Optimal Decision Model Research Articles

Structure and Stress Mechanism of Snti-buckling Support for High-rise Connected Structures

This article explores the characteristics, composition, and design requirements of high-rise intelligent buildings, analyzes their superiority and social benefits, and studies their construction principles and future development trends. It is people-oriented, device-oriented and function-oriented. The original three major structural systems have been increasingly unable to meet new requirements. This paper seeks a new seismic system that is both safe and able to protect the instruments and equipment inside the building, and can be connected to the main structure of the structure for normal operation and economically. After a thorough discussion of the latest proposed seismic design methods and performance-based seismic design, this paper proposes performance-based structural seismic design countermeasures with the characteristics of high-rise intelligent buildings. Determine the target performance according to the owner’s requirements and building functions and investment benefit criteria; then determine the safety indicators of the high-performance intelligent building based on the performance structural design; compare the three optimization decision models for the reliability of the performance-based structural targets; An evaluation model of initial cost, inspection and maintenance costs, and failure loss costs. A reliability optimization decision-making model based on performance and earthquake resistance for a high-rise intelligent building is established. The research results show that the maximum displacement of the model structure in the X direction is 14.8mm and 13.8mm, and the maximum displacement in the Y direction is 13.7mm and 12.9mm, respectively. The maximum displacement of the latter in the X and Y directions is compared with the former. From the perspective of the curve change, the displacement change curve of the anti-buckling support frame is always smaller than the displacement curve of the ordinary support frame.

Performance-Based Structural Seismic Method in High-rise Building Design

The rise of intelligent buildings in China is based on two reasons. One is that with the reform and opening up, China’s national economy has continued to develop rapidly, its overall national strength has been continuously enhanced, and people’s living standards have been improved. People urgently need to improve and enhance their working and living environments. Architecture is just one of the important ways to meet this demand. Performance-based seismic design is based on the investment-benefit criterion as a basic principle, reflecting an important change in the seismic design thinking of modern structures, that is, focusing on structural safety from the past. To develop a comprehensive focus on structural performance, safety and economy. It makes the seismic design transition from macroscopically qualitative goals to concretely quantified multiple goals. Designers can choose the required performance goals, which is conducive to the innovation of building structures, using new structural systems, new technologies, and new materials; it is beneficial to adopt different performance goals and earthquake resistance for different fortification intensity, site conditions, and the importance of the building Measures. It is a change in the design concept and plays a significant role in the design and development of engineering structures. This paper explores the characteristics, composition, and design requirements of high-rise intelligent buildings, analyzes their superiority and social benefits, and studies their architectural principles and future development trends. High-rise intelligent buildings were born to meet the higher requirements of office and living environments. It is a combination of architectural art and information technology. It is the four basic elements of structure, system service and optimal combined system management, providing a safe, efficient, comfortable and convenient built environment. At present, only the uncertainty of the load, the strength of the material, the geometric dimensions and the uncertainty of the calculation model are usually considered. For the artificial uncertainty, the complexity and uncertainty of the economic loss estimation due to the complexity of earthquake damage As a result, it is very difficult to accurately estimate economic losses, especially the estimation of indirect losses. Therefore, the establishment of a performance-based reliability optimization decision-making model for high-rise intelligent buildings requires further research and improvement. To build more detailed and specific models for different types of requirements.

Alerting about possible risks vs. blocking risky choices: A quantitative model and its empirical evaluation

Alerting users about possible threats or blocking users’ ability to perform potentially dangerous actions are two common ways to protect systems from the adverse effects of threats, such as malicious email attachments, fraudulent requests, or system malfunctions. We present a normative model of the effects of alerting and blocking on the value of the outcomes, on measures of risk-taking, and on the number of successful attacks. We compared warning and blocking systems and binary- and likelihood-alarm systems as a function of properties of the threats and the security system. We also compared model predictions to actual user behavior, as measured in a controlled experiment. The experimental results were generally in line with the normative model. However, the model predicted that the outcomes from blocking would always be worse or equal to those from warnings. The experiment, however, showed that blocking may have an advantage over warnings, because it leads to fewer undetected events (as predicted by the model), without significantly lowering the mean value of outcomes (the model predicts a lower value). We discuss practical implications regarding the use of blocking and alerting and the more general value of combining optimal decision models and empirical experiments for determining system designs.

