Multiobjective Optimization of Hybrid Electrical Vehicle Powertrain Mounting System Using Hybrid Genetic Algorithm

2011 ◽  
Vol 87 ◽  
pp. 30-37 ◽  
Author(s):  
Jian Feng He ◽  
Xiao Xiong Jin
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Multiobjective Optimization ◽  
Energy Distribution ◽  
Reaction Force ◽  
Hybrid Genetic Algorithm ◽  
Electrical Vehicle ◽  
Distribution Matrix ◽  
Vehicle Powertrain ◽  
Hybrid Electrical Vehicle

Powertrain mounting system of a Hybrid Electrical Vehicle (HEV) is analyzed and researched, the expression of energy distribution matrix and that of mounting reaction force are derived, and mathematical model of the system is established in Matlab. Correctness of the model established is tested and verified through model establishing for simulation and calculation in ADAMS. Features of Hybrid Genetic Algorithm (HGA) for multiobjective optimization are analyzed and researched, model for calculation of multiobjective optimization using Hybrid Genetic Algorithm is established, targets for optimization of the system are determined, and optimization is executed based on the mounting stiffness parameters. The result that the system is optimized apparently by Hybrid Genetic Algorithm is revealed through contrast of the energy distribution matrix and mounting reaction force of pre and post-optimization.

Estimation of Dielectric Response Parameters of Transformers Using Hybrid Genetic Algorithm

2012 ◽  
Vol 571 ◽  
pp. 579-583
Author(s):  
Tao Zhang ◽  
Dong Hui Li ◽  
Fei Lin Ma ◽  
Hua Jun Ran
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Equivalent Circuit ◽  
Dielectric Response ◽  
Hybrid Genetic Algorithm ◽  
Initial Slope ◽  
Characteristic Quantity ◽  
Equivalent Circuit Parameters ◽  
Polarization Characteristics ◽  
The Relationship

It is important to reveal the relationship between the characteristic quantity of return voltage and polarization characteristics of dielectric in theory. In recent years, some researchers have proposed some equivalent circuit models to study the relationship between the equivalent circuit and the characteristic quantity of return voltage. However, seldom shows how the mathematical model of equivalent circuit parameters estimated is established and these parameters are estimated. The initial slope of return voltage is of great significance to identify the parameters of equivalent circuit based on linear dielectric response theory. Therefore, this paper establishes a mathematical model with the data of initial slope of return voltage for estimating its parameter, and then, a hybrid genetic algorithm is used for solving the problem. The result shows that the model established is feasible and effective.

Multi-Depot Vehicle Routing Problem with Hybrid Genetic Algorithm

2012 ◽  
Vol 479-481 ◽  
pp. 555-560 ◽  
Author(s):  
Li Wei Dang ◽  
Xiao Ming Sun
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Genetic Algorithms ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Hybrid Genetic Algorithm ◽  
Routing Problem ◽  
Transport Distance ◽  
Delivery Costs ◽  
Two Factors

About the multi-depot vehicle routing problem, considering the transport distance and the number of dispatching vehicles together can effectively reduce the total delivery costs. Firstly establish the corresponding mathematical model by taking the two factors into account. Secondly solve the model by using hybrid genetic algorithms. Thirdly demonstrate the effectiveness of the model and algorithm by an example

scholarly journals Hybrid genetic algorithm-based optimisation of the batch order picking in a dense mobile rack warehouse

PLoS ONE ◽  
2021 ◽  
Vol 16 (4) ◽  
pp. e0249543
Author(s):  
Jianglong Yang ◽  
Li Zhou ◽  
Huwei Liu
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Hierarchical Clustering ◽  
Hybrid Genetic Algorithm ◽  
Order Picking ◽  
Location Information ◽  
Storage Space ◽  
Time Cost ◽  
Solution Algorithms ◽  
Multiple Orders

The utilization of a storage space can be considerably improved by using dense mobile racks. However, it is necessary to perform an optimisation study on the order picking to reduce the time cost as much as possible. According to the channel location information that needs to be sorted, the multiple orders are divided into different batches by using hierarchical clustering. On this basis, a mathematical model for the virtual order clusters formed in the batches is established to optimize the order cluster picking and rack position movement, with the minimum picking time as the objective. For this model, a hybrid genetic algorithm is designed, and the characteristics of the different examples and solution algorithms are further analysed to provide a reference for the solution of the order picking optimisation problem in a dense mobile rack warehouse.

A Hybrid Genetic Algorithm for Production Capacity Allocation Problem with Multiple Regional Demands in Supply Chain

2014 ◽  
Vol 945-949 ◽  
pp. 3107-3111
Author(s):  
Zhen Wang ◽  
Lei Huang
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Supply Chain ◽  
Hybrid Genetic Algorithm ◽  
Production Capacity ◽  
Experimental Tests ◽  
Capacity Allocation ◽  
Allocation Problem ◽  
Proposed Model

Concentrating on the supplier with limited production capacity in supply chain, this paper established a mathematical model for production capacity allocation problem with consideration of multiple regional demands. The genetic algorithm is employed as solution mainframe in which a heuristics rule is developed to initiate the population and an elite pool is adopted to store those solutions with outstanding fitness values. The experimental tests show that the proposed model and algorithm are feasible and effective.

scholarly journals A Hybrid Genetic Algorithm for Nurse Scheduling Problem considering the Fatigue Factor

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Atefeh Amindoust ◽  
Milad Asadpour ◽  
Samineh Shirmohammadi
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Hybrid Genetic Algorithm ◽  
Real Case ◽  
Scheduling Problem ◽  
Nurse Scheduling ◽  
Proposed Model ◽  
The World ◽  
New Situation

