The Industry Assembly Line Layout’s Modeling and Simulation in Industry Manufacturing Based on Genetic Algorithm

2013 ◽  
Vol 675 ◽  
pp. 3-7
Author(s):  
Fang Guo ◽  
Zhi Hong Huang
Keyword(s):  
Genetic Algorithm ◽  
Modeling And Simulation ◽  
Equilibrium Problem ◽  
Assembly Line ◽  
Simulation Software ◽  
Heuristic Procedure ◽  
Plant Simulation ◽  
Line Design

The equilibrium problem is one important aspect of industry assembly line design. This paper puts forward the method to solve the industry assembly line’s equilibrium problem based on the genetic algorithm’s heuristic procedure and on this basis it also optimizes the industry assembly line’s layout and synthetically considers the material carrying cost, plant area’s use ratio and other factors in industry manufacturing. Then it optimizes by eM-Plant simulation software and combining with genetic algorithm to efficiently acquire visual and satisfying layout effects. At last, it uses examples of industry assembly line to verify this method’s feasibility.

Simulated Annealing Genetic Algorithm and its Application in Mixed-Model Assembly Line Design

2010 ◽  
Vol 136 ◽  
pp. 64-68 ◽  
Author(s):  
Yan Jiang ◽  
Xiang Feng Li ◽  
Dun Wen Zuo ◽  
Guang Ming Jiao ◽  
Shan Liang Xue
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Local Search ◽  
Assembly Line ◽  
Mixed Model ◽  
Simulated Annealing Genetic Algorithm ◽  
Simple Genetic Algorithm ◽  
Mixed Model Assembly Line ◽  
Line Design ◽  
Mixed Model Assembly

Simple genetic algorithm has shortcomings of poor local search ability and premature convergence. To overcome these disadvantages, simulated annealing algorithm which has good local search ability was combined with genetic algorithm to form simulated annealing genetic algorithm. The tests by two commonly used test functions of Shaffer’s F6 and Rosenbrock show that simulated annealing genetic algorithm outperforms the simple genetic algorithm both in convergence rate and convergence quality. Finally, the simulated annealing genetic algorithm was firstly applied in a practical problem of balancing and sequencing design of mixed-model assembly line, once again, the solution results show that simulated annealing genetic algorithm outperforms the simple genetic algorithm. Meanwhile, it provides a new algorithm for solving the design problem of mixed-model assembly line.

Concurrent Optimization of Product Module Selection and Assembly Line Configuration: A Multi-Objective Approach

2005 ◽  
Vol 127 (4) ◽  
pp. 875-884 ◽  
Author(s):  
Zhonghui Xu ◽  
Ming Liang
Keyword(s):  
Genetic Algorithm ◽  
Assembly Line ◽  
Solution Procedure ◽  
Programming Approach ◽  
Reconfigurable Manufacturing ◽  
Multi Objective ◽  
Modular Product ◽  
Conflicting Objectives ◽  
Module Selection ◽  
Line Design

Both modular product design and reconfigurable manufacturing have a great potential to enhance responsiveness to market changes and to reduce production cost. However, the two issues have thus far mostly been investigated separately, thereby causing possible mismatch between the modular product structure and the manufacturing or assembly system. Therefore, the potential benefits of product modularity may not be materialized due to such mismatch. For this reason, this paper presents a concurrent approach to the product module selection and assembly line design problems to provide a set of harmonic solutions to the two problems and hence avoid the mismatch between design and manufacturing. The integrated nature of the problem leads to several noncommensurable and often conflicting objectives. The modified Chebyshev goal programming approach is applied to solve the multi-objective problem. A genetic algorithm is further developed to provide quick and near-optimum solutions. The proposed approach and the solution procedure have been applied to an ABS motor problem. The performance of the genetic algorithm has also been examined.

Sequencing Mixed Model Assembly Lines Based on Genetic Algorithm Optimization

2011 ◽  
Vol 279 ◽  
pp. 412-417
Author(s):  
Yin Di Huang ◽  
Rong Hua Bian ◽  
Zhen Xu
Keyword(s):  
Genetic Algorithm ◽  
Waiting Time ◽  
Production Efficiency ◽  
Assembly Line ◽  
Mixed Model ◽  
Optimization Problems ◽  
Simulation Software ◽  
Algorithm Optimization ◽  
Genetic Algorithm Optimization ◽  
Line Production

Firstly, genetic algorithm optimization method was put forward to solve optimization problems of sequencing in actual mixed model passenger car factory assembly line. Then genetic algorithm optimization methods and procedures of mixed model total assembly line production sequencing were studied. Finally, according to the characteristics of the mixed production line, the least total waiting time of vehicle was set to complete assembly as a sequencing optimization objective, the AutoMod simulation software was used to balance the allocation of resources and optimize the sequence by way of genetic algorithm, and the optimal sequencing solution was obtained. After optimization and balance, the total waiting time of mixed model vehicles in complete assembly line was reduced by 69.5%, which improves production efficiency greatly. This also proves the effectiveness of genetic algorithm optimization.