Optimal Decision Model Design of Diversified Port Investment

Fu, T. and Zhang, Y., 2020. Optimal decision model design of diversified port investment. In: Yang, Y.; Mi, C.; Zhao, L., and Lam, S. (eds.), Global Topics and New Trends in Coastal Research: Port, Coastal and Ocean Engineering. Journal of Coastal Research, Special Issue No. 103, pp. 159–162. Coconut Creek (Florida), ISSN 0749-0208.In order to solve the problem of low profit in the traditional optimal decision model of diversified port investment, a new optimal decision model of diversified port investment is designed. Firstly, the investment decision -making index system of major stakeholders is constructed, in which the major stakeholders include the government, investors, creditors, contractors, operators, and so on. Carry on hinterland area analysis and hinterland goods quantity forecast. Then, the constraints of the optimal decision model for diversified port investment are determined, including cost-benefit constraints, ecological carrying capacity constraints, supply and demand constraints, integer programming constraints. Finally, the optimal decision model of diversified port investment is constructed according to the given constraints. The model consists of demand forecasting module, income module, cost module and investment and financing module. In order to prove that the profit performance of the optimized decision model is good, the traditional model is compared with the model. The experimental results show that the model is better than the traditional model.

Analysis of a firm's optimal size based on accessible market capacity

E-commerce has dramatically reduced the limitation of space and time on economic activities, resulting in individuals having access to a huge number of consumers. In this paper, we propose a company's optimal size decision model containing management costs as a means of investigating the evolution of the company size in e-commerce. Given that production decisions are made based on accessible market capacity, we explain how a company enters the market, and we draw an evolutionary path of the optimal company size. The results show that in the early expansion stage of accessible market capacity, a firm's optimal size keeps increasing; after reaching a peak, the change in a firm's optimal size depends on its cost management. When the accessible market capacity reaches a threshold, the firm will no longer be in the market, and may no longer exist. Finally, we construct a simulation framework based on complex adaptive systems to validate our proposed model. A simulation experiment confirms our model and reveals the dynamic co-evolution process of individual producers and firms.

Decision Optimization of Low-Carbon Dual-Channel Supply Chain of Auto Parts Based on Smart City Architecture

Affected by the Internet, computer, information technology, etc., building a smart city has become a key task of socialist construction work. The smart city has always regarded green and low-carbon development as one of the goals, and the carbon emissions of the auto parts industry cannot be ignored, so we should carry out energy conservation and emission reduction. With the rapid development of the domestic auto parts industry, the number of car ownership has increased dramatically, producing more and more CO2 and waste. Facing the pressure of resources, energy, and environment, the effective and circular operation of the auto parts supply chain under the low-carbon transformation is not only a great challenge, but also a development opportunity. Under the background of carbon emission, this paper establishes a decision-making optimization model of the low-carbon supply chain of auto parts based on carbon emission responsibility sharing and resource sharing. This paper analyzes the optimal decision-making behavior and interaction of suppliers, producers, physical retailers, online retailers, demand markets, and recyclers in the auto parts industry, constructs the economic and environmental objective functions of low-carbon supply chain management, applies variational inequality to analyze the optimal conditions of the whole low-carbon supply chain system, and finally carries out simulation calculation. The research shows that the upstream and downstream auto parts enterprises based on low-carbon competition and cooperation can effectively manage the carbon footprint of the whole supply chain through the sharing of responsibilities and resources among enterprises, so as to reduce the overall carbon emissions of the supply chain system.

