Dynamic Decision-making Approach Research Articles

Failure-aware resource provisioning for hybrid computation offloading in cloud-assisted edge computing using gravity reference approach

This paper tackles the challenges of computation offloading in the cloud–edge paradigm. Although many solutions exist for enhancing the server’s computational and communication efficiency, they mainly focus on reducing latency and often neglect the impact of overlapping multi-request processing on scheduling reliability. Additionally, these approaches do not account for the preemptive characteristics of applications running in the VMs that lead to higher energy consumption. We propose a novel hybrid integer multi-objective dynamic decision-making approach enhanced with the gravity reference point method. This method determines the proportion of computations executed on cloud servers versus those handled locally on edge servers. Our hybrid approach leverages the gravitational potential reference point and crowding degrees to improve the characteristics of whale populations, addressing the limitations of the traditional whale algorithm, which depends on individual whales’ varying foraging behaviors influenced by a random probability number. By evaluating the crowding level around the prey, the foraging behavior of individual whales is adjusted to enhance the algorithm’s convergence speed and optimization accuracy, thereby increasing its reliability. The results show that our hybrid computation offloading model significantly improves time latency by 76.45%, energy efficiency by 63.12%, reliability by 82%, quality of service by 83.78%, distributor throughput by 87.31%, asset availability by 73.05%, and guarantee ratio by 89.72% compared to traditional offloading methods.

A dynamic decision-making approach for cabin unlawful interference emergency disposal using dynamic Bayesian network

Disposal of unlawful interference incidents is essential for is crucial for the advancement of aviation security. Effective emergency disposal requires a comprehensive approach that includes the perspectives of airlines, airports, and passengers. In this context, each component of the disposal process can fail randomly. The objective of this research is to optimize emergency disposal decisions to enhance the efficiency of civil aviation operations, reduce accidents, and lower costs. Given the dynamic complexity of unlawful interference incidents, a dynamic fault tree consisting of 26 nodes was constructed to analyze the emergency disposal process. To explore the relationships and priorities of each event, the Dynamic Fault Tree is converted into a dynamic Bayesian network. Based on historical statistical data, simulation analysis is conducted in three aspects: posterior probability, sensitivity, and importance. Simulation results reveal that the top three critical nodes in cabin unlawful interference incidents are “structural damage to the cabin,” “inadequate training by airlines,” and “untimely airport police takeover of disruptive passengers.” Further analysis shows that (1) most of the critical nodes are associated with airlines. (2) The decision-making rationale and pathways of the critical nodes can be clearly observed and prioritized. (3) Besides airlines, other entities such as airports can implement targeted emergency disposal measures. Through quantitative analysis and simulation, this study provides decision-making guidance for participating groups on dynamic emergency disposal, thereby enhancing civil aviation security.

A hybrid approach for optimizing deep excavation safety measures based on Bayesian network and design structure matrix

Considering the dynamic risk factors and risk situation throughout the entire deep excavation operations, timely adjustment and optimization of safety measures can enhance the practicality of construction technical plans on sites. A digital and quantitative model representing the practical risk situation of the deep excavation is urgently required for realizing the prediction, optimization, and control of the actual construction state. Thus, this research aims to propose a real-world-oriented model integrating Bayesian network (BN) and design structure matrix (DSM) for decision-making in safety risk management. First, risk factors were identified, and the BN model was established to evaluate the anti-risk ability of the construction site. Then, a multi-objective safety measure optimization model under specific constraints was established. Particularly, the DSM was adopted to express the control relationship between risk factors and safety measures. Moreover, with genetic algorithms applied, the optimal safety measure set for on-site safety risk management can be obtained. For model validation, a deep excavation project of metro construction in Wuhan, China, was selected as a case study. The hybrid optimization model showed the characters of initiative and timeliness in construction risk management. By providing the timely and optimized combination of safety measures, the dynamic decision-making approach can proactively and effectively improve the risk resistance ability of construction sites.

An Inner Dependence Analysis Dynamic Decision-Making Framework

During the last decade, with the rapid development of information technology, the immense volume of data poses a challenge to decision-makers. We use a combined dynamic decision-making approach based on the analytic hierarchy process (AHP) to select the best supplier. In this paper, we discuss the interaction between criteria that can lead to expanding our proposed dynamic framework to consider the inner dependencies among criteria. The main contributions are: (1) identifying the most important criteria of supplier selection in a steel bar manufacturer in Taiwan; (2) proposing a simple and rapid analysis of the appropriate supplier selection evaluation framework; and (3) using the AHP and transformation matrix to present the inner dependence among the criteria.

