling; applications within manufacturing, construction, transportation, hospitals, and services; and the extent to which real data is used and results are implemented. Our work shows that operational research has contributed numerous effective algorithms and heuristic solution procedures to fatigue mitigation. We also identify several important research directions for operational research, to promote its broader and more effective use to identify and mitigate the effects of fatigue on operational performance.

The construction industry is one of the sectors where accidents causing injuries among workers appear more frequently than in other industries. The safety issues are of high interest. There are special regulations introduced to protect all people engaged on a construction site. Besides the human attitude, every accident causes forced brakes in the construction process. It leads to financial losses and lowering the trademark of a certain contractor. Curing process of injured workers, as well as, investigation about for reasons of an accident generates additional cost. The development of accidents prevention then is highly desirable for several reasons. As wearable technologies become more popular and affordable, they can be applied to monitor the locations of machinery and the staff on the construction site. Based on that risky configurations can be detected, and relevant actions can be undertaken to protect working people from any injuries. Different types of wearable technologies are presented together with the discussion of their applicability in monitoring in the construction sector. Moreover, the survey is done to better recognition of health risks. Based on that the recommendations are formulated for future applications of wearable technologies as an innovative tool of risk management in construction.

Implantable biosensors are not yet at the developmental phase where they can be vital for personalized medicine, deconstructing healthcare as we see it today. However, the obstacles have been identified and multidisciplinary teams of researchers worldwide have been working to overcome the main technical problems that hamper their clinical application. TE strategies for implantable biosensors seem to be the way to tackle body’s immune rejection, since they can minimize the bodýs response by increasing their biocompatibility and reducing tissue mismatch, maintaining biosensor functionality for longer.

There are three main criteria that account for a construction project’s success: completion on-time, within budget, and following the specifications. Nowadays, construction projects are facing various problems where delays are considered major one of them, particularly in Egypt. Analyzing construction delay causes while attempting to mitigate their impact is a matter of crucial importance. That’s why a program was developed to help the user assess the delay causes’ impact with respect to the project’s concerned activities. Besides, it was designed to identify the potential mitigation measures according to the delay cause type. The Building Information Modeling (BIM) was the platform used to carry out such analysis. Thus, the main objective of this study is to investigate the delay causes’ effect on the project’s duration and cost. This was achieved by analyzing the concerned activities’ affected duration and cost, according to the selected delay cause and its associated mitigation measures, by means of a developed user plug-in. Then, duration/cost summary tables were presented for the user to highlight the main findings in terms of delay and saving impact. The attained results were considered important indicators to be referred to for similar cases throughout the project progress. Apart from being a helpful tool for assessing construction delay causes’ impact, this developed user plug-in could also serve in future research works associated to this subject.

Congestion is an economic phenomenon of overinvestment that occurs when excessive inputs decrease the maximally possible outputs. Although decision-makers are unlikely to decrease outputs by increasing inputs, congestion is widespread in reality. Identifying and measuring congestion can help decision-makers detect the problem of overinvestment. This paper reviews the development of the concept of congestion in the framework of data envelopment analysis (DEA), which is a widely accepted method for identifying and measuring congestion. In this paper, six main congestion identification and measurement methods are analysed through several numerical examples. We investigate the ideas of these methods, the contributions compared with the previous methods, and the existing shortcomings. Based on our analysis, we conclude that existing congestion identification and measurement methods are still inadequate. Three problems are anticipated for further study: maintaining the consistency between congestion and overinvestment, considering joint weak disposability assumption between desirable outputs and undesirable outputs, and quantifying the degree of congestion.

