Optimal Decision Model Research Articles

Steam Trap Maintenance-Prioritizing Model Based on Big Data.

Steam traps in large facilities need continuous maintenance to prevent corrosion and other damage that could pose a considerable threat to a facility and its workers. However, a significant amount of human resources is required for the maintenance of steam traps. An automatic method to inform stakeholders regarding maintenance cycles will be beneficial for the maintenance process. Therefore, an optimal maintenance priority decision model is developed in this study to establish an efficient steam trap management system. First, the frequency of failures, installation locations, and specifications of steam traps were determined as parameters causing a failure. A relative score and conversion score are calculated for each parameter. The final conversion score is the sum of the conversion score multiplied by the corresponding steam trap data weight factor. Steam traps within the range requiring inspection are classified as high priority cases. Experimental results confirmed that the failure accuracy rate is approximately 95%, and the average failure error rate is within 3%. Additionally, the number of steam traps to be checked was reduced by 3616. The proposed model significantly reduces maintenance in commercial industries.

Farmers credit optimization decision model and applications based on common risk guarantee fund

The paper proposes the farmers credit optimization decision model and applications based on common risk guarantee fund. By considering two conditions of with default risk and without default risk, the individual rationality of bank and farmer is designed, respectively, and the mathematical formulas for calculating the risk loss ratio of bank loans under the guarantee fund are established and discussed innovatively, respectively. A nonlinear optimal model based on risk compensation fund is established in the paper. By mathematical proof, the optimal bank credit decision mechanism is studied. By the analysis of numeric experiments of two cases of with default risk and without default risk, the expected income and invest income are varied by the changing of farmer project success probability and default probability, and the relationship of loan interest, farmer expected income and invest income is studied. The research has scientific guiding significance and practical application value for farmers' credit decision making.

A hybrid classical techniques and optimal decision model for iris recognition under variable image quality conditions

One of the best biometrics used for human verification and identification is iris recognition. The contrast of its unique characteristics differs from one candidate to another in which the iris pattern has numerous well-known features like uniqueness texture, stability and compactness representation for human identification. Among these facts, several approaches in these areas are localized, but there is still an abundant problems such as the low match rate of the score level and low accuracy. Therefore, a decision model should be essential for iris recognition frameworks. This paper proposes a iris recognition based on a decision model using a unified framework based on the integration of three detection schemes due to variation occurred in shading and position change using Smallest Univalue Segment Assimilating Nucleus (SUSAN), Generalized Hough Transform (GHT) and Viola–Jones (SUSANGHT-VJ) for eye detection, enhancing the dimmer and darker areas using fuzzy retinex method and normalizing the iris boundary using daugman’s Rubber Sheet Model for segmentation. Also, the iris corner points is extracted using Gabor Wavelet Transform (GWT), the vector properties of the blurred texture features are quantized using Local Phase Quantization (LPQ) and the optimal decision model based on Atom Search Optimization (ASO) and Feed Forward Counter propagation Neural Network (FFCNN) for matching score level and classification task. Furthermore, the current framework prevents false matches and inappropriate iris input, thus making the iris match score framework more reliable. The evaluation of the proposed approach is trained and tested with the employed eye template of iris and face datasets. Therefore, the results depict that the proposed technique gives a high recognition rate of 99.9% on different datasets compared to existing methods.

Binary goal programming model for optimizing tire selection using branch and bound algorithm

The problem of assessment and adoption of automotive tyre design specifications has not been addressed sufficiently in literature. This is in spite of its significance as a crucial component relevant to design and safety of the automobile. In this paper, a multi-objective optimization model of the tyre design trademark adoption decision is proposed. Multi-attribute or multi-criterion decision making techniques are heuristics providing good solution, but do not guarantee optimum solution. Up to date, there is no optimal yielding method for selection of vehicle tyre manufacturer or trademark based on prespecified design targets. The proposed model is formulated as a binary goal programming model for optimizing tyre trademark design selection decision by adopting an optimal tyre design trademark that best achieve design targets. The model is solved by the branch and bound algorithm. One advantage of the proposed model is flexibility to incorporate multiple design targets, tolerance limits and different constraints. The proposed model can support efficient and effective decision making concerning the adoption of tyre trademark design for new automobile or to re-adopt new design for new road vehicle operating conditions.

