Uncertain Scenarios Research Articles

Minimum spanning tree hierarchical clustering algorithm: A new Pythagorean fuzzy similarity measure for the analysis of functional brain networks

Clustering structures are one of the most important aspects of complex networks. Minimum spanning tree (MST), the tree that connects all vertices with minimum total weight, can be considered as a fundamental unit of original weighted graphs. There are different types of algorithms that identify clusters in a network, but the existing theories and algorithms for searching trees have not been investigated for uncertain scenarios. This paper tackles the situations where network parameters may be uncertain. Rather, we permit the parameters to take the form of Pythagorean fuzzy numbers (PFNs). Moreover, to represent qualitative aspects of uncertainty, the use of linguistic variables (LVs) has effective means for experts in expressing their views. The current study proposes a graph theory-based agglomerative hierarchical clustering technique for Pythagorean fuzzy sets. We first define generalized Pythagorean fuzzy numbers (GPFNs) and LR-type PFNs. Then we compute the PF distance between two GPFNs and also LR-type PFNs, and formulate the expressions for PF similarity measure. This paper mainly examines the use of PF distance and similarity measures in a minimum spanning tree agglomerative hierarchical clustering method by considering PFLVs. Since the structural and functional systems of brain are characterized by complex networks, we apply the proposed algorithm on a functional brain network to prove its practicality and efficiency. We discuss the hierarchical clustering consequences of the proposed algorithm in the shape of a dendrogram. Finally, we compare the clustering results obtained by different similarity measures.

Geopolitics and Financial Profitability, the Big Pharmaceutical Corporations

The pandemic evidenced the struggle for financial profitability of Big Pharmaceutical Corporations, inequality between countries, and within nations, resulting from more than fifty years of stabilization policies, where monetary and fiscal policies have subsumed themselves to the institutional investors’ interests. In the face of lockdown measures and the attempt to return to “normality,” central banks, as lenders of last resort, abandoned financial restriction policies to give way to fiscal policies and promoted a soft readjustment through credit expansion and accelerated public debt looking to recover economic growth in a “fast track.” Proof of this is the worldwide recovery of GDP growth rates but, despite having a rapid recovery, they showed an uncertain scenario in the short and medium terms, accompanied by financial exuberance in stock market indicators at the international level. The focus of this paper will be on demonstrating the profits of pharmaceutical companies, a business that, in the long term, intensified due to the new mutations that SARS-CoV-2 has had during the current situation. It is also important the unequal vaccine access by country and by region. Concluding with a reflection on the struggle between an economy for life and financialized economy.

A Frequency Security Constrained Scheduling Approach Considering Wind Farm Providing Frequency Support and Reserve

The proposal of the “carbon neutrality” target promotes the rapid development of variable renewable energy, which leads to the reduction of inertia and the deficiency of flexible reserve in power systems. Wind turbines (WTs) now are asked for providing frequency support and reserve through proper control strategy. This proposes a new issue on how to coordinate the operation of conventional units and WTs. This paper proposes a frequency security constrained scheduling approach considering wind farms providing frequency support and reserve. Two dynamic frequency indices: RoCoF and nadir, are derived and introduced into the scheduling model. The frequency support capability of wind farm is accurately quantified by considering the real grid-connected WT capacity and wake effect. A wind power reserve model considering uncertainty is established by linking the reserve under the forecasted scenario and uncertain scenarios through reserve auxiliary variables. The contribution of wind farms to power system frequency support and reserve is quantified in the robust scheduling model. Case studies show that the frequency support and reserve from wind farms can effectively reduce the whole operation cost and VRE curtailment. Moreover, the value of wind farms in CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> emission reduction is properly quantified.