On behavioral changes towards sustainability for connected individuals: a dynamic decision-making approach

In the context of sustainable lifestyle it has been observed that, while expressing eco-positive attitudes, individuals often do not act accordingly in their habitual behavior. This gap, termed the “value-action” gap, has been explained in terms of desire to seek social approval or as a consequence of the presence of overriding conflicting goals, associated for instance with material costs. In this work, we study a two-scale networked model for dynamic decision-making in which interacting agents are able to exchange opinions and discuss the different reasons they produce their choices and, in addition, are able to observe the actions of their neighbors in the network and adjust their preferences. Coupling on the two scales leads to a reduced value-action gap, and ultimately to a consensus. A numerical example illustrates the effect that tradeoffs between goals and social pressure have on the behavior of the group.

A Risk-Based Dynamic Decision-Making Approach for Cybersecurity Protection in Industrial Control Systems

Decision-making is a key component of industrial control system (ICS) security. However, due to the stability and real-time requirements of ICSs, current decision-making approaches typically designed for IT systems are not entirely suitable for ICSs. In this paper, with consideration of the characteristics of ICSs, a risk-based multistep dynamic decision-making approach for protecting ICSs is proposed. Following this proposal, multiple models, including multilayer Bayesian network, process model, and attack-defense strategy model are built first. On this basis, a state controller is designed to ensure the safe degradation/upgradation of ICSs. Finally, a game theory-based optimal defense strategy generation approach is presented. To verify the effectiveness of the proposed approach, a simulation on a simplified chemical reactor control system is conducted in MATLAB. The simulation results clearly demonstrate that the proposed dynamic decision-making approach has the ability to generate the optimal defense strategy to minimize system loss.

A Dynamic Decision-Making Approach for Intrusion Response in Industrial Control Systems

Industrial control systems (ICSs) are facing more and more cybersecurity issues, leading to increasingly severe risks in critical infrastructure. To mitigate risks, developing an appropriate security strategy is of paramount importance. However, existing efforts on decision making in ICSs inherit some limitations, such as the lack of consideration of the strategy for securing both cyber and physical domains and a tradeoff between security and system requirements. To overcome these limitations, a decision-making approach is presented in this paper for intrusion response in ICSs. Aiming to determine the optimal security strategy against attacks promptly, it tries to secure the most “dangerous” attack paths and respond to functional failures. In this approach, measures that cover both cyber and physical domains are designed with in-depth analysis of attack propagation. They ensure the completeness of candidate security strategy space. A number of Pareto optimal solutions are determined from the strategy space through multiobjective optimization. The objective is to maximize the objective vector composed of security benefit, system benefit, and state benefit. Then, these solutions are prioritized by using a distance-based evaluation method, which pursues the optimal protection ability by making the objective vector of the selected strategy closest to the ideal one. The effectiveness of the proposed approach is demonstrated with a case study on a simulated process control system.

CRM-BASED DYNAMIC DECISION-MAKING WITH HESITANT FUZZY INFORMATION FOR THE EVALUATION OF RANGELANDS

As one of the important components of global land ecosystem, rangeland ecosystem has important value of ecosystem services. With the degeneration of rangeland in recent years, sustainability within rangeland ecosystem has become an increasingly important issue. The aim of this paper is to develop a novel dynamic decision-making approach based on hesitant fuzzy information to evaluate rangeland sustainability that considers ecological, social and economic aspects. Firstly, a modified satisfaction degree of alternative is presented, based on which a mathematical model for determining the stage weights is constructed. Secondly, the compromise ratio method (CRM), whose basic principle is that the optimal alternative should have the nearest distance from positive ideal solution and the longest distance from negative ideal solution simultaneously, is extended to accommodate hesitant fuzzy environment, and then adopted to tackle the dynamic decision-making with hesitant fuzzy information. Compared with the existing methods, the proposed method can eliminate the impact of attribute magnitude and dimension. Lastly, a numerical example on the evaluation of rangelands is provided to illustrate the practicality and superiority of the proposed method.