Optimization Model of Network‐Level Pavement Maintenance Decision considering User Travel Time and Vehicle Fuel Consumption Costs

The maintenance management decisions of network‐level asphalt pavements have long been a challenge to highway agencies, and a great amount of factors have been involved. In this study, a network‐level optimization method was established by integrating the maintenance benefits into the zero‐one programming optimization model. An optimized performance evaluation method of asphalt pavement was proposed which contains 11 different kinds of combinations. The benefit model quantifies the cost savings of user travel time and vehicle fuel consumption to the pavement condition index (PCI) and ride quality index (RQI), respectively. Based on the simplified evaluation method as well as the quantified maintenance benefit model, an optimization model was established by employing the zero‐one programming. This optimization model aimed to maximize the improvements/price ratio of pavement maintenance for the whole pavement network. The calculation results present the optimal strategies of maintenance for every road section in the network. The applicability of the newly proposed model was validated by a case study. The methodology developed in this study helps to offer guidelines to highway agencies in managing and making decisions about network‐level pavement maintenance.

Premium Power Value-Added Service Product Decision-Making Method Based on Multi-Index Two-Sided Matching

With the further reform and development of the electricity retail market in China, the premium power value-added service (PPVS) product is becoming increasingly demanded by high-tech customers. However, the problem of PPVS demand-level matching has not been well studied. How to build an optimal matching and decision-making model which can improve both customers’ and electricity retail companies’ satisfaction degree is an urgent issue. Thus, this paper proposes a decision-making method for PPVS products with multi-index expectation based on two-sided matching. Firstly, a multi-index evaluation system is established from the perspective of customers and electricity retail companies. Secondly, the loss and gain matrices of customers and electricity retail companies are constructed, considering the difference between the expected level (EL) and the actual level (AL) of the evaluation indices. Thirdly, perceived utility (PU) of both customers and electricity retail companies are described with the introduction of elation function and disappointment function, due to different perceptions concerning the matching results under both sides’ evaluation indices. Fourthly, a multi-objective two-sided matching optimization model that aims to maximize the PU of both sides is developed. Finally, an empirical analysis is conducted on three large high-tech electronic-based customers in an industrial park in western Guangdong for demonstrating the effectiveness and rationality of the proposed method.

Bidding Strategy of a Flexible CHP Plant for Participating in the Day-Ahead Energy and Downregulation Service Market

To integrate renewable energy generation from wind and solar radiation, most combined heat and power (CHP) plants in northern China are undergoing large-scale upgrades and retrofitting to improve operational flexibility. Simultaneously, the downregulation service (DRS) market is still retained in the day-ahead market in many provinces. Considering a flexible CHP plant with multiple types of coal-fired CHP units and heat storage as a price taker, this paper establishes a collaborative bidding strategy for the CHP plant to participate in both the day-ahead energy market and day-ahead DRS market. The bidding strategy includes the optimal output decision model, power output range model, capacity segmenting model, and bidding models for two markets determined by the marginal generation cost (MGC) and the marginal downregulation cost (MDRC), respectively. These models are validated based on realistic data from a CHP plant in Northeast China.

Sustainability and reliability ensurance models for automated technological systems in chemical industry: systemic ergonomic approach

The paper considers accidents and potential hazards of the world chemical enterprises, and provides statistics of accidents and human casualties. The authors investigate harmful factors affecting the production process and a human-operator, showing the central role of a human in the technological process (both as a source of errors and as an active element that eliminates errors, failures and cyber attacks’ consequences). We essentially consider automated technological complex as a Human-Machine-Environment system, thus a human-system approach should be applied. The authors developed a complex of systemic components and morphological models, which describe the human-machine system in the sections required for analysis, to ensure sustainable and reliable design with initial data. The authors also propose a method and information technology for interfaces’ ergonomic assessment; the principles for adaptive interfaces design; and mathematical models and information technology to assess safety and timeliness indicators of the chemical production operators’ activities. The models are based on the principles of the functional-structural theory by Anatoly Gubinsky, Vladimir Evgrafov, Akiva Asherov, Pavel Chabaneko and others, and on the mathematical apparatus of functional networks. Further, the authors develop an optimization model for decision supporting organizing the human-machine control technology, using the criterion of minimizing losses from unreliability and unsustainability. Both the models and the information technology have undergone extensive testing, including solving the tasks of: choosing the automation level for the control process; distributing functions between operators; control algorithms design; user interface design, design of agent-managers to support the operators’ activities. The results can be used as the basis for a decision support system to ensure sustainability and reliability of automated technological systems in chemical industry.

Intelligent IoT‐Based Cross‐Border e‐Commerce Supply Chain Performance Optimization

Internet of Things (IoT) technology can benefit automated production, agriculture, intelligent autonomous driving, and many other industries by using billions of smart devices. As a good example, intelligent IoT and 5G are the main source of information acquisition and play an important role in the multiobjective optimization process of the supply chain. This paper develops an optimal management and coordination method to improve the performance of cross‐border e‐commerce supply chain by using IoT tracking technique and multiobjective decision‐making. The numerical results justify that our proposed scheme has high internal consistency, with the Cronbach’s alpha factor of each dimension of the optimized decision model all greater than 80%.