Evolution and Professional Revolution for Accounting Professionals

Abstract The role of the professional accountant has evolved with the development of accounting science. During this process, the professional accountant should not limit his training to only university courses, as his performance is linked to the continuous development of his skills and knowledge in the field. The current economic context outlines the necessity for a new type of professional accountant, one who is constantly up to date with legislative changes, always ready to implement aid schemes offered by the state, always able to support the business environment affected by the crisis. Considering all these aspects, the objectives of this article are directed towards answering the following questions: Can professional development and coordination by professional bodies prepare the accounting profession for the future? What are the conditions for professional evolution and professional revolution, considering extreme changes? Does personal responsibility for lifelong learning and career development represent necessary conditions for professional evolution? How can the professional evolution of the accountant be supported by professional associations? The methodology of the research is based on approaching the problem both theoretically and practically by studying the literature and analysing the legislation and the application of international standards in the field. A possible result of this article would be the estimation of the extent to which a coordinated professional development should be based on professional training, which triggers the challenges that professional accountants must face, and how professional bodies could intervene in the processes by updating the regulations, determining, thus, successful coordination within uncertain future scenarios.

A two-stage stochastic programming model for selective maintenance optimization

The problem of selective maintenance exists in many multicomponent systems carrying out an alternating sequence of missions, with scheduled breaks where only a limited number of components can be maintained due to time limit. In this paper, a stochastic programming approach is proposed for determining an optimum maintenance plan to minimize maintenance costs and expected failure costs, while maximizing the probability of successful accomplishment of the next mission under uncertainties in future operating conditions. Traditionally, future operating conditions that affect failure time distribution when calculating reliability in selective maintenance models were assumed as deterministic. In this study, future operating conditions are assumed to be uncertain. The system is subject to several uncertain condition scenarios of exposure, conditional, usage, stress, etc. Each scenario is modeled with its associated occurrence probability. The presented model is a two-stage stochastic mixed-integer nonlinear programming model with fixed recourse, where the first stage is associated with maintenance decisions made before uncertainties are revealed, and the second stage is modeled as a recourse function which is related to the occurrence probability of system failure. A numerical example of a series-parallel system is used to demonstrate the effectiveness of the suggested model.

Distributed Grouping Cooperative Dynamic Task Assignment Method of UAV Swarm

Aiming at the problem of UAV swarms with distributed subsets performing cooperative reconnaissance-and-attack tasks on multi-targets in complex and uncertain combat scenarios, a distributed grouping cooperative dynamic task assignment method is proposed based on extended contract network protocol. The dynamic task assignment model for the UAV swarm with the topology of distributed subsets is established considering multiple constraints such as task cooperation, performing sequence, dynamic environment, communication topology, payload model, and UAV capability. According to the characteristics of multi-participants and multi-tasks in the process of UAV swarm executing tasks, the determination mechanism on cooperators and the selection mechanism of sequential tasks are proposed, and then the contract network protocol is extended. On the basis of the above, an event-triggered task assignment strategy for dynamic tasks is designed. The simulated results show that the proposed method can achieve the cooperative dynamic assignment of the UAV swarm to perform reconnaissance-and-attack tasks to multi-targets in complex and uncertain combat scenarios, improve the adaptiveness of the swarm under the sudden circumstance, and realize the optimization for task execution efficiency of the UAV swarm.

Recoverable robust representatives selection problems with discrete budgeted uncertainty

Recoverable robust optimization is a multi-stage approach, in which it is possible to adjust a first-stage solution after the uncertain cost scenario is revealed. We analyze this approach for a class of selection problems. The aim is to choose a fixed number of items from several disjoint sets, such that the worst-case costs after taking a recovery action are as small as possible. The uncertainty is modeled as a discrete budgeted set, where the adversary can increase the costs of a fixed number of items. While special cases of this problem have been studied before, its complexity has remained open. In this work we make several contributions towards closing this gap. We show that the problem is NP-hard and identify a special case that remains solvable in polynomial time. We provide a compact mixed-integer programming formulation and two additional extended formulations. Finally, computational results are provided that compare the efficiency of different exact solution approaches.

