Optimal Decision Model Research Articles

An Investment Decision Model of Distribution Network Planning Based on Correlation Mining of Reconstruction Measures and Loss Load Index

In recent years, how to build a reliable, high-efficient, high-tech and environmental-friendly distribution network infrastructure and service system, especially to accurately assess the reliability impact of various technical measures and establish optimal investment decision model for the future distribution network planning, has great theoretical and practical significance. However, the traditional analysis of investment decision model of distribution network is based on complex power flow calculation, which is not suitable for current construction of large and smart distribution network with multi-agent interaction under electrical market environment. Therefore, in this paper, based on the sample data, the correlation mining methods of the reconstruction measures and loss load index, such as neural network method, are applied to analyse the relationship between different types of reconstruction measures and loss load index. Based on the correlation mining methods, the relationship between the reconstruction measures and loss load index can be obtained and the value of loss load can be fast predicted. Through the comparison the relationship between reconstruction measures and loss load index, the optimal reconstruction measures can be chosen, and the data relationship based accurate investment decision model can be built. Experimental result shows the accuracy and effectiveness of the presented methodology.

Optimal Decision Model under Mixture Inventory System

The International Scientific Research Organization for Science, Engineering and Technology (ISROSET) is a Non-Profit Organization; The ISROSET is dedicated to improvement in academic sectors of Science (Chemistry, Bio-chemistry, Zoology, Botany, Biotechnology, Pharmaceutical Science, Bioscience, Bioinformatics, Biometrics, Biostatistics, Microbiology, Environmental Management, Medical Science, Forensic Science, Home Science, Library Science, Material Science Military Science, Physical Science, Physical Education Science, Educational Science, Fisheries, seed technology, Agriculture, Forestry Science, Mathematics, Physics, Statistics and Geology/Earth Science), Computer Science, Engineering and Information Technology, Commerce, Management, Economics Sociology and Social Science.

Optimal Epidemic Information Dissemination in Uncertain Dynamic Environment

Optimization of stochastic epidemic information dissemination plays a significant role in enhancing the reliability of epidemic networks. This letter proposes a multi-stage decision-making optimization model for stochastic epidemic information dissemination based on dynamic programming, in which uncertainties in a dynamic environment are taken into account. We model the inherent bimodal dynamics of general epidemic mechanisms as a Markov chain, and a state transition equation is proposed based on this Markov chain. We further derive optimal policies and a theoretical closed-form expression for the maximal expected number of successfully delivered messages. The properties of the derived model are theoretically analyzed. Simulation results show an improvement in reliability, in terms of accumulative number of successfully delivered messages, of epidemic information dissemination in stochastic situations.

Stochastic Sizing of Energy Storage Systems for Wind Integration

In this paper, we present an optimal capacity decision model for energy storage systems (ESSs) in combined operation with wind energy in power systems. We use a two-stage stochastic programming approach to take into account both wind and load uncertainties. The planning problem is formulated as an AC optimal power flow (OPF) model with the objective of minimizing ESS installation cost and system operation cost. Stochastic wind and load inputs for the model are generated from historical data using clustering technique. The model is tested on the IEEE 39-bus system.

Optimal operation strategy for distributed battery aggregator providing energy and ancillary services

The high penetration of distributed renewable energy raises a higher concern for the safe and economic operation of the smart grid. Distributed batteries equipped in demand-side can not only contribute to the reliability and security of the grid, but also make profits by participating in the electricity market, especially when distributed batteries are combined and operated by an aggregator. Considering the well-operated mechanism of performance based regulation (PBR) in the U.S. electricity market, it becomes increasingly lucrative for batteries to participate not only in energy markets for energy arbitrage, but also in ancillary service markets to provide regulation and peak-load shaving services. In this study, distributed batteries are operated and coordinated by the aggregator, which simultaneously submits offers to the energy and the ancillary service markets as an individual entity. An optimal decision model is formulated for the aggregator to determine the operation and bidding strategy for the distributed batteries by considering the characteristics of batteries, including the terms of capacity, efficiency and degradation cost. Finally, a numerical case is conducted to evaluate the benefits of the decision model.

An Economical Optimization Model of Non-Periodic Maintenance Decision for Deteriorating System

Condition-based maintenance is an effective method for deciding when to maintain deteriorating system. In this paper, a non-periodic maintenance model with adaptive inspection intervals is proposed by the consideration of system stability and deterioration. Different maintenance plans and monitoring strategies are adopted in distinct stages of life cycle of the deteriorating system to reduce costs, which is a complex multiple optimization parameter problem. And particle swarm optimization algorithm combining heuristic rules is designed to solve this multi-objective problem. Finally, a numerical example is implemented to illustrate the effectiveness and rationality of the proposed model. The comparison between the proposed model and other maintenance models illustrates the economy of the proposed model and the importance of considering system stability. Sensitivity analysis is also performed to investigate the effect of seven cost factors.