A mixed activity-based costing and resource constraint optimal decision model for IoT-oriented intelligent building management system portfolios

Recent advances in the Internet of Things (IoT) have initiated the development of intelligent buildings. Intelligent buildings are expected to provide benefits such as high efficiency, energy-savings and smart services. IoT-oriented management systems play an important role in providing for the operational optimization of intelligent buildings. The main objective of the present study is to find the key determinants that provide an optimal portfolio of IoT-oriented Intelligent Building Management System (IBMS) adoptive strategies for decision-makers. In an IoT-oriented decision-making model, the IoT characteristics’ measurement for the evaluation and determination of management systems for intelligent building are incorporated through the MCDM method, and incorporates an Activity-Based Costing evaluation and resource constraints into Zero-One Goal Programming in the optimal portfolio selection process. The main results indicate that Disaster Prevention System and Energy Management System would be selected for the office building and Factory Environment Monitoring and Factory Energy Monitoring for the smart factory through the integrated decision model. Simultaneously, the result underlines the significant interrelation between Acts and Regulations and Ecosystem Value Chain to provide government policy making. The main contribution of this study is to provide a new decision model integrating activity-based costing and resource constraints into IBMS optimal portfolio selection.

Virtual Storage-Based Model for Estimation of Economic Benefits of Electric Vehicles in Renewable Portfolios

The expected increase in the presence of electric vehicles raises numerous questions regarding their impact on the market relations. Depending on the agreement between the involved parties, the position of EVs changes from passive (traditional role) to active. Active EVs are beneficial for variability and uncertainty-intense modern power systems. To enable this transition, a suitable framework in the form of agreements is required in order to establish the terms and responsibilities. Following the presented agreements, we propose a novel method for evaluation of the benefits that the newly added EVs bring to the portfolio. The method comprises two steps, a Monte Carlo simulation of the EV driving/charging patterns and an optimization model for market related decision making. The method results in the estimates on economic savings resulting from adding EVs to portfolios. An illustrative example is used in order to give an idea of the range of the benefits.

Modelling and Simulation of Facility Planning Problem Based on Improved SLP Method

Facility planning plays a critical role in various fields. An excellent facility layout can minimize the losses caused by the connection between facility units in production operations. In this paper, we improved the traditional Systematic Layout Planning (SLP) method, combine it with the multi-objective decision optimization model. The Improved SLP (ISLP) method is described in detail in this paper, including its principles, process of building model, and calculation methods. To make the ISLP more suitable for research and practical problems, we established a mathematical model for it. This optimization model was also applied to the existing facility layout problem to realistically validate the effectiveness of the ISLP and the model. Results on the established model were found first, then the Flexsim simulation software was used to simulate the layout of the facility before and after the improvement. Finally, the results of the experiment were analysed and compared.

Optimization of Shop Visit for Aeroengine Using the Proportional Hazard Model

According to the definition given by the world airlines technical operations glossary (WATOG), an aeroengine removal is classified as a shop visit (SV) whenever the subsequent engine maintenance performed before reinstallation entails separation of pairs of major mating flanges or removal of a disk, hub, or spool. Considering the high cost of both purchase and maintenance of the aeroengine, it is necessary to optimize the SV decision to decrease quantities of spare engines and the number of SVs for the aeroengine. Therefore, by making full use of monitoring data in aeroengine and adopting just-in-time theory, an optimization method of SV for the aeroengine using the proportional hazard (PH) model is proposed. First, a Weibull PH model for aeroengine is established to predict the remaining useful life (RUL), and the parameters are estimated with the method of principal component analysis and genetic algorithm. Second, an SV decision policy is proposed and the optimization model of SV decision is established based on the RUL to minimize the expected cost rate. Finally, a case study on a specific aeroengine is conducted to demonstrate the effectiveness of the proposed method. Results show that the prediction of RUL based on the proposed PH model has a low prediction error and the process of SV decisions is dynamic and feasible to obtain the optimal SV decision.

Research on Optimization of Airport Pavement Renovation Process

The huge investment in the airport has great significance for economic development and national defense. In the past 20 years, China has built a large number of airports, and its pavements have been exposed to the outdoor environment for a long time, resulting in easy be damaged. Therefore, in recent years, the problems faced by the renovation of airport pavements have become increasingly prominent. Based on the analysis of the core functional indicators of the airport pavement, the paper establishes the hierarchical structure model, functional model and cost model, and finally forms the optimization model of the renovation process decision. The simulation calculation is carried out through engineering examples, and the advantages, shortcomings and their gaps of different renovation processes are obtained, which prove the rationality and effectiveness of the model.

