A two-stage rule base optimization method based on combination of classical algorithm and GA for intelligent decision support system

A two-stage multi-objective optimization method for envelope and energy generation systems of primary and secondary school teaching buildings in China

Discrete sizing optimization method based on dividing rectangles algorithm and local response surface for steel frame structures

Due to the strong coupling relationship between active power and reactive power in distribution network, the traditional method of optimizing the active and reactive power cannot make the distribution network operate in the optimal state of economical operation. And the uncertainties of load demand and power of distributed generation bring great challenges to the operation of distribution network. Based on this, a two-stage robust economical operation optimization method for distribution network considering multiple adjustment measures, including on-load tap changer, capacitor banks, static var compensator, electrical storage system and switch reconfiguration, is proposed on the basis of the branch power flow model. And the non-linear power flow constraint is relaxed by second order cone so that the economical operation optimization model is transformed into the mixed integer second order conic programming model to solve effectively. To solve the uncertainties of load demand and power of distributed generation, two-stage robust optimization method is adopted, and the optimal robust operation strategy of each adjustment measure is obtained through the column and constraint generation algorithm. Finally, the validity of the proposed model is verified by IEEE 33 bus system.

Two-stage layout–size optimization method for prow stiffeners

With the large-scale uncertain renewable energy connected to the grid, the frequency regulation demand is growing day by day, and there is a great need to use energy storage to improve the automatic generation control (AGC) performance of the thermal power to meet the grid frequency regulation requirements. Therefore, a two-stage optimization method considering AGC performance and state of charge (SoC) control for energy storage coordinated with thermal power unit in AGC is proposed in this paper. Firstly, the AGC performance assessment index constraints are linearized, which is used to establish the first stage optimization model of the joint system participation in frequency regulation. Further, to reduce the depth charging/discharging situation of energy storage, the second stage optimization model is constructed with the objective of minimizing the SoC deviation. Finally, the simulation result shows that the proposed two-stage optimization method can improve both AGC performance and sustainability of energy storage, and effectively reduce the loss of energy storage working life.

Community structure based global hub location problem in liner shipping

One of the main challenges in microgrid system energy management is dealing with uncertainties such as the power output from renewable energy sources. The classic two-stage robust optimization (C-TSRO) method was proposed to cope with these uncertainties. However, this method is oriented to the worst-case scenario and is therefore somewhat conservative. In this study, focusing on the energy management of a typical islanded microgrid and considering uncertainties such as the power output of renewable energy sources and the power demand of loads, an expected-scenario-oriented two-stage robust optimization (E-TSRO) method is proposed to alleviate the conservative tendency of the C-TSRO method because the E-TSRO method chooses to optimize the system cost according to the expected scenario instead, while ensuring the feasibility of the first-stage variables for all possible scenarios, including the worst case. According to the structural characteristics of the proposed model based on the E-TSRO method, a column-and-constraint generation (C & CG) algorithm is utilized to solve the proposed model. Finally, the effectiveness of the E-TSRO model and the solution algorithm are analysed and validated through a series of experiments, thus obtaining some important conclusions, i.e., the economic efficiency of system operation can be improved at about 6.7% in comparison with the C-TSRO results.

Time modulated arrays (TMA) suffer from relatively low efficiencies due to radiations at different harmonics (sidebands) of modulation frequency in addition to main frequency. In order to increase this efficiency, optimization algorithms are usually used, but the performance and solution time of the optimizations must be studied. In this study, the two-stage optimization method is proposed for suppressing sidelobe level (SLL) and switching-induced sideband levels (SBL) in the time-modulated linear antenna array. It is shown through a sample scenario that the proposed method gives more effective results than the classical optimization approach.