Approximate Dynamic Programming of Long Term Power Generation Schedule

With respect to the features in optimal scheduling of long-term power generation such as long cycle, large scale and strong randomness, the paper proposed a multi-stage optimized decision model based on approximate dynamic programming. In this model, the influence of random variables including load, fuel price, and water was tested by means of stochastic simulations. Staged solution was used to reduce the size and difficulty of solving the problem. The proposed value function approximation strategy of pondage and coal inventory solved the "curse of dimensionality" in staged recursion and maintained the overall optimization characteristics after staged decomposition. Through the iterative update between the decision and the approximation function, the approximate optimal decision sequence was solved to obtain the optimization scheme of power generation scheduling. The calculation and analysis results in a real system in some province showed that the modeling by approximate dynamic programming is an effective way to solve such large-scale optimization and has a promising future by virtue of its conciseness, effectiveness, convenience in dealing with random factors, high calculation accuracy and rapidity of solution.

Optimal Action Strategy approach for Desert Crossing Based on Dynamic Programming

It is a novel but challenging task that simulated desert crossing strategy for the game players under the specified conditions. This study is aimed to develop an optimal action strategy approach for desert crossing based on dynamic programming. According to the game rules, a multi-stage decision optimization model was first established to maximize the remaining funds of players within given game time. Then, a simulation-based dynamic programming algorithm was designed to solve the optimization model, and the optimal action strategy was obtained by backtracking crossing routes. Finally, two experimental case studies with different initial conditions were used to validate the model. In comparison with the benchmark exhaustive search algorithm, the proposed approach has higher performance and efficiency. The case studies confirm that the method can obtain the optimal action strategy under different game maps and initial resources.

Robust Optimization Model for R&D Project Selection under Uncertainty in the Automobile Industry

In a company, project management is responsible for project selection from candidates under some limited constraints to achieve the company’s goal before the project begins as well as the project operations in progress. The development of new technologies and products can broaden a company’s market share, and to do so, research and development (R&D) projects are significant. However, limited funds force a company to select projects that can best represent the company’s interests. As projects may take a long time to develop, a number of uncertainties may occur, and the most concerning uncertainty is cost uncertainty. In this study, a robust optimization decision model for project selection considering cost uncertainty is proposed to assist the decision-making process for companies that need to select projects from a number of candidates due to limited funds. The model considers project selection in view of the total cost of ownership, which is a key factor for customers and companies in the automobile industry. The proposed model is tested in the automobile industry environment with different conservatism levels about cost uncertainty, and an analysis of expected market changes and a company’s income is performed with the solutions obtained from the proposed model. The result shows that the presented model reacts to cost uncertainty robustly for assisting the decision-makers in the company.

Integrated construction supply chain: an optimal decision-making model with third-party logistics partnership

Studies have confirmed the benefits of using Third-party logistics (TPL) for efficient construction management, especially in large projects. Nevertheless, there is a dearth of decision-making models evaluating the exact role of TPL providers as drivers for supply chain (SC) integration and optimisation. This study aims to develop a decision-making model for construction supply chain (CSC) optimisation, with possible TPL integration. The proposed model considers two types of purchased materials (type-1 and type-2) and assists the main contractor in determining construction supply chain management (CSCM) strategies, including supplier selection, order quantity determination, and TPL use evaluation. Using the model, the main contractor can take advantage of the TPL provider’s warehouse and order larger quantities, if necessary, to obtain lower prices offered by suppliers. Through case examples in Canada, we find that the proposed model performs better in optimising total SC cost, as compared to the model without TPL. Model validations also show that TPL can be conditionally used to improve construction logistics performance and to meet practical requirements targeting issues in the construction industry.

Multi-Objective Optimization Model for P + R and K + R Facilities’ Collaborative Layout Decision

In order to reduce the pressure on urban road traffic, multi-modal travel is gradually replacing single-modal travel. Park and ride (P + R) and kiss and ride (K + R) are effective methods to integrate car transportation and rail transit. However, there is often an imbalance between supply and demand in existing car occupant transfer facilities, which include both P + R and K + R facilities. Therefore, we aim to conduct a research on P + R and K + R facilities’ collaborative decision. It first classifies car occupant transfer facilities into types and levels and sets the service capacity of each category. On the premise of ensuring the occupancy of parking spaces, our model aims to maximize the intercepted vehicle mileage and transfer utility and establishes an optimal decision model for car occupant transfer facilities. The model collaboratively decides the facilities in terms of location selection, layout arrangement, and overflow demand conversion to balance the supply and demand. We choose Chengdu as an example, apply the multi-objective optimization model of car occupant transfer facilities, give improved schemes, and further explore the influence of the quantity of facilities on the optimization objectives. The results show that the scheme obtained by the proposed model is significantly better than the existing scheme.