The advent of distributed energy resources is undoubtedly transforming the nature of the electric power system. The crisis of conventional energy sources and their environmental effects resulted in the integration of Distributed Generators (DGs) into the distribution system. Simultaneous application of optimum network reconfiguration, DGs, and Distribution Static Compensator (DSTATCOM) unit’s placement in the Radial Distribution Systems (RDS) comes with a raft of technical, economic, and environmental benefits. Benefits include improved power quality, reliability, stability, mitigation of power losses, and voltage profile improvement. In this paper, the combinational process of optimal deployment of DGs and DSTATCOM units in RDS with suitable network reconfiguration to achieve positive benefits has been analyzed. A recent metaphor-less based Artificial Intelligence (AI) technique named the Rao-1 method is employed to overcome this combinational nonlinear optimization problem. The objective functions are to mitigate the power losses, enhance the voltage profile, and voltage stability index of the RDS considering the net economic cost-benefit to the distribution utility. The simulation study of this pragmatic approach problem is carried out on IEEE 33-bus RDS. The comparison of the results obtained by the Rao-1 method with other existing meta-heuristic optimization methods has been made to show its efficacy.

Conventional environmental risk assessment of chemicals is based on a calculated risk quotient, representing the ratio of exposure to effects of the chemical, in combination with assessment factors to account for uncertainty. Probabilistic risk assessment approaches can offer more transparency by using probability distributions for exposure and/or effects to account for variability and uncertainty. In this study, a probabilistic approach using Bayesian network modeling is explored as an alternative to traditional risk calculation. Bayesian networks can serve as meta-models that link information from several sources and offer a transparent way of incorporating the required characterization of uncertainty for environmental risk assessment. To this end, a Bayesian network has been developed and parameterized for the pesticides azoxystrobin, metribuzin, and imidacloprid. We illustrate the development from deterministic (traditional) risk calculation, via intermediate versions, to fully probabilistic risk characterization using azoxystrobin as an example. We also demonstrate the seasonal risk calculation for the three pesticides. Integr Environ Assess Manag 2022;18:1072-1087. © 2021 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental ToxicologyChemistry (SETAC).

“First-last mile” (FLM) is an informal composed-word, widely used to indicate the first and the last leg of each transport movement. In this paper, it is defined as a complex system characterized by three interconnected macro factors that determine the success of a transport planning system. This is a very challenging part of the transport system from a planning perspective because of the critical issues that make it not much reliable and cost-effective. This paper capitalizes on the experiences gained in different implementations/studies and provides a theoretical framework for a good assessment and planning of the first-last mile. It stems from the recognition that the literature proposes different solutions and methods, ranging from the promotion of green transport to the use of ITS technologies. For this reason, this paper aims at elaborating some guidelines on how to tackle the problem in different territorial contexts, trying to be a help to the planners to reduce the negative consequences of mobility. Five main phases are identified here: 1) First-last mile identification; 2) Schematization of the problem and cost analysis; 3) Definition of the stakeholder involvement in the process; 4) Identification of the critical aspects of the process; 5) Identification of the best strategies to reduce costs. For each phase, the indicators to be taken into account for a correct transport planning are discussed, as a first step for stakeholders and policy-makers for making themselves aware of the significant potential of this issue.

Carrying out complex projects often involves several collaborating parties (agents) with conflicting goals. We consider project scheduling problems, where each activity belongs to one of several agents, with a given deadline on the project completion while one aims at a schedule with an efficient use of resources. As there are precedence relations among activities and the activity execution requires varying amounts of resources, the need for coordination among the agents arises. For the execution of activities, an agent can choose from several modes that determine the processing time and resource usage. Each agent aims to minimise his/her individual resource costs associated with the project. Hence, the problem at hand is the multi-agent generalisation of the multi-mode resource investment problem. Here, we consider local as well as global resources. The latter ones are shared among the agents, thus the problem involves the need for a suitable allocation of respective resource costs. As agents are unwilling to share critical and sensitive information (or are not expected to always provide truthful information), the employed solution procedure should not rely on such information under consideration of the agents’ incentives. We propose and extend such decentralised negotiation mechanisms which facilitate the allocation of global resources. We analyse their potential to overcome information asymmetry and yield high quality solutions utilising a distributed scheduling procedure and a representation which aids in learning effective mode decisions.

