Research on Production Cost Optimization of PC Exterior Wall Panel Based on Hybrid Genetic Algorithm

2021 ◽  
Author(s):  
Wei Chen ◽  
Anqi Chen ◽  
Jie Liu ◽  
Shiying Deng
Keyword(s):  
Genetic Algorithm ◽  
Production Cost ◽  
Cost Optimization ◽  
Hybrid Genetic Algorithm ◽  
Exterior Wall ◽  
Wall Panel

A Hybrid Genetic Algorithm for Optimization of Two-Dimensional Cutting-Stock Problem

2012 ◽  
pp. 56-71
Author(s):  
Ahmed Mellouli ◽  
Faouzi Masmoudi ◽  
Imed Kacem ◽  
Mohamed Haddar
Keyword(s):  
Genetic Algorithm ◽  
Linear Programming ◽  
Production Cost ◽  
Programming Model ◽  
Hybrid Genetic Algorithm ◽  
Cutting Stock Problem ◽  
Cutting Stock ◽  
Two Dimensional ◽  
Genetic Approach ◽  
Cutting Pattern

In this paper, the authors present a hybrid genetic approach for the two-dimensional rectangular guillotine oriented cutting-stock problem. In this method, the genetic algorithm is used to select a set of cutting patterns while the linear programming model permits one to create the lengths to produce with each cutting pattern to fulfill the customer orders with minimal production cost. The effectiveness of the hybrid genetic approach has been evaluated through a set of instances which are both randomly generated and collected from the literature.

A New Hybrid Optimization Algorithm and its Application in Job Shop Scheduling

2011 ◽  
Vol 55-57 ◽  
pp. 1789-1793
Author(s):  
Xian Zhou Cao ◽  
Zhen He Yang
Keyword(s):  
Genetic Algorithm ◽  
Production Cost ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Hybrid Genetic Algorithm ◽  
Hybrid Optimization ◽  
Job Shop Scheduling Problem ◽  
Shop Scheduling ◽  
Hybrid Optimization Algorithm ◽  
Active Schedule

In this paper, a dual-resource constrained job shop scheduling problem was studied by designing a hybrid genetic algorithm based on Genetic Algorithm (GA) and Simulated Annealing (SA). GA is used to search for a group of better solutions to the problem of minimizing production cost and then SA is applied to searching them for the best one. The combination of GA and SA utilizes the advantages of the two algorithms and overcomes their disadvantages. The operation-based encoding and an active schedule decoding method were employed. This hybrid genetic algorithm reasonably assigns the resources of machines and workers to jobs and achieves optimum on some performance. The results of numerical simulations, which are compared with those of other well-known algorithms, show better performance of the proposed algorithm.

A Hybrid Genetic Algorithm for Optimization of Two-dimensional Cutting-Stock Problem

2010 ◽  
Vol 1 (2) ◽  
pp. 34-49 ◽  
Author(s):  
Ahmed Mellouli ◽  
Faouzi Masmoudi ◽  
Imed Kacem ◽  
Mohamed Haddar
Keyword(s):  
Genetic Algorithm ◽  
Linear Programming ◽  
Production Cost ◽  
Programming Model ◽  
Hybrid Genetic Algorithm ◽  
Cutting Stock Problem ◽  
Cutting Stock ◽  
Two Dimensional ◽  
Genetic Approach ◽  
Cutting Pattern

In this paper, the authors present a hybrid genetic approach for the two-dimensional rectangular guillotine oriented cutting-stock problem. In this method, the genetic algorithm is used to select a set of cutting patterns while the linear programming model permits one to create the lengths to produce with each cutting pattern to fulfil the customer orders with minimal production cost. The effectiveness of the hybrid genetic approach has been evaluated through a set of instances which are both randomly generated and collected from the literature.

Cost Optimization of Scheduling Based on Simulation

2011 ◽  
Vol 201-203 ◽  
pp. 1039-1043 ◽  
Author(s):  
Ji Li ◽  
Ping An Du
Keyword(s):  
Genetic Algorithm ◽  
Production Cost ◽  
Cost Optimization ◽  
Scheduling Problems ◽  
Production Capability ◽  
Plant Simulation

For most past scheduling problems, research condition is based on enough orders. But on the condition of production capability more than the number of the orders, how to accomplish the limited orders to make the production cost as small as possible is a significantly considered question. Based on Plant Simulation and its Genetic Algorithm, a method of optimizing production cost when scheduling is put forward. This research realizes the relevant scheduling to minimize the production cost and propose a solution for these problems of complex combined cost optimization.

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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