Research on Multi-Objective Optimization Model and Its Improved Genetic Algorithm for Emergency Evacuation

2012 ◽  
Vol 7 (1) ◽  
pp. 672-675
Author(s):  
Changxi Ma ◽  
Yinzhen Li ◽  
Ruichun He ◽  
Aixia Diao
Keyword(s):  
Genetic Algorithm ◽  
Optimization Model ◽  
Emergency Evacuation ◽  
Multi Objective Optimization ◽  
Improved Genetic Algorithm ◽  
Multi Objective

scholarly journals Multi-objective Optimization Model of Multi-modal Transport Based on Regional Sustainability Indicators

2020 ◽  
Vol 325 ◽  
pp. 03001
Author(s):  
Shengjiao Yang ◽  
Zuoling Song
Keyword(s):  
Genetic Algorithm ◽  
Optimization Model ◽  
Social Indicators ◽  
Free Trade Area ◽  
Third Party ◽  
Local Optimum ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Freight Forwarder ◽  
Population Genetic Algorithm

With the development of “One Belt, One Road” initiative and free trade area, the volume of cross-border international logistics involving multiple modes of transport has surged. Meanwhile, the proportion of using integrated transportation system in domestic trunk transport has increased. Multi-modal transport (MMT) based on green transport can realize intensive utilization of transport capacity resources, and implement sustainable transport management with three bottom lines of economic, environmental and social aspects. In this paper, the carbon emission index and regional transportation infrastructure utilization index are introduced to construct a multi-objective optimization model with sustainable goals of environmental protection, cost saving and social contribution. The poly-population genetic algorithm (PPGA) is used to overcome the limitation of the traditional genetic algorithm running to the local optimum. The model proposed by this paper quantifies environmental and social indicators, balances comprehensive performance of environment, economy and society, and provides quantitative decision making support for carriers, international freight forwarder or third party logistics to carry out green MMT.

Research on the Multi-Objective Optimization Model of System-Level BIT Testability Index Determination

2011 ◽  
Vol 121-126 ◽  
pp. 2223-2227 ◽  
Author(s):  
Chun Sheng Zhu ◽  
Qi Zhang ◽  
Fan Tun Su ◽  
Hong Liang Ran
Keyword(s):  
Genetic Algorithm ◽  
Life Cycle ◽  
Mathematical Models ◽  
Optimization Model ◽  
System Reliability ◽  
Life Cycle Cost ◽  
System Level ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Life Circle

By weighing reliability, maintainability, availability and life-cycle cost of equipment which are influenced by testability，the testability indexes of system level BIT are determined on the basis of maximum system reliability & maintainability and minimum the life-circle cost. The influence mathematical models of system reliability, maintainability, availability and life-circle cost are established. According to these mathematical models, the multi-objective optimization model of system-level BIT testability indexes is established. The multi-objective optimization model is solved using Non-dominated Sorting Genetic Algorithm II, and the validity of the multi-objective optimization model is proved through an example.

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