Two-stage layout–size optimization method for prow stiffeners

Intelligent decision support system performance depends on knowledge base quality. With the decision problem and knowledge base becoming more complicated, it is necessary to provide effective method to optimize the management of knowledge base. Based on detailed analyzing of running characteristics and potential detects of rule base in IDSS, a novel two-stage optimization method is proposed. Conventional optimization approach and genetic algorithm are combined to recognize and eliminate kinds of defects in rule base. Exact defect definitions and optimization operation details are given. The sample shows that the method is feasible in practice.

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

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

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

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.

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.

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 numerical method of stamp topological optimization taking into account fatigue strength is presented in the work. It is proposed to take into account the restrictions on the stress state in accordance with the curve of the dependence of the maximum stresses on the number of loading cycles in the ESO topological optimization method. An approach to the selection of the evolutionary coefficient with a step-by-step increase in the rejection coefficient is proposed when constructing an iterative scheme for the rejection of elements by the method of topological optimization. The calculation of the stamp optimal topology with a decrease in volume due to the removal and redistribution of material was carried out in the study. The new geometric model of the optimal topology stamp is based on the predicted distribution of elements with a minimum stress level. The verification calculation of the stress state of the stamp of optimal topology with an assessment of fatigue strength was carried out in the work. The numerical calculation was carried out using the finite element method in the Ansys software package. The minimized stamp volume decreased by 35% according to the calculation results. The results of the study can be further applied in the development of topological optimization methods and in the design of stamping tools of optimal topology.

Under the complex operational environment of the antimissile operation, the application of Systems Modeling Language (SysML) parametric model in weapon system effectiveness evaluation is explored. The function of parametric graph model, the construction process of the model and the optimization method of the model are researched. Using feedback mechanism to collect, collate and calculate the parameters of the model, and realize the leap from the index relationship description to the calculation of the index. Finally, the feasibility and effectiveness of the proposed method is verified by using the system of antimissile weapon system as an example.

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.

This paper deals with a single removable repairman in the machine repair problem with warm standbys and working breakdowns. We assume that the repairman can be subject to working breakdowns only when there is at least one failed machine in the system. Applying the matrix-analytic method, we develop the steady-state probabilities of the number of failed machines in the system as well as several system-performance measures. We construct a cost model to compute the optimal threshold N, and the joint optimal values for the fast and slow service rates simultaneously by a two-stage optimization method whose effectiveness is proved by numerical results. Moreover, we also analyze the sensitivity with numerical illustrations based on different system parameter values.

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.

Competition and cooperation in the dual-channel green supply chain with customer satisfaction

Faced with the problem of fossil energy depletion and the power industry’s low-carbon requirements, low-carbon technologies in collaboration with market mechanisms, supplemented by flexible resources, are critical to achieving the low-carbon operation of integrated energy systems (IES). This paper establishes an IES considering multiple types of low-carbon factors and demand response. Firstly, the IES is deemed to participate in the carbon trading market and introduce a ladder-type carbon trading mechanism at the low-carbon policy level. Then, at the low-carbon technology level, carbon capture power plants and power-to-gas equipment are introduced to refine the modeling of the power-to-gas process. Secondly, the integrated energy system includes pluralistic energy storage technology and demand response to increase the IES’s flexibility. Based on multiple types of low carbon factors and demand response mechanisms, the scheduling model for energy efficiency improvement is constructed with the lowest sum of the cost of wind abandonment penalty, the cost of purchasing energy, the cost of equipment operation and maintenance, and the cost of carbon trading as the optimization objective, and solved by a two-stage optimization method. Five energy efficiency indicators are presented to efficiently evaluate dispatching results: wind power consumption rate, carbon trading cost, actual carbon emissions, total cost, and load fluctuation. Finally, according to an arithmetic test system based on various operation scenarios, the proposed model may increase the IES’s comprehensive energy efficiency under the coupling effect of multiple types of low-carbon factors and demand response.