Modeling and Simulation of Aircraft Assembly Line Based on Quest

2012 ◽  
Vol 569 ◽  
pp. 666-669 ◽  
Author(s):  
Hu Lu ◽  
Xia Liu ◽  
Wei Pang ◽  
Wen Hua Ye ◽  
Bi Sheng Wei
Keyword(s):  
Modeling And Simulation ◽  
Process Model ◽  
Production Efficiency ◽  
Assembly Line ◽  
Simulation Software ◽  
Assembly Process ◽  
Simulation And Optimization ◽  
Aircraft Assembly ◽  
Modeling Simulation ◽  
Aircraft Production

The assembly line is very complicated and its performance directly determines the aircraft production efficiency and cost. Therefore, the modeling, simulation and optimization for aircraft assembly line are very important. In this paper, a modeling and simulation approach for aircraft assembly line based on Quest simulation software has been investigated. Firstly, the characteristics of the aircraft assembly and Quest are briefly introduced in this article. Then, the assembly process model of center fuselage was established, the assembly process is simulated and analyzed, the bottleneck in the production line has been found, which can be used to optimize the assembly line.

scholarly journals Simulation and Optimization of the VTC200C Machining Center Production Plan

TEM Journal ◽  
2020 ◽  
pp. 1295-1306
Author(s):  
Marian Králik ◽  
Vladimír Jerz
Keyword(s):  
Genetic Algorithm ◽  
Simulation Model ◽  
Input Data ◽  
Simulation Software ◽  
Efficient Production ◽  
Production Plan ◽  
Output Data ◽  
Simulation And Optimization ◽  
Machining Center ◽  
Plant Simulation

The paper describes the use of the Plant Simulation software to create a simulation model of the manufacturing process in the VTC200C machining centre. A genetic algorithm was used to optimize the production process. It is an algorithm that learns itself and looks for the best solution based on input data. The optimization is done based on the assessment of the output data from the simulation model. Based on the results of the optimization, a more efficient production plan was designed in the selected company.

scholarly journals The Use of a Genetic Algorithm for Sorting Warehouse Optimisation

Processes ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 1197
Author(s):  
Patrik Grznár ◽  
Martin Krajčovič ◽  
Arkadiusz Gola ◽  
Ľuboslav Dulina ◽  
Beáta Furmannová ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Business Processes ◽  
Optimal Number ◽  
Simulation Software ◽  
Heuristic Methods ◽  
Major Purpose ◽  
Processing Times ◽  
Speed Up ◽  
Plant Simulation

In the last decade, simulation software as a tool for managing and controlling business processes has received a lot of attention. Many of the new software features allow businesses to achieve better quality results using optimisation, such as genetic algorithms. This article describes the use of modelling and simulation in shipment and sorting processes that are optimised by a genetic algorithm’s involvement. The designed algorithm and simulation model focuses on optimising the duration of shipment processing times and numbers of workers. The commercially available software Tecnomatix Plant Simulation, paired with a genetic algorithm, was used for optimisation, decreasing time durations, and thus selecting the most suitable solution for defined inputs. This method has produced better results in comparison to the classical heuristic methods and, furthermore, is not as time consuming. This article, at its core, describes the algorithm used to determine the optimal number of workers in sorting warehouses with the results of its application. The final part of this article contains an evaluation of this proposal compared to the original methods, and highlights what benefits result from such changes. The major purpose of this research is to determine the number of workers needed to speed up the departure of shipments and optimise the workload of workers.

scholarly journals Production Planning Based on a Genetic Algorithm Controlled Simulation Model

2021 ◽  
pp. 233-243
Author(s):  
Alexey N. Sochnev
Keyword(s):  
Genetic Algorithm ◽  
Simulation Model ◽  
Optimization Procedure ◽  
Simulation Models ◽  
Simulation Software ◽  
Simulation Approach ◽  
Fitness Functions ◽  
Optimization And Simulation ◽  
Plant Simulation ◽  
Positive Effect

The article proposes an approach to solving the task of operational calendar planning of production based on the application of the principles of the optimization and simulation approach. The production simulation model is implemented using the Tecnomatix Plant Simulation software. The optimization procedure is represented by a genetic algorithm. In the implementation of the genetic algorithm, a simulation model is used to evaluate fitness functions. An example of using the proposed approach for a typical production system is given and the positive effect of its application is confirmed. Features of use, positive and negative properties, as well as the possibility of replication to other types of simulation models are revealed

Assembly line design in final assembly excavator at XYZ Corp using genetic algorithm

2019 ◽  
Author(s):  
Risty Mayang Sari ◽  
Dida Diah Damayanti ◽  
Widia Juliani
Keyword(s):  
Genetic Algorithm ◽  
Assembly Line ◽  
Final Assembly ◽  
Line Design
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