ES-DQN: A Learning Method for Vehicle Intelligent Speed Control Strategy Under Uncertain Cut-In Scenario

Uncertain cut-in maneuver of vehicles from adjacent lanes makes it difficult for vehicle&#x0027;s automatic speed control strategy to make judgments and effective control decisions. In this paper, an intelligent speed control strategy for uncertain cut-in scenarios is established based on a basic autonomous driving system. This strategy judges cut-in maneuver from surrounding vehicles and outputs adaptive control action under current environment according to Q value of state-action pair based on a Q network. In addition, according to the analysis of cut-in scenarios, the Q network is trained based on a novel reinforcement learning method named as experience screening deep Q-learning network (ES-DQN). The proposed ES-DQN is an extension of double deep Q-learning network (DDQN) algorithm, and includes two parts: experience screening and policy learning. Based on the experience screened from the experience screening part, the proposed learning method can train an intelligent speed control strategy which has stronger adaptability and control effect in uncertain cut-in scenarios. According to simulation results, the proposed intelligent speed control strategy trained by ES-DQN has better performance under uncertain cut-in scenarios than DDQN method and traditional ACC strategy. Meanwhile, by adjusting weight value in reward function, the system can realize different control target.

Surrogate-assisted population based ACO for resource constrained job scheduling with uncertainty

In mining supply chains, transporting ore from mines to ports, is a problem of significant interest. Resources required in transporting ore are limited, and hence using resource efficiently goes a long way towards solving this problem. A key issue is that the resource availability is subject to uncertainties. Previous studies have considered similar (deterministic) problems, commonly tackled as resource constrained job scheduling. However, the uncertainty associated with resources needed to transport ore has not been considered. This study extends previous ones, by investigating resource constrained job scheduling with uncertainty (considering resource availabilities). The focus is on robust optimisation, where the aim is to find high-quality solutions irrespective of the input data. To solve this problem a population-based ant colony optimisation approach is devised, with the aim of identifying high-quality solutions across several uncertain scenarios. Moreover, computing the objective is inefficient due to uncertainty, and hence surrogate models are used. Experiments are conducted on a wide range of problem instances with varying uncertainty levels considering resources, and find that population-based ant colony optimisation is superior to ant colony optimisation on its own. The key advantage of this method is that it is able to maintain a population of excellent solutions, which can be used to efficiently approximate the objective values of new solutions, and hence update the pheromones globally of the ant colony optimisation algorithm. Using this information the surrogate model considers only high quality solutions, and by carrying out increased learning in short time-frames, achieves excellent results.

Assessing behavioral intention toward green hotels during COVID-19 pandemic: the moderating role of environmental concern

PurposeThe study aims to investigate the consumers' behavioral intention toward green hotels. The tendency of individuals to afford green hotels is further escalating with progressing coronavirus disease-2019 (COVID-19) pandemic recurring waves. The increased worry of consumers toward health, hygiene and the climate is acquiring momentum and transforming how consumers traditionally perceive green hotels.Design/methodology/approachThe study has recommended an integrated framework incorporating various research fields as attitude-behavior-context theory, theory of planned behavior (TPB) and moderating influences to study the associations among the antecedents of consumers' behavioral intention toward green hotels. The study comprised the participation of 536 respondents residing in the Delhi and National Capital Region (NCR) of India. The data analysis strategy involved the use of structural equation modeling (SEM) analysis to test the proposed research framework.FindingsThe results and findings of the study indicated a significant influence of fear and uncertainty of the COVID-19 pandemic and environmental concern on green trust. The results also revealed the considerable impact of green trust on willingness to pay premium, attitude and subjective norms, which significantly influenced behavioral intention. The analysis also revealed the moderating influence of environmental concern in the relationship of green trust and behavioral intention.Research limitations/implicationsThe study has recommended significant theoretical. The theorists may use this research framework to analyze better the transforming consumer behavior trends toward green hotels in the ongoing fearful and uncertain COVID-19 pandemic scenario.Practical implicationsThe study has recommended significant managerial implications. The industry practitioners may also utilize the framework to sustain the hotel business and bring new strategic insights into practice to combat the impact of the pandemic and simultaneously win consumers' trust in green hotels.Originality/valueAlthough the researchers have previously emphasized consumers' intention toward green practices embraced by hotels, the impact of the COVID-19 pandemic on the green hotel industry gained noticeable attention from researchers. Furthermore, there is a scarcity of literature providing insights on the behavioral dynamism of hotel customers' trust, attitude and willingness to pay for green hotels during the repetitive waves of the COVID-19 pandemic. The study will support the existing literature gap by enlightening the associations among the various antecedents of green hotels' behavioral intention, COVID-19 and environmental concern.