Probabilistic Load Flow With Wind Farms Using a Frequency and Duration Method

Probabilistic load flow (PLF) is an important tool in power system planning and operation. One limitation of conventional PLF is that only the probability information of random variables is obtained as a reference for related analyses. Frequency and duration information often plays an important role in power system assessment. In this paper, a frequency and duration method for PLF with wind farms (WFs) is proposed based on Markov chains by using an improved probability-frequency distribution function (PFDF) method. Random input variables, including intermittent loads, conventional generator (CG) power outputs associated with CG failures, and WF power outputs associated with both wind speed uncertainties and wind turbine failures, are modeled using corresponding PFDFs. With the proposed method, not only probability information but also frequency and duration information of random PLF outputs are efficiently and analytically computed through the operations of PFDFs of random inputs. Moreover, an optimal decision-making model for determining the clustering number of random states is proposed to improve the credibility of stochastic process modeling of Markov-chain-based random variables. The performance of the proposed method is verified and compared with that of a sequential Monte Carlo simulation technique using two modified IEEE test systems.

A dynamic decision model of users' sustained participation in crowdsourcing innovation

As a new open innovation model, crowdsourcing innovation is used to make full use of external crowd wisdoms to realise innovation performance. Due to the complexity and diversity of innovative tasks and users' free, voluntary participation, it is showed that the crowdsourcing innovation in practice with poor innovation performance and low willingness of users' sustained participation. Therefore, based on the theory of relationship marketing and knowledge sharing, this paper defines the concept of knowledge commitment in crowdsourcing innovation process, and the point is proposed that the essence of users' sustained participation in crowdsourcing innovation is the establishment of knowledge commitment. Then the key effect factors of knowledge commitment are analysed, the dynamic optimal decision model of users' sustained participation is established. Finally, the optimal decision results via the dynamic optimisation techniques are discussed.

A dynamic decision model of users' sustained participation in crowdsourcing innovation

As a new open innovation model, crowdsourcing innovation is used to make full use of external crowd wisdoms to realise innovation performance. Due to the complexity and diversity of innovative tasks and users' free, voluntary participation, it is showed that the crowdsourcing innovation in practice with poor innovation performance and low willingness of users' sustained participation. Therefore, based on the theory of relationship marketing and knowledge sharing, this paper defines the concept of knowledge commitment in crowdsourcing innovation process, and the point is proposed that the essence of users' sustained participation in crowdsourcing innovation is the establishment of knowledge commitment. Then the key effect factors of knowledge commitment are analysed, the dynamic optimal decision model of users' sustained participation is established. Finally, the optimal decision results via the dynamic optimisation techniques are discussed.

The optimization of Chinese power grid investment based on transmission and distribution tariff policy: A system dynamics approach

Grid investment optimization is an effective method for solving the mismatch between investment demand and the investment capacity of power grids. China is carrying out a new round of power system reform, and the main content is to control the revenue of the power grid enterprise through the transmission and distribution tariff policy. Revenue control may make the investment capacity of the power grid enterprise appear even more insufficient than it is currently. This paper studies the influence of the transmission and distribution tariff policy on the cash flow of the power grid enterprise and establishes an investment optimization decision-making model for the corporation using system dynamics theory. Then, taking a city as an example to undertake an empirical analysis, the paper puts forward suggestions and policy implications for the investment decision of the power grid enterprise after the reform of the electricity market.

Optimal operation of a battery energy storage system: Trade-off between grid economics and storage health

The utilization of grid-scale energy storage is growing exponentially due its decreasing costs and added flexibility to providing numerous services. Among the currently available storage systems, batteries based on lithium-ion chemistries are poised to provide a significant share of such flexibility due to their high power and energy density, and relatively low cost per unit energy. However, research on such systems has been segregated into focus on its chemical properties, and focus on the grid integration separately. This paper proposes a data-driven framework to characterize battery energy storage systems embedded into a decision-making optimization model. The model embeds two mechanisms, variable C-rates and variable efficiencies, so that batteries may be scheduled at high-power (high C-rate) operations to capture additional grid revenues, only if economical against the cost of degradation effects. The framework is applied to stationary battery energy storage systems in retail markets in order to explore the improved energy arbitrage benefits.

