Analysis on the dynamic balance between cultivated land supply and demand at multimeasures based on RS data

Basing on the disequilibrium theory, taking use of SPSS software, taking advantaging of multiple linear regression methodology, this article analyzed the state of supply and demand of newly built commodity-housing market from1998 to 2011 in Xian, constructed the equation about land supply, housing price, load rate and housing-supply, and constructed the equation about per capita disposable income, and housing-demand. Basing on the supply-equation and demand-equation, this article obtained the state of supply and demand of new-built commodity- housing in Xian, at last, calculated the non-equilibrium degree and drew a conclusion.

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A study of multi-objective dynamic water resources allocation modeling of Huai River

Water Science & Technology Water Supply ◽

10.2166/ws.2015.037 ◽

2015 ◽

Vol 15 (4) ◽

pp. 817-824 ◽

Cited By ~ 3

Author(s):

Jing Peng ◽

Ximin Yuan ◽

Lan Qi ◽

Qiliang Li

Keyword(s):

Water Resources ◽

Dynamic Balance ◽

Supply And Demand ◽

Low Flow ◽

Resources Allocation ◽

Time Interval ◽

Water Resources Allocation ◽

Allocation Model ◽

Multi Objective ◽

Huai River

Water resources supply and demand has become a serious problem. Water resources allocation is usually a multi-objective problem, and has been of concern for many researchers. In the north of China, the lack of water resources in the Huai River Basin has handicapped the development of the economy, especially badly in the low-flow period. So it is necessary to study water resources allocation in this area. In this paper, a multi-objective dynamic water resources allocation model has been developed. The developed model took the overall satisfaction of water users in a time interval as the objective function, applied an improved simplex method to solve the calculation, considered the overall users' satisfaction variation with time, and followed the principle that the variation of the system satisfaction within adjacent periods of time must be minimal. The established model was then applied to the Huai River, for the present situation (2010), short-term (2020) and long-term (2030) planning timeframes. From the calculation results, the overall satisfaction in late May and mid September in 2030 was 0.65 and 0.70. After using the model allocation optimization, the overall satisfaction was improved, increasing to 0.78 and 0.79, respectively, thus achieving the dynamic balance optimization of water resources allocation in time and space. This model can provide useful decision support in water resources allocation, when it is used to alleviate water shortages occurring in the low-flow period.

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Ecological balance between supply and demand in Chongqing City based on cultivated land ecological footprint method

Acta Ecologica Sinica ◽

10.5846/stxb201211231662 ◽

2013 ◽

Vol 33 (6) ◽

pp. 1872-1880 ◽

Cited By ~ 5

Author(s):

施开放 SHI Kaifang ◽

刁承泰 DIAO Chengtai ◽

孙秀锋 SUN Xiufeng ◽

左太安 ZUO Taian

Keyword(s):

Ecological Footprint ◽

Supply And Demand ◽

Cultivated Land ◽

Ecological Balance ◽

Chongqing City

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Dynamic Bicycle Dispatching of Dockless Public Bicycle-sharing Systems Using Multi-objective Reinforcement Learning

ACM Transactions on Cyber-Physical Systems ◽

10.1145/3447623 ◽

2021 ◽

Vol 5 (4) ◽

pp. 1-24

Author(s):

Jianguo Chen ◽

Kenli Li ◽

Keqin Li ◽

Philip S. Yu ◽

Zeng Zeng

Keyword(s):

Reinforcement Learning ◽

Dynamic Balance ◽

Supply And Demand ◽

Pareto Frontier ◽

Cyber Physical Systems ◽

Physical Systems ◽

Optimal Dispatch ◽

Multi Objective ◽

Dispatching Algorithm ◽

Multiple Dispatch

As a new generation of Public Bicycle-sharing Systems (PBS), the Dockless PBS (DL-PBS) is an important application of cyber-physical systems and intelligent transportation. How to use artificial intelligence to provide efficient bicycle dispatching solutions based on dynamic bicycle rental demand is an essential issue for DL-PBS. In this article, we propose MORL-BD, a dynamic bicycle dispatching algorithm based on multi-objective reinforcement learning to provide the optimal bicycle dispatching solution for DL-PBS. We model the DL-PBS system from the perspective of cyber-physical systems and use deep learning to predict the layout of bicycle parking spots and the dynamic demand of bicycle dispatching. We define the multi-route bicycle dispatching problem as a multi-objective optimization problem by considering the optimization objectives of dispatching costs, dispatch truck's initial load, workload balance among the trucks, and the dynamic balance of bicycle supply and demand. On this basis, the collaborative multi-route bicycle dispatching problem among multiple dispatch trucks is modeled as a multi-agent and multi-objective reinforcement learning model. All dispatch paths between parking spots are defined as state spaces, and the reciprocal of dispatching costs is defined as a reward. Each dispatch truck is equipped with an agent to learn the optimal dispatch path in the dynamic DL-PBS network. We create an elite list to store the Pareto optimal solutions of bicycle dispatch paths found in each action, and finally get the Pareto frontier. Experimental results on the actual DL-PBS show that compared with existing methods, MORL-BD can find a higher quality Pareto frontier with less execution time.

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