Stratified hybrid decision model with constrained attributes: Recycling facility location for urban healthcare plastic waste

• Recycling facility location selection for urban healthcare plastic waste is addressed. • Extensions of CoCoSo and WASPAS methods are presented based on constrained attributes. • Impacts of potential uncertain future events and scenarios are included using stratified BWM model. • A novel hybrid model is developed based on Stratified BWM, CoCoSo, and WASPAS with constrained attributes. • A real-life case study of Istanbul is investigated to show applicability of the proposed model. For efficient management of plastic waste in urban healthcare environment, the development of in-depth solutions considering all possible effective factors is required. Recycling plastic waste of healthcare centers has become an essential process for urban regions considering environmental, economic, and social issues. In this regard, recycling facility location selection is a multi-dimensional decision-making problem that can be addressed with the support of MCDM. Although traditional MCDM techniques have usually focused on benefit and cost attributes, in real-life practices, decision-makers in environmental sectors have shown a high tendency to consider specific constraints on attributes according to real-life standards. To address the recycling location selection problem with a practical solution, this study proposed a novel hybrid constrained decision-making model that integrates the hierarchical stratified best-worst method (H-SBWM) with the constrained combined compromise solution (CoCoSo) and the constrained weighted aggregated sum product assessment (WASPAS) methods, called H-SBWM-C-CoCoPAS. To examine the proposed approach, a case study is investigated for six location candidates in Istanbul under technical and sustainability aspects. The research findings show that the most important criterion for recycling facility location selection is carbon footprint with the weight coefficient of 0.1757. Also, the Pendik district is the best location for opening a recycling center in the Istanbul.

Distributionally robust ramp metering under traffic demand uncertainty

This study presents a distributionally robust optimization model to address the ramp metering problem with uncertain traffic demand flows. The aim of this model is to minimize the total travel delay of the system based on the macroscopic cell transmission model (CTM) of traffic flow. In our model, the only required data is the partial distributional information of stochastic demand flows. Using the Worst-Case Conditional Value-at-Risk (WCVaR) constraints to approximate the distributionally robust chance constraints, the proposed problem can be conservatively approximated as a semidefinite programming (SDP), which is computationally efficient. The performances of our proposed model are illustrated by practical applications. Experimental results show that the distributionally robust control strategy can achieve reliable performances over a range of uncertain scenarios.

Modeling and optimization of bioethanol production planning under hybrid uncertainty: A heuristic multi-stage stochastic programming approach

When making planning decisions, fluctuation in both product demand and yield for the bioethanol plant denotes an important factor to affect the results. In this paper, a mixed multi-stage stochastic optimization model is proposed to solve the production planning problem of a bioethanol plant with multiple raw materials simultaneously considering uncertain demand and yield. The multi-source uncertainty is described as a multi-layer hybrid scenario tree model according to the historical statistics. Due to the diversity of pretreatment approach in the bioethanol production process, the yield of saccharification liquid is different. At each node of the scenario tree, the uncertainty of demand is expressed by probability. The uncertain scenarios of the yield are integrated at each node of demand to form a mixed scenario tree. To improve the solution efficiency, a heuristic algorithm based on fast forward selection is designed to simplify the decomposition of the large-scale hybrid scenario tree in the mixed multi-stage stochastic programming problem. The effectiveness of the proposed model is verified by a practical case. The results show that the proposed approach performs better than the two-stage stochastic programming and the deterministic approaches, and the algorithm can significantly reduce the computational efforts.

Disentangling sources of future uncertainties for water management in sub-Saharan river basins