At present, green manufacturing plays a vital role in the manufacturing industry. In a sense, Integrated Process Planning and Scheduling (IPPS) itself is a complex NP-Complete problem. However, multi-objective problems in green model are more difficult to address. Consequently, solving IPPS problem under green manufacturing environment is a challenging job. The Green Multi-Objective Integrated Process Planning and Scheduling (GMOIPPS) problem is studied in this paper. A mathematical model for GMOIPPS including efficiency objective and energy consumption objective is established. An effective two-stage optimization method is adopted to deal with GMOIPPS problem. The basic NSGA-II algorithm is employed to optimize the flexible process planning stage and provide the near-optimal process plans for job shop scheduling stage dynamically. An improved NSGA-II algorithm (INSGA-II) with N5 neighborhood structure is designed to find the non-dominated scheduling plans in job shop scheduling stage. Three instances with different scales are constructed to verify the validity of the proposed model and optimization method. The experimental results show that the proposed two-stage optimization method can effectively solve the GMOIPPS problem.

The object of the study is intelligent decision support systems. The problem addressed in the research is to improve the accuracy of modeling the functioning process of intelligent decision support systems. A polymodel complex for resource management of intelligent decision support systems has been developed. The originality of the study lies in: – the comprehensive description of the functioning processes of intelligent decision support systems; – the capability to model both an individual process occurring in intelligent decision support systems and to perform comprehensive modeling of the processes taking place within them; – the establishment of conceptual dependencies in the functioning process of intelligent decision support systems. This makes it possible to describe the interaction of individual models at all stages of solving computational tasks; – the description of coordination processes in hybrid intelligent decision support systems, which ensures an increase in the reliability of managerial decision-making; – the modeling of processes for solving complex computational tasks in intelligent decision support systems through the conceptual description of the specified process; – the coordination of computational processes in intelligent decision support systems, which leads to a reduction in the number of computational resources of the systems; – the comprehensive resolution of conflicts through a set of corresponding mathematical models. The proposed polymodel complex is advisable for use in solving the problem of managing intelligent decision support systems characterized by a high degree of complexity

Intelligent decision support (IDS) systems leverage artificial intelligence techniques to generate recommendations that guide human users through the decision making phases of a task. However, a key challenge is that IDS systems are not perfect, and in complex real-world scenarios may produce suboptimal output or fail to work altogether. The field of explainable AI (XAI) has sought to develop techniques that improve the interpretability of black-box systems. While most XAI work has focused on single-classification tasks, the subfield of explainable AI planning (XAIP) has sought to develop techniques that make sequential decision making AI systems explainable to domain experts. Critically, prior work in applying XAIP techniques to IDS systems has assumed that the plan being proposed by the planner is always optimal, and therefore the action or plan being recommended as decision support to the user is always optimal. In this work, we examine novice user interactions with a non-robust IDS system – one that occasionally recommends suboptimal actions, and one that may become unavailable after users have become accustomed to its guidance. We introduce a new explanation type, subgoal-based explanations, for plan-based IDS systems, that supplements traditional IDS output with information about the subgoal toward which the recommended action would contribute. We demonstrate that subgoal-based explanations lead to improved user task performance in the presence of IDS recommendations, improve user ability to distinguish optimal and suboptimal IDS recommendations, and are preferred by users. Additionally, we demonstrate that subgoal-based explanations enable more robust user performance in the case of IDS failure, showing the significant benefit of training users for an underlying task with subgoal-based explanations.

A SDSS combines database storage technologies, geographic information systems (GIS) and decision modeling into tools which can be used to address a wide variety of decision support areas (Eklund, Kirkby, and Pollitt, 1996). Recently, various emerging technologies in computer hardware and software such as speedy microprocessors, gigabit network connections, fast internet mapping servers along with Web-based technologies like extensible markup language (XML), Web services, etc provide promising opportunities to take the traditional spatial decision support systems one step further to provide easy-to-use, round-the-clock access to spatial data and decision support over the Web. Traditional DSS and Web-based spatial DSS can be further improved by integrating expert knowledge and utilizing intelligent software components (such as expert systems and intelligent agents) to emulate the human intelligence and decision making. These kinds of decision support systems are classified as intelligent decision support systems. The objective of this chapter is to discuss the development of an intelligent web-based spatial decision support system and demonstrate it with a case study for planning snow removal operations.

