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The key to solving the problem of redundant financial indicators in addressing financial warning issues is to reduce the dimensionality of the original financial indicators. This paper proposes a model based on the whale optimization algorithm with mixed strategy (IWOA) combined with support vector machine (SVM), namely, the IWOA-SVM early warning model, which simultaneously performs index optimization and dimensionality reduction, and financial risk early warning identification. This paper takes a total of 302 enterprises specially treated in Shanghai and Shenzhen stock exchanges and normal enterprises of the same specification as the research samples to design the model. The results show that the improved whale optimization algorithm has better optimization speed and accuracy and improves the search ability of the original algorithm for the optimal solution. Compared with other dimensionality reduction methods, the IWOA-SVM model has the lowest index dimension after dimensionality reduction and has more excellent recognition effect. The dimensionality reduction results have certain universality for different classifiers, which provides a new idea for the selection of indicators for financial early warning. [ABSTRACT FROM AUTHOR]

This paper proposed the mathematical model with the goal of minimizing transportation costs and vehicle fixed costs which based on the urban waste classification collection and transportation problem, considering the vehicle loading capacity constraints, hard time window constraints, and the impact of loading rate on cost. Dynamic fusion of an improved ant colony algorithm that considered the time fit factor and the vehicle capacity utilization factor and chaotic electromagnetic field optimization algorithm, combined with 2-opt and two-point exchange local search method, this paper proposed a novel hybrid ant colony algorithm with improved ant colony algorithm as the external framework and chaotic electromagnetic field optimization algorithm as the internal module to solve the urban waste classification collection and transportation problem. According to the idea of complementary advantages between algorithms, using the advantages of the two algorithms to make up for the defects of a single algorithm, it successfully applied to this problem. Finally, it used the vehicle routing problem standard test set and data from Yangpu district, Shanghai as examples to test and compare, verifies the correctness of the model and the effectiveness and optimization capabilities of the algorithm. [ABSTRACT FROM AUTHOR]

In this paper, based on the existing research, the rural talent supply situation is analyzed. Rural talent relative to the existing rural labor force has a higher human capital of the labor force. We also discuss the part of moving workers who can be considered as talents. Financial dimension is mostly through literature collection and statistical data finding and computational calculation, according to the results of communicate indicators, to reflect its performance level. The performance analysis of organizational management dimension and institutional development dimension mainly focuses on qualitative analysis and comparative analysis, and then we summarize and evaluate the performance status of Shanghai basic endowment insurance system for urban and rural residents. Therefore, all countries in the world are constantly exploring and trying to improve the performance management model of their social security institutions. Through the evaluation and analysis of the above four dimensions, it can be found that the overall performance level of urban and rural residential insurance in Shanghai is relatively high, but there are still some problems in the operation process. [ABSTRACT FROM AUTHOR]

The problem of the optimal three-level location allocation of transfer center, processing factory and distribution center for supply chain network under uncertain transportation cost and customer demand are studied. We establish a two-stage fuzzy 0-1 mixed integer optimization model, by considering the uncertainty of the supply chain. Given the complexity of the model, this paper proposes a modified hybrid second order particle swarm optimization algorithm (MHSO-PSO) to solve the resulting model, yielding the optimal location and maximal expected return of supply chain simultaneously. A case study of clothing supply chain in Shanghai of China is then presented to investigate the specific influence of uncertainties on the transfer center, clothing factory and distribution center three-level location. Moreover, we compare the MHSO-PSO with hybrid particle swarm optimization algorithm and hybrid genetic algorithm, to validate the proposed algorithm based on the computational time and the convergence rate. [ABSTRACT FROM AUTHOR]

The objective of this paper is to optimize the passive design of newly-built residential buildings in hot summer and cold winter region of China for improving indoor thermal comfort while reducing building energy demand. In this respect, this paper investigates the performance of a representative apartment building in the city of Shanghai and evaluates the optimum solutions by using a developed optimization approach, which includes three major steps of 1) setting the model for multi-objective optimization, 2) sensitivity analysis for reducing the dimension of input variables, and 3) multi-objective optimization by using the Non-dominated Sorting Genetic Algorithm II (NSGA-II) coupled with the Artificial Neural Network (ANN), among which a novel indicator for evaluating the annual indoor thermal comfort of residential buildings of Shanghai named Comfort Time Ratio (CTR) is defined based on the modification of Szokolay’s theory in terms of bioclimatic analysis, and the impacts of passive design variables on the indoor thermal comfort and building energy demand in terms of different directions are comprehensively investigated. Results of the multi-objective optimization indicate that the residential buildings of Shanghai have a great potential in comfort-improvement and energy-saving. A series of novel optimal passive design tactics for residential buildings in Shanghai are derived accordingly which could be easily understood and conveniently carried out by the architects in practice. [ABSTRACT FROM AUTHOR]

