A perspective of ecological civilization: research on the spatial coupling and coordination of the energy-economy-environment system in the Yangtze River Economic Belt.

The concept of sustainable economic development as the core promotes socio-economic progress and rapid economic development. Nevertheless, in the new era of China, the contradiction between socio-economic development and environmental sustainability is still prominent. In order to deal with the coordinated development relationship among regional economy, energy, and environment, this paper constructs an energy-economy-environment (3E) index system from the perspective of green development and ecological integrity. On this basis, the distance-based coupling coordinated degree (CCD) model and dynamically comprehensive coordination degree model were used to evaluate the coupling coordinated development levels of 11 provinces (cities) along the Yangtze River Economic Belt (YREB) from 2008 to 2017. Then, a spatial econometric model was used to explore the spatial effects of the regional 3E system of the YREB. The results indicated that (1) the overall coordination of the 3E system in the provinces (cities) along the YREB shows an increasing trend. However, change paths varied with the provinces (cities) due to the influence of regional policies; (2) the CCD of the 3E system in the YREB differed spatially significantly, showing a stepped distribution of "high in the east and low in the west"; (3) the 3E system in the YREB had a positive spatial correlation and showed the characteristics of aggregation, with Jiangsu and Zhejiang provinces mainly showing "high-high" agglomeration and Sichuan showing "high-low" aggregation, while the rest of the provinces had the characteristics of random distribution in terms of spatial effects. The research process and results show that combining the ecological civilization concept and the CCD model can provide the vision and indicators for evaluating and researching the regional 3E system.

Multi-objective AGV scheduling in an automatic sorting system of an unmanned (intelligent) warehouse by using two adaptive genetic algorithms and a multi-adaptive genetic algorithm.

Automated guided vehicle (AGV) is a logistics transport vehicle with high safety performance and excellent availability, which can genuinely achieve unmanned operation. The use of AGV in intelligent warehouses or unmanned warehouses for sorting can improve the efficiency of warehouses and enhance the competitiveness of enterprises. In this paper, a multi-objective mathematical model was developed and integrated with two adaptive genetic algorithms (AGA) and a multi-adaptive genetic algorithm (MAGA) to optimize the task scheduling of AGVs by taking the charging task and the changeable speed of the AGV into consideration to minimize makespan, the number of AGVs used, and the amount of electricity consumption. The numerical experiments showed that MAGA is the best of the three algorithms. The value of objectives before and after optimization changed by about 30%, which proved the rationality and validity of the model and MAGA.