Epistemology of Optimization Models for Decision Making in Organizations

The intelligibility nucleus that subjects use for decision making in companies such as models based on nomothetic sciences and ideographic sciences has been applied to a wide range of situations within different areas of management. In the conscious decision making under certainty or uncertainty, limited or unlimited rationalities, every day or complexities and static or emerging properties, analyzes, forecasts, predictions and interventions are always performed. We might think that we are not forecasting, but our options will be driven by the anticipation of results of actions or inactions. The application of Optimization Models for decision making has in each moment an epistemological support, in the thinking and acting in the organization; therefore, have also served to analyze the organizations at their different levels. All this is argued in epistemological cradles that define the direction of organizations.

Adaptive decision-making of breast cancer mammography screening: A heuristic-based regression model

The American Cancer Society (ACS) updated their breast cancer screening guidelines in late 2015 and recommends that all women have the choice to start annual mammography screenings beginning at age 40. For women ages 45–54, the ACS explicitly recommends annual mammograms. However, due to the potential harms associated with screening mammography, such as overdiagnosis and unnecessary work-ups, the best strategy to design an appropriate breast cancer mammography screening schedule remains controversial. Instead of recommending a one-size-fits-all screening schedule, this study identifies a personalized mammography screening strategy adaptive to each woman's age-specific breast cancer risk. We present a two-stage decision framework: (1) age-specific breast cancer risk estimation and (2) annual mammography screening decision-making based on estimated risk. The results suggest that the optimal combinations of independent variables used in risk estimation are not the same across age groups. Our optimal decision models outperform the existing mammography screening guidelines in terms of the average loss of life expectancy. While most earlier studies improved the breast cancer screening decisions by offering lifetime screening schedules, our proposed model provides an adaptive screening decision aid by age. Since whether or not a woman should receive a mammogram is determined based on her breast cancer risk at her current age, our “on-line” screening policy adapts to a woman's latest health status, which reflects the current individual risk of each woman more accurately.

A Decision Model to Predict the Optimal Size of the Diversified Management Industry from the View of Profit Maximization and Coordination of Industrial Scale

To avoid the risk of single and homogeneous development, China’s coal enterprises have explored a diversified development model and are actively developing coal-based industries such as electric power, coal chemical, coal equipment manufacturing, logistics, and building materials. In previous studies of the diversification strategy, the focus has been placed on the motivation for diversification, the measurement of diversification, and the relationship between diversification and enterprise performance. From an enterprise strategic decision, we predicted the optimal size of each industry by considering the limited enterprise capital, human resources, the synergetic relationship among industrial clusters (mainly the scale coordination), and policy factors. The optimal decision model for diversified industrial management was constructed using linear programming methods. The decision target was to maximize the enterprise’s profit, but to also consider the social and environmental benefits. One of the largest listed coal enterprises in China, China Coal Energy (also a typical diversified enterprise), was selected as a case for analysis. Data were collected from surveys and annual reports from 2010 to 2014. The optimal scales of coal, electric power, chemical, and equipment manufacturing were predicted, and could be used as a reference for future enterprise production decisions. Furthermore, this decision model can be used as a reference for other diversified enterprises.

An optimal mandatory lane change decision model for autonomous vehicles in urban arterials

ABSTRACTAutonomous driving has become a popular topic in both industry and academia. Lane-changing is a vital component of autonomous driving behavior in arterial road traffic. Much research has been carried out to investigate discretionary lane changes for autonomous vehicles. However, very little research has been conducted on assisting autonomous vehicles in making mandatory lane changes (MLCs), which is the core of optimal lane-specific route planning for autonomous vehicles. This research aims to determine the best position for providing MLC instruction to autonomous vehicles. In this article, an optimization model is formulated to determine the optimal position at which an instruction to change lanes should be given through automotive navigation systems. First, the distribution of time spent waiting for safe headway to make a lane change is modeled as an exponential distribution. Lane-specific travel times are then calculated for vehicles in various situations by applying traffic shockwave theory and horizontal queuing theory. Finally, the expected travel time is derived for a vehicle receiving a lane change instruction to change lanes at an arbitrary position along the road. The proposed model is validated by a comparison with a simulation model in VISSIM. Additional experiments show that the instruction should be given earlier in the case of denser traffic or higher travel speed in the target lane and that vehicles can save considerable time, if they follow the guidance provided by the proposed model. The proposed model can be applied to guide autonomous vehicles to travel an optimal route.