Multi-objective optimization design of green building envelopes and air conditioning systems for energy conservation and CO2 emission reduction

Configurations of the building envelope and the air conditioning system have a considerable effect on annual building energy efficiency. Taiwan government proposed two green building indices, namely the Envelope Energy Load (ENVLOAD) and the Performance of Air Conditioning System (PACS), as energy performance models of building envelopes and air conditioning systems. Relatively few studies, however, have been mainly conducted, using multiobjective optimization design for building envelope and air condition systems, with a goal to treat both the building energy systems and the envelope. Consequently, this study is to make contribution to this related filed by proposing a multi-objective optimal building envelope and air conditioning system energy performance decision model (MOBELM).This model was used to design the optimal configurations of building envelopes and air conditioning systems based on the ENVLOAD and PACS constrictions and the two conflicting objectives. The first refers to the minimal construction cost when the building envelope and air condition system are built. The second is concerned with the minimal CO2 emission when the envelope materials are manufactured and used to form the building envelope. In the case study, the optimized configurations that dominated the original unoptimized design could validate the MOBELM.

Research on the Optimization of Integrated Schedule and Transport Plan of E-Commerce Goods

Based on the line-haul freight transport of a certain e-commerce company, this paper studies the cargo scheduling schemes between different supply centres and demand centres with different transport modes. Taking the minimum transport cost as the objective function, time window and supply-demand balance as constraints, considering the various types of goods, this paper establishes the decision-making optimization model for e-commerce goods line-haul transportation, and calculates the cargo scheduling schemes in different scenarios by LINGO 18.0. The paper finds out that highway is still the main mode of transporting e-commerce goods at present; with the continuous improvement of high-speed rail freight transportation, railway shows great potentials to carry e-commerce goods; at the same time, air transport is irreplaceable for transporting time-sensitive products and high value-added products.

A collision avoidance decision-making system for autonomous ship based on modified velocity obstacle method

This paper proposed a new collision avoidance decision-making system designed for autonomous ship. The system outputs collision avoidance decisions based on the latest information at a certain frequency, which is suitable for real ship application. Front end and back end are two main components of this system. Front end provides preliminary information while back end generates collision avoidance decisions. Based on modified velocity obstacle method, the multistage optimization decision model is introduced and various constrains are considered including ship maneuverability, multi-ship, COLREGS, off-course and seamanship. Then the interactive actions taken by other ships during collision avoidance process are further analyzed. The modified velocity obstacle method incorporates a Finite State Machine (FSM) which can be used to handle the dynamic behavior of other ships. Finally, the case study is completed on the Electronic Chart System (ECS) to show that the proposed collision avoidance decision-making system is robust and effective under various marine scenarios. Therefore, the system has great potential to be equipped on board in the future.

The Criteria of Optimal Training Cost Allocation for Sustainable Value in Aesthetic Medicine Industry

Medical disputes that result in medical compensation and losses affect the financial management and sustainable operational risks of enterprises. Employee training plays an important role in the sustainable growth of human resource management and also can help avoid any potential risks to enterprises’ operating revenue. Based on data of a company, this study’s model aims to establish a mathematical model to find the most suitable decision variables in order to provide decision-making analysis and judgment of a company’s individual economic behaviors. From the second-order differential modeling method, where the functional training time of the aesthetic medicine industry (including medical errors/dispute incidences, functional training costs, and medical benefits) links to a specific functional relationship, the optimal decision-making model and evaluation criteria for the proportion of this training time under the concept of sustainable management can be constructed. The method proposed herein reduces medical errors or disputes, strengthens risk and financial management, provides customers with the best service quality, and serves as the basis for decision-making evaluation of the maximum benefits of sustainable operations.

Optimization Model of Adaptive Decision Taking Support System for Distributed Systems Cyber Security Facilities Placement

Optimization Model of Adaptive Decision Taking Support System for Distributed Systems Cyber Security Facilities Placement

When Risk Perception Gets in the Way: Probability Weighting and Underprevention

Prevention efforts, such as quitting smoking, flu vaccination, and exercising, are of crucial importance in health policy, but people tend to undertake too few of them. The main reason is that most prevention efforts only reduce but do not completely eliminate the risk of poor health. This makes it harder for people to assess the benefits of prevention, because they tend to misperceive and transform probabilities. In “When Risk Perception Gets in the Way: Probability Weighting and Underprevention,” Baillon et al. introduce psychological insights (probability weighting) in a model of optimal decision making and show that most people undertake too little prevention when the risk of poor health is between 10% and 80%. The paper discusses several policy measures to make people spend more on prevention.