Performing multiobjective optimization on perforated plate matrix heat exchanger surfaces using genetic algorithm

John, Anish K.; Krishnakumar, K.

doi:10.1051/smdo/2016011

Cited by 7 publications

(4 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The agricultural supply chain network model studied in this paper involves many parameters, and GA has the advantages of strong search ability and fast solution [14], which has applications in many problems such as engineering parameter optimization [15] and structural topology optimization [16]. Therefore, GA is used to solve the model in this paper.…”

Section: Model Solving Based On An Improved Genetic Algorithmmentioning

confidence: 99%

Optimization of the supply chain network planning problem using an improved genetic algorithm

Zhao,

Xie

2023

Int. J. Simul. Multidisci. Des. Optim.

View full text Add to dashboard Cite

The planning problem of supply chain network is highly related to logistics cost and product quality. In this paper, for the optimization of supply chain network planning problem, an agricultural product supply chain network under the direct docking model between farmers and supermarkets was taken as an example to establish an agricultural product supply chain network planning model with the lowest cost as the objective. Then, an improved genetic algorithm (GA) was designed to solve the model. The analysis of the arithmetic example showed that compared with the traditional GA, the total cost obtained by the improved GA was lower, at 39,004.48 $, which was 6.5% less than that of the traditional GA; the solution result of the improved GA was also superior to that of other heuristic algorithms, such as particle swarm optimization and ant colony optimization. The experimental results demonstrate the optimization effectiveness of the improved GA for the supply chain network planning problem, and it can be applied in practice.

show abstract

Section: Model Solving Based On An Improved Genetic Algorithmmentioning

confidence: 99%

Optimization of the supply chain network planning problem using an improved genetic algorithm

Zhao,

Xie

2023

Int. J. Simul. Multidisci. Des. Optim.

View full text Add to dashboard Cite

show abstract

“…Solutions are obtained from population and utilized to form a new population. This process continues until the best solution is produced or until the number of population is determined [31][32][33][34][35][36][37][38].…”

Section: Genetic Algorithmmentioning

confidence: 99%

Optimization of Project Scheduling Activities in Dynamic CPM and PERT Networks Using Genetic Algorithms

Calp¹,

Akçayol²

2018

SDÜ Fen Bil Enst Der

View full text Add to dashboard Cite

Projects consist of interconnected dimensions such as objective, time, resource and environment. Use of these dimensions in a controlled way and their effective scheduling brings the project success. Project scheduling process includes defining project activities, and estimation of time and resources to be used for the activities. At this point, the project resource-scheduling problems have begun to attract more attention after Program Evaluation and Review Technique (PERT) and Critical Path Method (CPM) are developed one after the other. However, complexity and difficulty of CPM and PERT processes led to the use of these techniques through artificial intelligence methods such as Genetic Algorithm (GA). In this study, an algorithm was proposed and developed, which determines critical path, critical activities and project completion duration by using GA, instead of CPM and PERT techniques used for network analysis within the scope of project management. The purpose of using GA was that these algorithms are an effective method for solution of complex optimization problems. Therefore, correct decisions can be made for implemented project activities by using obtained results. Thus, optimum results were obtained in a shorter time than the CPM and PERT techniques by using the model based on the dynamic algorithm. It is expected that this study will contribute to the performance field (time, speed, low error etc.) of other studies. Özet: Projeler; amaç, zaman, kaynak ve çevre olmak üzere birbirleriyle ilişkili birtakım boyutlardan oluşmaktadır. Bu boyutların kontrollü kullanılması ve etkin planlanması proje başarısını getirmektedir. Proje planlama süreci, proje faaliyetlerinin tanımlanması ve projedeki faaliyetler için zaman ve kaynak tahmini yapılması süreçlerini kapsar. Bu noktada, proje kaynak planlama problemleri, Program Değerlendirme ve Gözden Geçirme Tekniği (PERT) ve Kritik Yol Metodu (CPM) birbiri ardına geliştirildikten sonra daha fazla dikkat çekmiştir. Bununla birlikte, CPM ve PERT işlemlerinin karmaşıklığı ve zorluğu, bu teknikleri Genetik Algoritma (GA) gibi yapay zeka yöntemleri ile kullanmaya itmiştir. Bu çalışmada, proje yönetimi kapsamında şebeke analizi için kullanılan CPM ve PERT tekniklerinin yerine, GA kullanılarak kritik yol, kritik faaliyet ve proje tamamlanma süresini belirleyen bir algoritma önerilmiş ve geliştirilmiştir. GA kullanılmasının amacı, bu algoritmaların karmaşık optimizasyon problemlerin çözümünde etkili bir yöntem olmasıdır. Böylece, elde edilen sonuçlar kullanılarak gerçekleştirilecek proje faaliyetleriyle ilgili doğru kararlar alınabilmektedir. Nitekim, geliştirilen dinamik algoritma ile CPM ve PERT tekniklerinden daha kısa sürede optimum sonuçlara ulaşılmıştır. Çalışmanın diğer çalışmaların performans alanına (zaman, hız, düşük hata vb.) katkı sağlaması beklenmektedir.

show abstract

“…Various researchers have employed methods which include experimental, statistical, and analytical approach in posture prediction of human with various degrees of freedom [3][4][5][6][7][8][9][10][11][12][13] for modeling. Inverse dynamics [3] and image processing tools [4] are also used by various researchers to predict posture and optimization using GA [9][10][11], ANN [15], MOO [16][17][18][19][20][21][22][23] to attain the posture prediction of human.…”

Section: Introductionmentioning

confidence: 99%

Posture prediction and optimization for a manual assembly operation involving lifting of weights

Rout

Dash

Dhupal

2020

Int. J. Simul. Multidisci. Des. Optim.

View full text Add to dashboard Cite

The present work combines ergonomics with the posture prediction in the assembly process to avoid musculoskeletal issues of human operator. For improved productivity the operator should be in a better work environment and in sound health. The purpose of this paper is to provide a different perspective to avoid ergonomic risk factors in manual assembly. Here, a human is modeled as 20-DOF as modeled in robotic analysis and simulated in a virtual environment. In the present study, two objective cost functions i.e. joint discomfort function and energy expenditure function have been employed for evaluating the optimized posture. For posture prediction, a combined multi-objective optimization (MOO) method is used and the objective cost functions are minimized i.e. less joint discomfort and less energy in MOO method required to do the manual assembly operation and consequently, the results are compared and finally the movements are tested using REBA technique.

show abstract

Performing multiobjective optimization on perforated plate matrix heat exchanger surfaces using genetic algorithm

Cited by 7 publications

References 16 publications

Optimization of the supply chain network planning problem using an improved genetic algorithm

Optimization of the supply chain network planning problem using an improved genetic algorithm

Optimization of Project Scheduling Activities in Dynamic CPM and PERT Networks Using Genetic Algorithms

Posture prediction and optimization for a manual assembly operation involving lifting of weights

Contact Info

Product

Resources

About