scholarly journals Customized bus passenger boarding and deboarding planning optimization model with the least number of contacts between passengers during COVID-19

2021 ◽  
pp. 126244
Author(s):  
Feng Chen ◽  
Haorong Peng ◽  
Wenlong Ding ◽  
Xiaoxiang Ma ◽  
Daizhong Tang ◽  
...  
Keyword(s):  
Optimization Model ◽  
Planning Optimization

Generation Resource Planning Optimization Model under Emission Constraint

2013 ◽  
Vol 772 ◽  
pp. 868-871
Author(s):  
Sun Lei ◽  
Zhong Fu Tan ◽  
Li Wei Ju ◽  
He Yin ◽  
Zhi Hong Chen
Keyword(s):  
Energy Consumption ◽  
Energy Saving ◽  
Optimization Model ◽  
Resource Planning ◽  
Power Industry ◽  
Pollutant Emissions ◽  
Total System ◽  
Planning Optimization ◽  
Pollutants Emission ◽  
Generation Capacity

As a large energy consumption and pollutants emission department, power industry energy saving and emission reduction is of great significance for the community's overall energy consumption and pollutants emission control. As the main energy saving measures of the power industry, optimize the structure of power based on grid-connected renewable energy. Therefore, this article is based on energy distribution in China, considering load, electricity, resources, environmental pollution and the unit served, target is the total system power generation installed capacity and pollutant emissions at minimum costs, to construct generation resource planning optimization model under emission constraint, install generation capacity costs, running costs and the cost of pollutant emissions will be take into account, in order to make reasonable recommendations on power resource planning in China.

Regional Power Planning Optimization Model and Scenario Analysis Considering the Environmental Cost

2013 ◽  
Vol 805-806 ◽  
pp. 1122-1128
Author(s):  
Zong Wu Wang ◽  
Guo He Huang ◽  
Xiao Kun Li
Keyword(s):  
Linear Programming ◽  
Scenario Analysis ◽  
Pollution Control ◽  
Optimization Model ◽  
Programming Model ◽  
Environmental Cost ◽  
Mixed Integer ◽  
Regional Power ◽  
Planning Optimization ◽  
Decision Alternatives

In this study, a regional power planning optimization model (RPPOM) is developed considering the environmental cost and the restriction of resource and environment, based on interval linear programming and mixed integer linear programming. Model is applied to a case study on the power planning in Henan province, and scenario analysis is conducted. Interval solutions associated with scenario of pollution control have been obtained. They can be used for generating decision alternatives and helping decision makers identify desired power policies for power planning to meet the growth in electricity demand considering the constraints of resources and environment with a minimized system cost. Scenario analysis of environmental pollution control at different levels can also be tackled.

Prediction-Based Multi-Objective Optimization for Oil Purchasing and Distribution with the NSGA-II Algorithm

2016 ◽  
Vol 15 (02) ◽  
pp. 423-451 ◽  
Author(s):  
Lean Yu ◽  
Zebin Yang ◽  
Ling Tang
Keyword(s):  
Information Technology ◽  
Optimization Model ◽  
Profit Maximization ◽  
Optimization Approach ◽  
Risk Minimization ◽  
Nsga Ii ◽  
Training Time ◽  
Multi Objective ◽  
Planning Optimization ◽  
Oil Markets

Due to the uncertainty in oil markets, this paper proposes a novel approach for oil purchasing and distribution optimization by incorporating price and demand prediction, i.e., the prediction-based oil purchasing-and-distribution optimization model. In particular, the proposed method bridges the latest information technology and decision-making technique by introducing the recently proposed information technology (i.e., extreme learning machine (ELM)) into the oil purchasing-and-distribution optimization model. Two main steps are involved: market prediction and planning optimization in the proposed model. In market prediction, the ELM technique is employed to provide fast training time and accurate forecasting results for oil prices and demands. In planning optimization, two objectives of general profit maximization and inventory risk minimization are considered; and the most popular multi-objective evolutionary algorithm (MOEA), nondominated sorting genetic algorithm II (NSGA-II), is implemented to search approximate Pareto optimal solutions. For illustration and verification, the motor gasoline market in the US is focused on as the study sample, and the experimental results demonstrate the superiority of the proposed prediction-based optimization approach over its benchmark models (without market prediction and/or planning optimization), in terms of the highest profit and the lowest risk.

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