Abstract. Water management in sub-Saharan African river basins is challenged by an uncertain future climatic, social and economical patterns potentially causing diverging water demands and availability, and by multi-stakeholder dynamics, resulting in evolving conflicts and tradeoffs. In such contexts, a better understanding of the sensitivity of water management to the different sources of uncertainty can support policymakers in identifying robust water supply policies balancing optimality and low vulnerability against likely adverse future conditions. This paper contributes an integrated decision-analytic framework combining an optimization, robustness, sensitivity, and uncertainty analysis to retrieve the main sources of vulnerability to optimal and robust reservoir operating policies across multi-dimensional objective spaces. We demonstrate our approach on the lower Umbeluzi river basin, Mozambique, which an archetypal example of sub-Saharan river basin, where surface water scarcity compounded by substantial climatic variability, uncontrolled urbanization rate, and agricultural expansion are hampering the Pequenos Libombos dam's ability to supply the agricultural, energy, and urban sectors. We adopt an Evolutionary Multi-Objective Direct Policy Search (EMODPS) optimization approach for designing optimal operating policies, whose robustness against social, agricultural, infrastructural, and climatic uncertainties is assessed via robustness analysis. We then implement the generalized likelihood uncertainty estimation (GLUE) and PAWN uncertainty and sensitivity analysis methods for disentangling the main challenges to the sustainability of the operating policies and quantifying their impacts on the urban, agricultural, and energy sectors. Numerical results highlight the importance of a robustness analysis when dealing with uncertain scenarios, with optimal non-robust reservoir operating policies largely being dominated by robust control strategies across all stakeholders. Furthermore, while robust policies are usually vulnerable only to hydrological perturbations and are able to sustain the majority of population growth and agricultural expansion scenarios, non-robust policies are sensitive also to social and agricultural changes and require structural interventions to ensure stable supply.

Travel Bubbles in Air Transportation: Myth or Reality?

Aviation has been hit hard by COVID-19, with passengers stranded in remote destinations, airlines filing for bankruptcy, and uncertain demand scenarios for the future. Travel bubbles are discussed as one possible solution, meaning countries which have successfully constrained the spread of COVID-19 gradually increase their mutual international flights, returning to a degree of normality. This study aims to answer the question of whether travel bubbles are indeed observable in flight data for the year 2020. We take the year 2019 as reference and then search for anomalies in countries’ flight bans and recoveries, which could possibly be explained by having successfully implemented a travel bubble. To the best of our knowledge, this study is the first to try to address the identification of COVID-19 travel bubbles in real data. Our methodology and findings lead to several important insights regarding policy making, problems associated with the concept of travel bubbles, and raise interesting avenues for future research.

Adaptive Fault-Tolerant Control of a Hybrid VTOL UAV against Actuator Faults and Model Uncertainties under Fixed-Wing Mode

This paper proposes an adaptive fault-tolerant control strategy for a hybrid vertical take-off and landing (VTOL) unmanned aerial vehicle (UAV) to simultaneously compensate actuator faults and model uncertainties. With the proposed adaptive control schemes, both actuator faults and model uncertainties can be accommodated without the knowledge of fault information and uncertainty bounds. The proposed control scheme is constructed with two separate control modules. The low-level control allocation module is used to distribute the virtual control signals among the available redundant actuators. The high-level control module is constructed with an adaptive sliding mode controller, which is employed to maintain the overall system tracking performance in both faulty and uncertain conditions. In the case of actuator faults and model uncertainties, the adaptive scheme will be triggered to generate more virtual control signals to compensate the virtual control error and maintain the desired system tracking performance. The effectiveness of the proposed control strategy is validated through comparative simulation tests under different faulty and uncertain scenarios.

Optimal strategy for remanufacturing system of sustainable products with trade credit under uncertain scenario

The manufacturing of commodities using cost-effective strategies that decrease adverse environmental effects while conserving raw materials and energy is based on sustainable manufacturing. Through sustainable production, employees, the community, and product safety are all improved. Carbon emissions are also considered as another factor for developing a sustainable strategy during the production process. To develop a policy for the production period for both the manufacturer and the remanufacturer under a fuzzy environment present study, a three-layer fuzzy sustainable manufacturing model has been established, with the producer, remanufacturer, and retailer as contributors, and the effects of economic and environmental factors were addressed. The aim of the article is to minimize overall cost by developing a manufacturing model that takes into account trade-credit policies, product recycling, and carbon emission effects. By managing the cycle time and a fraction of the time for a production chain, this model may assist with making selections to boost the effectiveness of sustainable inventory management. The input parameters (manufacturing cost, remanufacturing cost, and retailer cost) are represented as fuzzy trapezoidal numbers, which helps to handle uncertainty in the manufacturing process. The framework is mathematically presented and a sensitivity investigation is conducted to validate it.