It is expected that commercial use of drones in the near future will involve delivery service operations by e-commerce companies. We consider relevant strategic and tactical decisions that these retailers will face in drone-based delivery operations, and derive policies on when to offer drone delivery, what delivery capacity to maintain, and what amount to charge for such deliveries. To this end, we develop a Markov decision process (MDP) framework, and introduce two heuristic procedures, through which near-optimal closed-form solutions can be obtained. The results are aimed at helping online retailers to determine in real time whether and to what extent to offer drone-based delivery for given product categories in different service zones. In addition, we study delivery fee structures and identify drone-based delivery pricing strategies under two widely used delivery pricing schemes. For capacity planning decisions, we describe an algorithm to identify the fleet size to utilize to fulfill uncertain demand in a given service region. We also identify structural characteristics on how these decisions and the expected profit are affected by changes in various problem parameters, which can generate generic insights on drone-based delivery operations for e-commerce companies. We find that retailers should prioritize more profitable items when allocating drone delivery capacity, and invest in adding more drones when per order opportunity costs are higher and promised delivery time thresholds are shorter. Retailers can potentially boost their net profits by increasing the effective promised delivery time threshold and/or decreasing the effective delivery delay costs and per order opportunity costs.

The recent increase in Renewable Generation (RG) has prompted many states, utilities, and other stakeholders to improve the methods used to determine the value of RG in efforts of replacing the Net Metering approach. However, these studies result in a wide range of values. This paper proposes a methodology based on the RG valuation studies conducted in recent years. The method includes the most common cost and benefit components considered in these studies and adopts a comprehensive method to calculate each component. The main categories include avoided energy, avoided generation capacity, avoided transmission capacity, avoided system losses, price hedging benefits, environmental benefits, and grid integration costs. A realistic case study emulating a large utility is also conducted to illustrate the application of the proposed method. The results show that all the main components can be estimated based on detailed system models or simulations. The results also illustrate some of the data challenges associated with such a study.

Innovative solutions targeting improvements in the behavior of energy consumers will be required to achieve desired efficiency in the use of energy. Among other measures for stimulating consumers’ behavior changes based on attention triggers, personalized recommendations are essential to enhance sustainable progress towards energy efficiency. In light of this challenge, the current study focuses on innovative energy services that are based on intelligent recommendation systems and digital twins. We review several trends associated with the modeling and diffusion of energy services, taking into account the positive interrelationships existing between recommendation provisions and demand-side consumer energy behavior. This is achieved by means of a content analysis of the state-of-the-art works, focusing on the IEEE Xplore and Scopus databases. Based on this review, we present new empirical evidence to validate data-driven twin technologies as novel ways of implementing consumer-oriented demand-side management via sophisticated abstraction of consumers energy behaviors, and identify various barriers associated with the adoption of energy services, especially as they relate to the implementation and overall adoption of the digital-twins concept. Lastly, we use the review to summarize a coherent policy recommendations related to the wide-spread adoption of the digital-twins concept, and demand-side management solutions in general.

New regulations allow peer-to-peer energy trading worldwide, empowering users while recognizing the prosumer as a critical stakeholder. In recent years, microgrids have surfaced as a distributed and embedded energy production alternative. This literature review analyses agent-based model systems with distinct approaches that model, control, and supervise microgrids, establishing a viewpoint for the transition to renewable energy, which engulfs energy security, and technology adoption. Procurement of microgrids utilizing energy transactions between end-users and implementing agent-based models has derived a better understanding of local and micro-energy markets. This literature review explores community microgrids deployment’s social dynamics and technical performance from a micro point of view. Finally, we present a roadmap for long and short-term use of agent-based models to access energy security via local energy markets, energy storage systems and community-based microgrids in rural areas. The results of this comprehensive review give way to further research questions on the use of agent-based models to improve the adoption of energy storage systems, the use of technologies to accelerate the creation of local energy markets, and the successful deployment of community-based microgrids.