Nowadays and due to the pandemic of COVID-19, nurses are working under the highest pressure benevolently all over the world. This urgent situation can cause more fatigue for nurses who are responsible for taking care of COVID-19 patients 24 hours a day. Therefore, nurse scheduling should be modified with respect to this new situation. The purpose of the present research is to propose a new mathematical model for Nurse Scheduling Problem (NSP) considering the fatigue factor. To solve the proposed model, a hybrid Genetic Algorithm (GA) has been developed to provide a nurse schedule for all three shifts of a day. To validate the proposed approach, a randomly generated problem has been solved. In addition, to show the applicability of the proposed approach in real situations, the model has been solved for a real case study, a department in one of the hospitals in Esfahan, Iran, where COVID-19 patients are hospitalized. Consequently, a nurse schedule for May has been provided applying the proposed model, and the results approve its superiority in comparison with the manual schedule that is currently used in the department. To the best of our knowledge, it is the first study in which the proposed model takes the fatigue of nurses into account and provides a schedule based on it.

A Hybrid Genetic Algorithm for Solving a Type of Scheduling Problem

2013 ◽  
Vol 380-384 ◽  
pp. 1710-1715
Author(s):  
Meng Lan Wang
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Optimization Algorithm ◽  
Hybrid Genetic Algorithm ◽  
Scheduling Problem ◽  
Tasks Scheduling ◽  
General Mathematical Model ◽  
Simulation Results

Genetic algorithm is the most widely used and successful bionic optimization algorithm. In this paper we will discuss the tasks scheduling problem on equipments, establish a general mathematical model and put forward a hybrid genetic algorithm to solve this problem. The simulation results show the effectiveness of the hybrid genetic algorithm.

scholarly journals A Dynamic Scheduling Method for Logistics Tasks Oriented to Intelligent Manufacturing Workshop

2019 ◽  
Vol 2019 ◽  
pp. 1-18 ◽  
Author(s):  
Wenxiang Xu ◽  
Shunsheng Guo ◽  
Xixing Li ◽  
Chen Guo ◽  
Rui Wu ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Dynamic Scheduling ◽  
Hybrid Genetic Algorithm ◽  
Intelligent Manufacturing ◽  
Automated Guided Vehicles ◽  
Scheduling Problem ◽  
Scheduling Model ◽  
Intelligent Logistics ◽  
Simultaneous Scheduling

Aiming at the logistics dynamic scheduling problem in an intelligent manufacturing workshop (IMW), an intelligent logistics scheduling model and response method with Automated Guided Vehicles (AGVs) based on the mode of “request-scheduling-response” were proposed, and they were integrated with Internet of Things (IoT) to meet the demands of dynamic and real time. Correspondingly, a mathematical model was developed and integrated with a double-level hybrid genetic algorithm and ant colony optimization (DLH-GA-ACO) to minimize the finish time with the minimum AGVs and limited time. The mathematical model optimized the logistics scheduling process on two dimensions which include the sequence of tasks assigned to an AGV and the matching relation between transfer tasks and AGVs (AGV-task). The effectiveness of the model was verified by a set of experiments, and comparison among DLH-GA-ACO, hybrid genetic algorithm and particle swarm optimization (H-GA-PSO), and tabu search algorithm (TSA) was performed. In the experiments, the DLH-GA-ACO ran in a distributed environment for a faster computing speed. According to the comparisons, the superiority and effectiveness of DLH-GA-ACO on dynamic simultaneous scheduling problem were proved and the intelligent logistics scheduling model was also proved to be an effective model.

Design of GA and Ontology based NLP Frameworks for Online Opinion Mining

2019 ◽  
Vol 13 (2) ◽  
pp. 159-165
Author(s):  
Manik Sharma ◽  
Gurvinder Singh ◽  
Rajinder Singh
Keyword(s):  
Genetic Algorithm ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Opinion Mining ◽  
Hybrid Genetic Algorithm ◽  
Online Reviews ◽  
Middle Tier ◽  
Complete Set ◽  
Mining Model

Background: For almost every domain, a tremendous degree of data is accessible in an online and offline mode. Billions of users are daily posting their views or opinions by using different online applications like WhatsApp, Facebook, Twitter, Blogs, Instagram etc. Objective: These reviews are constructive for the progress of the venture, civilization, state and even nation. However, this momentous amount of information is useful only if it is collectively and effectively mined. Methodology: Opinion mining is used to extract the thoughts, expression, emotions, critics, appraisal from the data posted by different persons. It is one of the prevailing research techniques that coalesce and employ the features from natural language processing. Here, an amalgamated approach has been employed to mine online reviews. Results: To improve the results of genetic algorithm based opining mining patent, here, a hybrid genetic algorithm and ontology based 3-tier natural language processing framework named GAO_NLP_OM has been designed. First tier is used for preprocessing and corrosion of the sentences. Middle tier is composed of genetic algorithm based searching module, ontology for English sentences, base words for the review, complete set of English words with item and their features. Genetic algorithm is used to expedite the polarity mining process. The last tier is liable for semantic, discourse and feature summarization. Furthermore, the use of ontology assists in progressing more accurate opinion mining model. Conclusion: GAO_NLP_OM is supposed to improve the performance of genetic algorithm based opinion mining patent. The amalgamation of genetic algorithm, ontology and natural language processing seems to produce fast and more precise results. The proposed framework is able to mine simple as well as compound sentences. However, affirmative preceded interrogative, hidden feature and mixed language sentences still be a challenge for the proposed framework.

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