A Multiobjective Multiperiod Mixed-Integer Programming Optimization Model for Integrated Scheduling of Supply Chain Under Demand Uncertainty

The problem of integrated scheduling of supply chain has a huge impact on operational efficiency and cost effectiveness. The increasing number of nodes, different time window constraints for customers, and a variety of uncertain scenarios make supply chain scheduling complicated. This research develops a multi-objective multi-period mixed-integer programming optimization model. We consider comprehensively the effect of demand uncertainty, time window constraints, the constraints of node capability, multi-period and sub-period factors. As the conflicting benefit factors, the cost and service level are two optimization objectives. The first objective function aims to minimize the total cost in all periods. The second objective function considers service level through minimizing the material flow of out-of-stock items in all periods to maximize the service level. And the suppliers&#x2019; capacity, the selection of suppliers, manufacturers&#x2019; productivity, the transaction relationship, the sub-period time, the inventory capacity and lead time for delivery are also considered. Then total costs and service levels are normalized, whose sum is the objective function. And the problem is transformed into a multi-period non-linear optimization problem. An improved Mixed Genetic Algorithm is designed to solve the model. Finally, the practicability of the proposed model and algorithm is demonstrated through its application in an electronics supply chain case study. The results indicate that the proposed model and algorithm can provide a promising approach to fulfill a multi-objective multi-period integrated scheduling plan under uncertain demand scenarios.

Assessment of the Solid Waste Disposal Method during COVID-19 Period Using the ELECTRE III Method in an Interval-Valued q-Rung Orthopair Fuzzy Approach

As the quantity of garbage created every day rises, solid waste management has become the world’s most important issue. As a result, improper solid waste disposal and major sanitary issues develop, which are only detected after they have become dangerous. Due to the system’s lockdown during the COVID-19 pandemic, this scenario became much more uncertain. We are at the stage to develop and execute effective waste management procedures, as well as long-term policies and forward-thinking programmes that can work even in the most adverse of scenarios. We incorporate major solid waste (organic and inorganic solid wastes) approaches that actually perform well in normal cases byreducing waste and environmental disasters; however, in such an uncertain scenario like the COVID-19 pandemic, the project automatically allows for a larger number of criteria, all of which are dealt with using fuzzy Multi-Criteria Group Decision Making (MCGDM) methods. The ELECTRE III (ELimination Et Choice Translating REality-III) approach, which is a novel decision-making strategy for determining the best way to dispose and reduce garbage by combining traditional ELECTRE III with an interval-valued q-rung orthopair fuzzy set (IVq-ROFS), is described in detail in this article. To confirm the efficacy of the recommended model, a numerical explanation is provided, as well as sensitivity and comparative analyses. Obviously, the findings encourage decision-makers in authorities to deliberate about the proposals before creating solid waste management policies.

Unmanned Aerial Vehicle Swarm Cooperative Decision-Making for SEAD Mission: A Hierarchical Multiagent Reinforcement Learning Approach

Unmanned aerial vehicle (UAV) swarm cooperative decision-making has attracted increasing attentions because of its low-cost, reusable, and distributed characteristics. However, existing non-learning-based methods rely on small-scale, known scenarios, and cannot solve complex multi-agent cooperation problem in large-scale, uncertain scenarios. This paper proposes a hierarchical multi-agent reinforcement learning (HMARL) method to solve the heterogeneous UAV swarm cooperative decision-making problem for the typical suppression of enemy air defense (SEAD) mission, which is decoupled into two sub-problems, i.e., the higher-level target allocation (TA) sub-problem and the lower-level cooperative attacking (CA) sub-problem. We establish a HMARL agent model, consisting of a multi-agent deep Q network (MADQN) based TA agent and multiple independent asynchronous proximal policy optimization (IAPPO) based CA agents. MADQN-TA agent can dynamically adjust the TA schemes according to the relative position. To encourage exploration and promote learning efficiency, the Metropolis criterion and inter-agent information exchange techniques are introduced. IAPPO-CA agent adopts independent learning paradigm, which can easily scale with the number of agents. Comparative simulation results validate the effectiveness, robustness, and scalability of the proposed method.