An Optimal Ordering Decision-making Model with Random DemandUnder Carbon Constraint

Under the increasing pressure to reduce carbon emission, the enterprises need to actively take account into carbon emission, and take low carbon action among daily business activities. This not only relate to the realization of carbon emission goal, but probably efficient solution to our country carbon emission targets. Applying to optimal theory under carbon-constrained, this paper comprehensively include economical cost and environment cost to construct random optimal decision-making model. Then using mat lab numerical analysis, this paper reveals the decision-making mechanism of enterprise ordering making under carbon cap constrained, and provides management implications and future research direction. The result indicates that: An enterprise can significantly reduce carbon emission without significantly increasing cost through adjusting ordering quantity. Enterprise’s carbon emissions show a certain correlation with order quantity when carbon emission cap is within the scope of threshold value, but optimal ordering decision-making has no correlation with carbon emission cap when cap is beyond the scope of threshold value. Caron emission cap is set voluntarily by an enterprise’s decision maker or put forward mandatorily by an external regulatory agency, and its setting should be reasonable and scientific.

Optimal Maintenance Decision Based on Remaining Useful Lifetime Prediction for the Equipment Subject to Imperfect Maintenance

Focusing on the fact that the existing research on optimal maintenance decision for remaining useful lifetime (RUL) prediction and imperfect maintenance has low accuracy of RUL prediction and rationality of decision results, an optimal maintenance decision method based on RUL prediction for the equipment subject to imperfect maintenance is proposed in this paper. Firstly, the nonlinear Wiener process is used to characterize the degradation law of the equipment. Secondly, the imperfect maintenance model that meets the upper limit of the maintenance number is established based on the nonhomogeneous Poisson process. Then, based on the concept of the first hitting time, the probability density function (PDF) of the RUL is derived. Finally, based on the RUL prediction results, the optimal maintenance decision model for the equipment subject imperfect maintenance is constructed. Through the example verification and cost parameter sensitivity analysis, the proposed method can effectively improve the accuracy of the RUL prediction and the scientific of maintenance decision results, which has engineering application value.

Optimally Imprecise Memory and Biased Forecasts

We propose a model of optimal decision making subject to a memory constraint. The constraint is a limit on the complexity of memory measured using Shannon’s mutual information, as in models of rational inattention; but our theory differs from that of Sims (2003) in not assuming costless memory of past cognitive states. We show that the model implies that both forecasts and actions will exhibit idiosyncratic random variation; that average beliefs will also differ from rational-expectations beliefs, with a bias that fluctuates forever with a variance that does not fall to zero even in the long run; and that more recent news will be given disproportionate weight in forecasts. We solve the model under a variety of assumptions about the degree of persistence of the variable to be forecasted and the horizon over which it must be forecasted, and examine how the nature of forecast biases depends on these parameters. The model provides a simple explanation for a number of features of reported expectations in laboratory and field settings, notably the evidence of over-reaction in elicited forecasts documented by Afrouzi et al. (2020) and Bordalo et al. (2020a).

Load Aggregator Purchase Optimization Strategy Considering Renewable Energy Quota

The introduction of the renewable energy quota system has an impact on the benefits of all market players in the electricity market. This paper aimed at the decision making of load aggregators’ electricity purchase under the assessment of renewable energy quota. Firstly, the market subject, assessment method and strategy of renewable energy quota system were analyzed effectively. Then the scenario of electricity purchasing faced by load aggregators was described and the optimal decision-making model of electricity purchasing by load aggregators considering renewable energy quota was proposed. At last, the model proposed in this paper was verified by relevant examples, and the results show that the optimal purchasing strategy of load aggregator proposed in this paper had obvious economy.

Coordination model of supply chain based on deciders’ risk attitudes in a fuzzy decision environment

This paper considers a two-stage supply chain consisting of one manufacturer and one retailer, exploring the impact of the fuzzy uncertainty of product yield and demand and the deciders’ risk attitudes on the optimal order quantity of the retailer. At the same time, this study tries to analyze the coordination problem in the two-stage supply chain with consideration of the retailer and the manufacturer’s risk attitudes. Firstly, this study develops a supply chain optimal decision model in a centralized decision framework. In the proposed model, the L-R fuzzy numbers are used to depict the yield and demand with fuzzy characteristics. Then, the coordination of quantity discount in a supply chain is studied. Consequently, this research further investigates a special case in which the market demand and yield are assumed to be triangular fuzzy numbers, and the optimal solution of the order quantity and the wholesale price are obtained. At last, this paper utilizes several numerical examples to validate the proposed model. The results show that the quantity discount contract can coordinate the supply chain in a fuzzy environment, and the optimal order quantity decreases with the increasing of the risk bias coefficient of the retailer and the manufacturer. It also suggests that risk-seeking retailer will order more products, in addition, the manufacturer tend to choose a risk-seeking retailer as partner and the retailer is more likely to choose a risk-seeking rather than risk-aversion manufacturer as partner.