Timetable optimization techniques offer opportunity for saving energy and hence reducing operational costs for high-speed rail services. The existing energy-saving timetable optimization is mainly concentrated on the train running state adjustment and the running time redistribution between two stations. Not only the adjustment space of timetables is limited, but also it is hard for the train to reach the optimized running state in reality, and it is difficult to get feasible timetable with running time redistribution between two stations for energy-saving. This paper presents a high-speed railway energy-saving timetable based on stop schedule optimization. Under the constraints of safety interval and stop rate, with the objective of minimizing the increasing energy consumption of train stops and the shortest travel time of trains, the high-speed railway energy-saving timetable optimization model is established. The fuzzy mathematics programming method is used to design an efficient algorithm. The proposed model and algorithm are demonstrated in the actual operation data of Beijing-Shanghai high-speed railway. The results show that the total operating energy consumption of the train is reduced by 3.7%, and the total travel time of the train is reduced by 11 minutes. [ABSTRACT FROM AUTHOR]

Operational performance optimization of signalized intersections is one of the most important tasks for traffic engineers and researchers. To compensate for the limitations of practical implementation, simulation software packages have been widely used to evaluate different optimization strategies and thus to improve the efficiency of the intersections as well as the entire network. However, for the existing optimization studies on signalized intersections, the relationships among various optimization measures and the combination of strategies have not been fully investigated. In this paper, uniform design experimentation was introduced to combine different optimization measures into strategies and achieve the minimum time cost in model construction. VISSIM software package was then calibrated and used to evaluate various optimization strategies and identify the one with the best measurement of performance, namely, control delay at the signalized intersection. By taking a representative congested intersection in Shanghai as a case study, the optimal strategy was identified to reduce the overall control delay by 27.3%, which further verified the modeling capability of the proposed method. [ABSTRACT FROM AUTHOR]

Thermal Energy Storage (TES) systems in Building Combined Cooling, Heating and Power (BCHP) plants are attracting more attentions nowadays in China. In this paper, a simple linear programming optimization model is developed for BCHP systems with an Ice Storage System (ISS), a hybrid Electric Chiller (EC) and Absorption Chiller (AC). Two BCHP systems (one with TES and the other without TES) are compared with the conventional system in a super high-rise building in Shanghai city. The design and operation of both BCHP systems are optimized with the linear programming tool. The results show that natural gas price is the most important factor regarding the economic performance of the BCHP plants, followed by electricity utility price and the price of the prime motor. In this case study, both BCHP options show higher energy use efficiency and better economic performance than the conventional solution. Compared with the BCHP system without TES, the one with TES has similar energy efficiency level, but lower operation cost due to its load shifting capability. Thus, considering the overall life cycle cost, the BCHP system with TES option is suggested to be used in similar high-rise buildings in Shanghai city. [ABSTRACT FROM AUTHOR]

• A model predictive control based optimization framework for heat pump system of a residential district is proposed. • Heating load and indoor state is predicted by data-driven model as the input of the optimization process. • Data-driven models and traditional fitting models for system components are combined in the optimization process. • Onsite validation is conducted. Heat pump system is widely used in district heating because of its high energy efficiency and economic benefits. Because of the complexity of large heat pump systems, achieving optimal operation in practical project remains challenging. Model predictive control is a promising method for operation optimization. This paper represents a data-driven optimization framework based on model predictive control for optimal district energy supply. Four sub-models of the framework including heating load prediction model, heat pump performance model, main pump performance model and indoor state prediction model were built via machine learning and fitting methods. The particle swarm optimization algorithm was adopted to determine the optimal operation strategy. The framework' effectiveness was verified by the operation data of a district energy system in Shanghai by means of both offline and onsite validation. The optimization results under low load conditions show that the energy-saving ratio can reach 9.43%, correspondingly, 12.37% for medium load conditions and 4.50% for high load conditions. In onsite validation, the Mean Absolute Percentage Error and Root Mean Square Error of heating load prediction are 6.72% and 104.21 kW, respectively. Compared with the day under similar weather conditions, the framework helps operators to obtain 12.9% energy-saving ratio in practice. [ABSTRACT FROM AUTHOR]