For a long time, research into integrated deterministic-probabilistic safety assessment has been continuously conducted to point out and overcome the limitations of classical ET (event tree)/FT (fault tree) based PSA (probabilistic safety assessment). The current paper also attempts to assert the reason why a technical transformation from classical PSA is necessary with a re-interpretation of the categories of risk. In this study, residual risk was classified into interpolating- and extrapolating-censored categories, which represent risks that are difficult to identify through an interpolation or extrapolation of representative scenarios due to potential nonlinearity between hardware and human behaviors intertwined in time and space. The authors hypothesize that such risk can be dealt with only if the classical ETs/FTs are freely relocated, entailing large-scale computation associated with physical models. The functional elements that are favorable to find residual risk were inferred from previous studies. The authors then introduce their under-development enabling techniques, namely DICE (Dynamic Integrated Consequence Evaluation) and DeBATE (Deep learning–Based Accident Trend Estimation). This work can be considered as a preliminary initiative to find the bridging points between deterministic and probabilistic assessments on the pillars of big data technology.

This study aims to identify appropriate innovative strategies for solar energy investments for both commercial and non-commercial users. For this purpose, TRIZ-based 8 different innovative strategies are identified. Later, these strategies are weighted with the interval type-2 fuzzy DEMATEL (IT2 FDEMATEL) method to define more important strategies. Thus, the main motivation of this study is to figure out the weights of the strategies for solar energy investments The findings indicate that the replacement of mechanical system is found as the most effective TRIZ-based investment strategy for solar energy projects regarding both commercial and non-commercial customers. Thus, it is recommended that solar energy companies should give special support to their customers with respect to the security issues. Moreover, local quality is another significant strategy for commercial users. Therefore, it will be appropriate to provide special services according to the characteristics of the customers. Within this framework, having distribution centers close to customers and employing customer representatives who know well the culture of that region can be very helpful. Additionally, cushion in advance is found as another appropriate TRIZ-based innovative strategy for commercial customers. Hence, it would be appropriate to ensure the security in solar panels. Within this context, necessary measures should be taken for problems such as electric shock, material destruction, theft, and risk of destroying data on the computer.

During nuclear facility decommissioning, workers are continuously exposed to high-level radiation. Hence, adequate path planning is critical to protect workers from unnecessary radiation exposure. This work discusses recent development in radioactive path planning and the algorithms recommended for the task. Specifically, we review the conventional methods for nuclear decommissioning path planning, analyze the techniques utilized in developing algorithms, and enumerate the decision factors that should be considered to optimize path planning algorithms. As a major contribution, we present the quantitative performance comparison of different algorithms utilized in solving path planning problems in nuclear decommissioning and highlight their merits and drawbacks. Also, we discuss techniques and critical consideration necessary for efficient application of robots and robotic path planning algorithms in nuclear facility decommissioning. Moreover, we analyze the influence of obstacles and the environmental/radioactive source dynamics on algorithms' efficiency. Finally, we recommend future research focus and highlight critical improvements required for the existing approaches towards a safer and cost-effective nuclear-decommissioning project.

Energy is one of the most important social and economic infrastructures and human life is strongly depends on it. In recent years, the occurrence of natural disasters such as drought as a high-impact low-probability event is undeniable. So, energy planning in the disruption situation is necessary in order to anticipate and maintain the performance of the energy system in optimal condition. This paper focuses on developing a hybrid long-term framework for evaluating the energy system resilience based on sustainability concept against drought. The model bridges the gap between long-term energy system planning in macro level and short-term switching operations or micro level modeling​ approach in response to drought resulted by climate change. The new resilience indexes are used to maximize the social welfare, to minimize the energy needed but not supplied (ENNS), to recover the system performance rapidly and minimum the total cost of the energy system after occurring drought. When disaster happens, the modeling of energy systems will do through two stages because the behavior of the system should be considered, compare and analyzed before and after disruption events. On the other hand, the system uncertainties, possible futures and different strategies can change the optimal path of the energy needed supply, which is examined by scenario analysis in the proposed model. So, the developed model is a two-stage optimization model with linear programming approach and scenario analyzing model with a combination of back-casting and forward future approaches Numerical model is performed on UTOPIA energy system and its results demonstrate the effectiveness of the proposed method.