Particle Swarm Optimized Hybrid Kernel-Based Multiclass Support Vector Machine for Microarray Cancer Data Analysis.

Determining an optimal decision model is an important but difficult combinatorial task in imbalanced microarray-based cancer classification. Though the multiclass support vector machine (MCSVM) has already made an important contribution in this field, its performance solely depends on three aspects: the penalty factor C, the type of kernel, and its parameters. To improve the performance of this classifier in microarray-based cancer analysis, this paper proposes PSO-PCA-LGP-MCSVM model that is based on particle swarm optimization (PSO), principal component analysis (PCA), and multiclass support vector machine (MCSVM). The MCSVM is based on a hybrid kernel, i.e., linear-Gaussian-polynomial (LGP) that combines the advantages of three standard kernels (linear, Gaussian, and polynomial) in a novel manner, where the linear kernel is linearly combined with the Gaussian kernel embedding the polynomial kernel. Further, this paper proves and makes sure that the LGP kernel confirms the features of a valid kernel. In order to reveal the effectiveness of our model, several experiments were conducted and the obtained results compared between our model and other three single kernel-based models, namely, PSO-PCA-L-MCSVM (utilizing a linear kernel), PSO-PCA-G-MCSVM (utilizing a Gaussian kernel), and PSO-PCA-P-MCSVM (utilizing a polynomial kernel). In comparison, two dual and two multiclass imbalanced standard microarray datasets were used. Experimental results in terms of three extended assessment metrics (F-score, G-mean, and Accuracy) reveal the superior global feature extraction, prediction, and learning abilities of this model against three single kernel-based models.

A stochastic multi-objective optimization decision model for energy facility allocation: a case of liquefied petroleum gas station

To mitigate air pollution problem, the government has been planning to build more liquefied petroleum gas stations to motivate drivers to use liquefied petroleum gas vehicles in Taiwan. Such facility allocation problem is a multi-objective optimization process considering spatial variation in the need of refueling. This study presents a stochastic multi-objective optimization model for liquefied petroleum gas station allocation (SMOMLSA) that integrates a nondominated sorting genetic algorithm II with a Monte Carlo simulation to optimally allocate liquefied petroleum gas stations according to three trade-off objectives, including investment performance, energy conversion, and business opportunity. Monte Carlo simulation procedure generates the starting location of a taxicab car in need of refueling in the spatial grid based on a probability distribution. Nondominated sorting genetic algorithm II resolves the station location problem with these multi-objectives. The SMOMLSA was validated by conducting a real-world case study. Result depicts that the SMOMLSA can provide information on the optimal allocation of liquefied petroleum gas stations for minimizing construction costs, minimizing average refueling distance for vehicles, and maximizing potential customers.

Game Theory based Optimal Vertical Handover Decision Model for Heterogeneous Mobile Networks

Game Theory based Optimal Vertical Handover Decision Model for Heterogeneous Mobile Networks

Evaluation and Decision-Making Method of Power Distribution Network Projects Based on Weighting with Game Theory and Implicit Enumeration

Aiming at the problems of one-sided determination of index weight in the evaluation method of distribution network projects, and instability of project optimization results, a method of evaluation and decision-making of distribution network construction and renovation projects based on weighting with game theory and implicit enumeration is proposed. Firstly, the evaluation index system is constructed from two dimensions of technical efficiency and economic benefit. Secondly, the indicators are weighted by the weight set model based on game theory. After that, the decision optimization model is solved by the implicit enumeration method. The case study shows that the method is practical and effective, and can provide a scientific and reasonable reference for the construction and renovation projects investment of grid companies.