Utilizing plug-in hybrid electric vehicles (PHEVs) is growing fast and booming nowadays. These vehicles, as portable loads and energy sources, may be connected to standard sockets at home. As a result, those extra electrical loads, grid to vehicle, generations, and vehicle to grid, have several impacts on distribution networks, e.g., network energy loss. This paper presents a two-stage optimization method to minimize the energy loss of microgrid with different penetration levels of PHEVs. In the first stage, a novel convex quadratic objective function for active power management of PHEVs is proposed, and daily required energy of PHEVs is calculated based on stochastic model of PHEV owners' behavior. It is supposed that PHEVs can be employed as distributed capacitors. Therefore, reactive power of PHEVs is specified in the second stage. Afterward, the proposed methodology is applied to a realistic distribution network. It will be illustrated that network energy loss is likely to rise considerably in the case of increasing penetration level of PHEVs without smart charging strategy; in order to minimize network energy loss, a smart management scheme will have to be considered. Also, the significant impact of PHEVs' active and reactive power management on energy loss reduction will be demonstrated.

The power to gas (P2G) technology can realize the large-scale storage of electric energy in the form of natural gas, so that the power system and the natural gas system have the ability to flow energy, and the degree of coupling between the power system and the natural gas system is deepened. As the coupling between the power system and the natural gas system continues to increase, an integrated energy system (IES) with the power grid as the core has emerged. However, under uncertain conditions, the current planning methods still cannot guarantee the optimal interests of each manufacturer, so a collaborative planning method for gas-electric systems based on non-cooperative game theory is proposed. With each energy unit and electricity and gas network companies as independent interest groups, and with the maximum annual net income as the optimization goal, establishing a comprehensive non-combination of wind turbines, gas turbines, electric-to-gas turbines, transmission lines and natural gas pipelines in the gas-electric integrated system linear programming model. Aiming at the uncertainty of wind power and load, an improved two-stage robust optimization method is proposed. Finally, the modified IEEE 39-node power system and 20-node natural gas system gas-electric network verify the application value of the model and algorithm.

To design a high-specific-speed axial flow pump with excellent hydraulic performance, this study comprehensively improves the hydraulic performance of the axial flow pump unit using a two-stage iterative orthogonal parameter optimization method while meeting the design requirements. In the design process, by integrating the mixed vortex hypothesis theory, a fourth-order two-segment method and a five-point Bézier curve were used to construct the camber lines of the impeller and guide vane airfoil, respectively. The planar cascade configuration was designed by superimposing the NACA 4-digit series airfoil thickness distribution. The variable projection method was introduced to achieve three-dimensional airfoil parametric modeling, enabling precise control of the circulation distribution by adjusting the projection coefficients. A 9-factor 9-level orthogonal table was used to optimize nine variables, including the cascade density, in the first stage. After four iterations, an initial optimized model was obtained with an efficiency of 85.73%, a head of 4.84 m, and a specific speed of 1028.9. Compared to the initial scheme in the first orthogonal test, which had a hydraulic efficiency of 80.52%, a head of 4.91 m, and a specific speed of 1017.17, the efficiency increased by 5.21%. Based on the first-stage optimization results, a 6-factor 5-level orthogonal table was used for the second-stage optimization of skeletal line control coefficients. After two iterations, the final optimized model was obtained with an efficiency of 86.59%, a head of 4.86 m, and a specific speed of 1025.75, representing an overall efficiency improvement of 6.07% compared to the initial scheme. The study confirms that this multi-parameter collaborative optimization design strategy effectively reduces turbulent entropy generation losses and fully validates the superiority and practicality of this design method and optimization process in improving the performance of axial flow pumps.