The Sendai Framework for Disaster Risk Reduction 2015−2030’s (SFDRR) framing moved away from disaster risk as a natural phenomenon to the examination of the inequality and injustice at the root of human vulnerability to hazards and disasters. Yet, its achievements have not seriously challenged the long-established capitalist systems of oppression that hinder the development leading to disaster risk creation. This article is an exploratory mapping exercise of and a collective reflection on Sustainable Development Goals (SDGs) and SFDRR indicators—and their use in measuring progress towards disaster risk reduction (DRR). We highlight that despite the rhetoric of vulnerability, the measurement of progress towards DRR remains event/hazard-centric. We argue that the measurement of disaster risk could be greatly enhanced by the integration of development data in future iterations of global DRR frameworks for action.

BACKGROUND: Nowadays, smart healthcare minimizes medical facilities costs, ease staff burden, achieve unified control of materials and records, and enhance patients’ medical experience. Smart healthcare treatments have critical barriers to improving patient outcomes, reducing the regulatory burden, and promoting the transition from volume to benefit. OBJECTIVE: In this paper, the Internet of Things-assisted Intelligent Monitoring Model (IoT-IMM) has been proposed to improve patient health and maintain health records. METHOD: The advanced IoT sensors can monitor patient health and insert into the patients’ bodies. Information collected can be analyzed, aggregated, and mined to predict diseases at an early stage. For that, an enhanced deep learning network using Bayes theorem (EDLN-BT) benefits to obtain and verify various patient health data in a specific aspect, making it easy to supervise the patient’s activities. RESULTS: The IoT-IMM-based EDLN-BT results show the smart health care monitoring has undergone substantial growth, improving patient satisfaction for the quality of the healthcare services offered in hospitals and many other healthcare facilities. It helps predict health diseases with increased accuracy, prediction rate with minimal residual error delay, and energy consumption.

An optimal flood-limiting water level (FLWL) is needed to solve the contradiction between flood control safety measures and utilizable benefits. Therefore, this paper took the Chengbi River reservoir as an example, applied the Monte Carlo stochastic simulation and water consumption rate per unit of electrical energy methods to calculate the flood control risks and benefits associated with raising the FLWL in stages, and used the fuzzy comprehensive evaluation method to select the optimal FLWL for each stage of the flood season, which provided a scientific basis for the effective use of rainfall and flood data in the reservoir. The major outcomes of the study are as follows. (1) The reservoir flood season can be divided into a preflood season (April to May), a main flood season (June to August), and a later flood season (September to October). (2) The flood risks and power generation benefits of the reservoir are calculated after raising the FLWL, the optimal FLWL for the main flood season is 185.5 m, and the optimal FLWL for the later flood season is 187.50 m, while the FLWL in the preflood season should be kept at 185.0 m. The above results can provide scientific support for flood control safety planning and the optimal operation of reservoirs. HIGHLIGHTS Flood risks are calculated for each staged FLWL scheme for the Chengbi River reservoir.; Using power generation benefits as an indicator, the utilizable benefits are calculated for each staged FLWL scheme.; A comprehensive evaluation of the flood risks and power generation benefits of each scheme is carried out to obtain the optimal FLWL scheme for each stage.