Optimizing Temporary Rescue Facility Locations for Large-Scale Urban Environmental Emergencies to Improve Public Safety

Emergency rescue facility is an important component of urban system and affects public safety. Due to the special characteristics and complex environment of large-scale environmental disasters in urban areas, locations of emergency rescue facilities should be optimized. This study proposed an innovative methodological framework to optimize the locations of emergency rescue facility in the case of large-scale urban environmental disasters. A decision optimization model which consists of three-objectives representing service capacity, global efficiency and equity was developed. An appropriate spatial representation and encoding strategy was designed and coupled with the NSGA-II algorithm for model solving. Based on a hypothetical disaster scenario, a case study of Chaoyang district in Beijing is presented to demonstrate the methodology and explore the conflicting objectives. The results provide evidence that the model is able to successfully generate the Pareto-optimal frontier for the multi-objective emergency facility location problem, and provide a pool of alternative solutions to the decision-makers. The findings show that the framework proposed in this study has the potential to be a useful decision support tool for urban planning with respect to public safety.

Community energy system planning: a case study on technology selection and operation optimization

Community energy planning has attracted wide attention in China as the implementation of China’s low-carbon city policies. Meanwhile the rapid development of energy science and technology has shifted buildings and communities from energy consumers to prosumers, which lead to more difficulties in configuring the energy technology portfolio. This study es-tablishes an optimal decision-making model for community energy planning and develops an online tool called District EnErgy Planning(DEEP). Based on DEEP, the impact of energy price and equipment cost on optimization results, and the corresponding operational strategies are discussed through case study and scenario analysis. The results show that, when natural gas price exceeds 2yuan/m3 the gas-based solutions become economically uncompetitive. The technologies are propitious when equipment prices are reduced by 50%, 50%, 80% for CHP facilities, PV panels and electric battery, respectively.

Bike-sharing systems: User dissatisfaction in the presence of unusable bicycles

ABSTRACTIn bike-sharing systems, at any given moment, a certain share of the bicycle fleet is unusable. This phenomenon may significantly affect the quality of service provided to the users. However, to date this matter has not received any attention in the literature. In this article, the users' quality of service is modeled in terms of their satisfaction from the system. We measure user dissatisfaction using a weighted sum of the expected shortages of bicycles and lockers at a single station. The shortages are evaluated as a function of the initial inventory of usable and unusable bicycles at the station. We analyze the convexity of the resulting bivariate function and propose an accurate method for fitting a convex polyhedral function to it. The fitted polyhedral function can later be used in linear optimization models for operational and strategic decision making in bike-sharing systems. Our numerical results demonstrate the significant effect of the presence of unusable bicycles on the level of user dissatisfaction. This emphasizes the need to have accurate real-time information regarding bicycle usability.

A Similarity Measure-based Optimization Model for Group Decision Making with Multiplicative and Fuzzy Preference Relations

Group decision making (GDM) problem based on different preference relations aims to obtain a collective opinion based on various preference structures provided by a group of decision makers (DMs) or experts, those who have varying backgrounds and interests in real world. The decision process in proposed question includes three steps: integrating varying preference structures, reaching consensus opinion, selecting the best alternative. Two major approaches: preference transformation and optimization methods have been developed to deal with the issue in first step. However, the transformation processes causes information lose and existing optimization methods are so computationally complex that it is not easy to be used by management practice. This study proposes a new consistency-based method to integrate multiplicative and fuzzy preference relations, which is based on a cosine similarity measure to derive a collective priority vector. The basic idea is that a collective priority vector should be as similar per column as possible to a pairwise comparative matrix (PCM) in order to assure the group preference has highest consistency for each decision makers. The model is computationally simple, because it can be solved using a Lagrangian approach and obtain a collective priority vector following four simple steps. The proposed method can further used to derive priority vector of fuzzy AHP. Using three illustrative examples, the effectiveness and simpleness of the proposed model is demonstrated by comparison with other methods. The results show that the proposed model achieves the largest cosine values in all three examples, indicating the solution is the nearest theoretical perfectly consistent opinion for each decision makers.

Sub-optimality in motor planning is retained throughout 9 days practice of 2250 trials.

Optimality in motor planning, as well as accuracy in motor execution, is required to maximize expected gain under risk. In this study, we tested whether humans are able to update their motor planning. Participants performed a coincident timing task with an asymmetric gain function, in which optimal response timing to gain the highest total score depends on response variability. Their behaviours were then compared using a Bayesian optimal decision model. After 9 days of practicing 2250 trials, the total score increased, and temporal variance decreased. On the other hand, the participants showed consistent risk-seeking or risk-averse behaviour, preserving suboptimal motor planning. These results suggest that a human’s computational ability to calculate an optimal motor plan is limited, and it is difficult to improve it through repeated practice with a score feedback.