Biochemistry Synthesis on a Cyberphysical Digital Microfluidics Platform Under Completion-Time Uncertainties in Fluidic Operations

Cyberphysical digital microfluidics enable the integration of fluidic operations, biochemical reaction-outcome detection, and software-based control in a biochip. However, biochemistry synthesis algorithms and biochip design methods proposed in the literature are oblivious to completion-time uncertainties in fluidic operations, and they do not meet the requirements of the cyberphysical integration in digital microfluidics. We present an operation-interdependency-aware synthesis method that uses frequency scaling and is responsive to uncertainties that are inherent in the completion times of fluidic operations such as mixing and thermal cycling. Using this design approach, we can carry out dynamic online decision-making for the execution of fluidic operations and droplet-routing in response to detector feedback. We use three common laboratorial protocols to demonstrate that, compared to uncertainty-oblivious biochip design, the proposed dynamic decision-making approach is more effective in satisfying realistic physical constraints. As a result, it decreases the likelihood of erroneous reaction outcomes, and it leads to reduced time-to-results, less repetition of reaction steps, and less wastage of precious samples and reagents.

The Concept of Earnout in Merger and Acquisition Transactions

This research paper throws light on assessment on merger and acquisition (M&A) deals based on earnouts. It is often said that M&A transactions involving earnouts possess option-like features, and thus this research paper using a theoretic approach to model the value of such observations made. Moreover, the impact of uncertainty on the optimal timing of M&A using earnouts has been dealt with in this paper. This paper shares a dynamic decision-making approach of the invest-to-learn option which is generated on account of investment made in an acquisition. This research paper also provides the implications of earnouts applied in M&A transactions.It has been found that earnouts provide best hedge to the acquiring companies for minimizing the risk of adverse selection in acquisitions. The reason being earnouts enable an acquiring company resolve the problem of over-valuation and that of non-performance by making part payment contingent on the ex post performance of the target company as well as by retaining target company’s managers respectively. The paper recommends earnouts as a valuable strategy for the acquiring companies in the emerging markets for their future global acquisitions as these companies usually end up overpaying the target companies due to lack of expertise in acquisitions. The paper has tried to fill the void in the existing literature by explicitly analyzing the impact of the different modes of payment on the risk profile of acquiring companies in the post acquisition period.

Assessing the Value of Frost Forecasts to Orchardists: A Dynamic Decision-Making Approach

The methodology of decision analysis is used to investigate the economic value of frost (i.e., minimum temperature) forecasts to orchardists. First, the fruit-frost situation and previous studies of the value of minimum temperature forecasts in this context are described. Then, after a brief overview of decision analysis, a decision-making model for the fruit-frost problem is presented. The model involves identifying the relevant actions and events (or outcomes), specifying the effect of taking protective action, and describing the relationships among temperature, bud loss, and yield loss. A bivariate normal distribution is used to model the relationship between forecast and observed temperatures, thereby characterizing the quality of different types of information. Since the orchardist wants to minimize expenses (or maximize payoffs) over the entire frost-protection season and since current actions and outcomes at any point in the season are related to both previous and future actions and outcomes, the decision-making problem is inherently dynamic in nature. As a result, a class of dynamic models known as Markov decision processes is considered. A computational technique called dynamic programming is used in conjunction with these models to determine the optimal actions and to estimate the value of meteorological information. Some results concerning the value of frost forecasts to orchardists in the Yakima Valley of central Washington are presented for the cases of red delicious apples, bartlett pears, and elberta peaches. Estimates of the parameter values in the Markov decision process are obtained from relevant physical and economic data. Twenty years of National Weather Service forecast and observed temperatures for the Yakima key station are used to estimate the quality of different types of information, including perfect forecasts, current forecasts, and climatological information. The orchardist's optimal actions over the frost-protection season and the expected expenses associated with the use of such information are determined using a dynamic programming algorithm. The value of meteorological information is defined as the difference between the expected expense for the information of interest and the expected expense for climatological information. Over the entire frost-protection season, the value estimates (in 1977 dollars) for current forecasts were $808 per acre for red delicious apples, $492 per acre for bartlett pears, and $270 per acre for elberta peaches. These amounts account for 66, 63, and 47%, respectively, of the economic value associated with decisions based on perfect forecasts. Varying the quality of the minimum temperature forecasts reveals that the relationship between the accuracy and value of such forecasts is nonlinear and that improvements in current forecasts would not be as significant in terms of economic value as were comparable improvements in the past. Several possible extensions of this study of the value of frost forecasts to orchardists are briefly described. Finally, the application of the dynamic model formulated in this paper to other decision-making problems involving the use of meteorological information is mentioned.