As the wind farm sector grows and becomes an established renewable energy source, it introduces new materials, technologies and processes that expose workers to increased and unique occupational risks. In this paper, we performed a generic review of scientific and industry literature on online scientific databases and search engines to identify the extent to which occupational health hazards and risks specific to wind farms have been considered. Our review revealed noise, electromagnetic fields, shadow flicker, epoxy and styrene and physical stress have been the focus of limited research, mainly including self-reported data from offshore wind farm employees. Factors such as vibration, welding fumes and other possibly harmful substances, weather conditions and biological hazards have not been addressed by studies, although their presence and combinations could be of concern. Therefore, there is a need for further research on unique and combined risks and hazards faced by workers across all lifecycle stages of wind energy production. This would improve knowledge and provide the opportunity to manage health hazards in current and newly constructed installations and inform future regulatory and other preventative measures.

The long-term quality assurance of bridges demands an adequate quality control plan detailing the main required activities and assessment tools. This paper presents the application of a quality control plan on two existing reinforced concrete bridges located in Portugal and North Macedonia. Based on the available results from visual inspections and non-destructive tests, defects and corresponding damage processes were identified. The structural safety of the case studies was assessed by means of a reliability index considering the identified damages. The reliability index computation was supported by a nonlinear finite element analysis. The computed reliability index that reflects the actual (‘today’) bridge condition was used as the base information in the preparation of possible maintenance scenarios. Three different maintenance scenarios were considered, namely, the improvement (corrective scenario), reduction (do nothing and rebuilt scenario), or delay in the reduction (preventative scenario) of the performance of the structure. By comparison of normalised net present values of the key performance indicators (reliability, safety, availability and costs), the most appropriate scenarios are found., This work was partly financed by: (i) national funds through FCT - Foundation for Science and Technology, under grant agreement ‘PD/BD/143142/ 2019’ and ‘PD/BD/143003/2018’ attributed to the 1st and 2ndauthor, respectively; and (ii) FCT/MCTES through national funds (PIDDAC) under the R&D Unit Institute for Sustainability and Innovation in Structural Engineering (ISISE), under reference UIDB/04029/2020.

In 2020, under the major impact of the global COVID-19 epidemic, people are paying more and more attention to safety issues including health and personal safety, and relevant supervision and testing methods are also constantly updated across different countries. In industrial enterprises, both spread of diseases and occurrence of accidents are largely caused by unsafe state of things and unsafe behaviors of people, among which, human factor is the most important factor. In recent years, many scholars have conducted theoretical and practical research on individual behavior from the perspective of individual psychological characteristics. Where, individual initiative as an important individual psychological trait is increasingly incorporated in the research category of safety behavior. A close correlation exists between individual initiative and evolution of safety production behavior. According to constraint conditions and replicator dynamics equation, this paper uses evolutionary game method and computer fuzzy system Matlab simulation software to conduct numerical experiment analysis on the ideal state of the game between organizations and individuals, thereby studying the behavior evolution trend. The basic idea of fuzzy control is to use computer to realize human's qualitative control experience. Research is found that whether an individual adopts safety obedience behavior will be directly affected by whether the organization adopts regulatory safety production management model. And if the organization adopts regulatory safety production management model and the individual does not implement safety obedience behavior, it is impossible to achieve a stable state. The evolution process of the two to the ideal state is affected by multiple factors. © 2021 - IOS Press. All rights reserved.

This letter investigates how opinion evolution in society regarding public health concerns during an epidemic hinders its progression. A multiplex bilayer network is considered in the study where the disease is transmitted through a directed physical interaction network layer while the opinions are formed over a directed social network layer. Towards that end, an opinion dynamical model is considered over the social network which incorporates people’s current opinion-dependent susceptibilities to considering the cautious measures. The disease transmission, on the other hand, is captured by network generalized Susceptible-Exposed-Infected-Vigilant (SEIV) epidemic model which incorporates vigilant attitude in the society. Stability analysis of the disease-free equilibrium (DFE) of the network transmission model is carried out, and sufficient condition for exponential stability of the DFE is derived. The simulation results corroborate the effectiveness of the theoretical findings.