Grid Independent Task Scheduling Multi-Objective Optimization Model and Genetic Algorithm

2010 ◽  
Vol 5 (12) ◽  
Author(s):  
Hai Zhu ◽  
Yuping Wang ◽  
Lei Fan ◽  
Xiaoli Wang
Keyword(s):  
Genetic Algorithm ◽  
Task Scheduling ◽  
Optimization Model ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Independent Task

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.

Adaptive multi-objective traffic signal control using NLRMNSGA-II algorithm

2018 ◽  
Vol 45 (11) ◽  
pp. 973-985
Author(s):  
Yuan-Yang Zou ◽  
Xue-Guo Xu ◽  
Gui-Hua Lin
Keyword(s):  
Genetic Algorithm ◽  
Optimization Model ◽  
Efficient Algorithm ◽  
Numerical Experiments ◽  
Adaptive System ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Multi Objective Optimization ◽  
New Model ◽  
Multi Objective

In this paper, we consider an adaptive system for controlling green times at junction. For this adaptive system, we present a multi-objective optimization model, which is much easier to solve than some existing models. Furthermore, to solve the new model, we suggest an algorithm, called NLRMNSGA-II, which is based on the nonlinear least regression and a modified non-dominated sorting genetic algorithm. Our numerical experiments indicate that the NLRMNSGA-II is an efficient algorithm for the considered adaptive system.

scholarly journals Genetic Algorithm for Solving Multi-Objective Optimization in Examination Timetabling Problem

2021 ◽  
Vol 16 (11) ◽  
pp. 4
Author(s):  
Son Tung Ngo ◽  
Jafreezal B Jaafar ◽  
Izzatdin Abdul Aziz ◽  
Giang Hoang Nguyen ◽  
Anh Ngoc Bui
Keyword(s):  
Genetic Algorithm ◽  
Optimization Model ◽  
Hard Problem ◽  
Multi Objective Optimization ◽  
Time Requirement ◽  
Examination Timetabling ◽  
Education Management ◽  
Timetabling Problem ◽  
Multi Objective ◽  
Np Hard Problem

Examination timetabling is one of 3 critical timetabling jobs besides enrollment timetabling and teaching assignment. After a semester, scheduling examinations is not always an easy job in education management, especially for many data. The timetabling problem is an optimization and Np-hard problem. In this study, we build a multi-objective optimizer to create exam schedules for more than 2500 students. Our model aims to optimize the material costs while ensuring the dignity of the exam and students' convenience while considering the rooms' design, the time requirement of each exam, which involves rules and policy constraints. We propose a programmatic compromise to approach the maximum tar-get optimization model and solve it using the Genetic Algorithm. The results show the effectiveness of the introduced algorithm.

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