scholarly journals Balancing a U-Shaped Assembly Line with a Heuristic Algorithm Based on a Comprehensive Rank Value

2022 ◽  
Vol 14 (2) ◽  
pp. 775
Author(s):  
Yuling Jiao ◽  
Nan Cao ◽  
Jin Li ◽  
Lin Li ◽  
Xue Deng
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Heuristic Algorithm ◽  
Manufacturing Industry ◽  
Assembly Line ◽  
Balance Model ◽  
Assembly Process ◽  
Calculation Time ◽  
Task Time ◽  
Comparison Of The Results

An aim of sustainable development of the manufacturing industry is to reduce the idle time in the product-assembly process and improve the balance efficiency of the assembly line. A priority relationship diagram is obtained on an existing assembly line in the laboratory by measuring the task time of the chassis model, analyzing the product structure, and designing the assembly process. The type-E balance model of the U-shaped assembly line is established and solved by a heuristic algorithm based on the comprehensive rank value. The type-E balance problem of the U-shaped assembly-line plan of the chassis model is obtained, and the production line layout is planned. Combining instances to compare the results of the heuristic algorithm, genetic algorithm, and simulated annealing, comparison of the results shows that the degree of load balancing is slightly higher than genetic algorithm and simulated annealing. The balance efficiencies obtained by the heuristic algorithm are smaller than the genetic algorithm and simulated annealing. The calculation time is significantly less than the genetic algorithm and simulated annealing, and the scale of instances has little effect on the calculation time. The results verify that the model and the algorithm are effective. This study provides a reference for the entire process of the U-shaped assembly-line, type-E balance and the assembly products in laboratories.

Hull mixed-model assembly line balancing using a multi-objective genetic algorithm simulated annealing optimization approach

2016 ◽  
Vol 25 (1) ◽  
pp. 30-40 ◽  
Author(s):  
Yu-guang Zhong
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Load Balancing ◽  
Assembly Line ◽  
Assembly Line Balancing ◽  
Line Balancing ◽  
Optimization Approach ◽  
Multi Objective ◽  
Mutation Operators ◽  
Mixed Model Assembly Line

Hull assembly line balancing has significant impact on performance of shipbuilding system and is usually a multi-objective optimization problem. In this article, the primary objectives of the hull assembly line balancing are to minimize the number of workstations, to minimize the static load balancing index, to minimize the dynamic load balancing index between workstations, and to minimize the multi-station-associated complexity. Because this problem comes under combinatorial optimization category and is non-deterministic polynomial-time hard, an improved genetic algorithm simulated annealing is presented. In genetic algorithm simulated annealing, the task sequence numbers are used as chromosomes, and selection, crossover, and mutation operators only deal with the elements of task set instead of the ones of the problem space. In order to prevent the algorithm appearing early convergence or getting local optimal result, the simulated annealing algorithm is used to deal with the individuals. Meanwhile, the algorithm is embedded with the hierarchical scheduling tactics in order to solve the selection problem on optimal solution in the Pareto-optimal set. A number of benchmark problems are solved to prove the superior efficiency of the proposed algorithm. Finally, a case study of the optimization of a hull assembly line was given to illustrate the feasibility and effectiveness of the method.

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.

Using Delmia V5 for Human Activity Improvement in an Assembly Line Production

2014 ◽  
Vol 657 ◽  
pp. 353-358 ◽  
Author(s):  
Rares Adrian Ghinea ◽  
Daniela Popescu ◽  
Călin Neamțu ◽  
Dan Hurgoiu ◽  
Florin Popister
Keyword(s):  
Virtual Environment ◽  
Human Activity ◽  
Manufacturing Industry ◽  
Assembly Line ◽  
Pneumatic Actuator ◽  
Assembly Process ◽  
Manual Assembly ◽  
Line Production ◽  
Simulation And Optimization

This paper presents a methodology for the simulation and optimization in a virtual environment of a manual assembly process. For validation of the methodology the authors chose an assembly process that is already used in the manufacturing industry. In the first step of the proposed methodology the existing process is simulated and then based on the simulation the activities and equipment involved are being analyzed and in the next step the assembly process is to be optimized using simulation and a series of modification vectors such as: layout, devices, tools and movement sequences. The case study presented in the paper aims to optimize the assembly process of a pneumatic actuator of a butterfly type valve.

scholarly journals Time and space multi-manned assembly line balancing problem using genetic algorithm

2021 ◽  
Vol 14 (4) ◽  
pp. 733
Author(s):  
Nessren Zamzam ◽  
Ahmed Elakkad
Keyword(s):  
Genetic Algorithm ◽  
Assembly Line ◽  
Real Life ◽  
Assembly Line Balancing ◽  
Line Balancing ◽  
Initial Population ◽  
Time And Space ◽  
Task Time ◽  
Assembly Line Balancing Problem ◽  
Simple Assembly

Purpose: Time and Space assembly line balancing problem (TSALBP) is the problem of balancing the line taking the area required by the task and to store the tools into consideration. This area is important to be considered to minimize unplanned traveling distance by the workers and consequently unplanned time waste. Although TSALBP is a realistic problem that express the real-life situation, and it became more practical to consider multi-manned assembly line to get better space utilization, few literatures addressed the problem of time and space in simple assembly line and only one in multi-manned assembly line. In this paper the problem of balancing bi-objective time and space multi-manned assembly line is proposedDesign/methodology/approach: Hybrid genetic algorithm under time and space constraints besides assembly line conventional constraints is used to model this problem. The initial population is generated based on conventional assembly line heuristic added to random generations. The objective of this model is to minimize number of workers and number of stations.Findings: The results showed the effectiveness of the proposed model in solving multi-manned time and space assembly line problem. The proposed method gets better results in solving real-life Nissan problem compared to the literature. It is also found that there is a relationship between the variability of task time, maximum task time and cycle time on the solution of the problem. In some problem features it is more appropriate to solve the problem as simple assembly line than multi-manned assembly line.Originality/value: It is the first article to solve the problem of balancing multi-manned assembly line under time and area constraint using genetic algorithm. A relationship between the problem features and the solution is found according to it, the solution method (one sided or multi-manned) is defined.

A Hybrid Heuristic Algorithm for the Intelligent Transportation Scheduling Problem of the BRT System

2015 ◽  
Vol 24 (4) ◽  
pp. 437-448 ◽  
Author(s):  
Zheng Ning ◽  
Chen Tao ◽  
Lin Fei ◽  
Xu Haitao
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Heuristic Algorithm ◽  
Simulated Annealing Algorithm ◽  
Local Optimum ◽  
Scheduling Problem ◽  
Selection Probability ◽  
Hybrid Heuristic ◽  
Hybrid Heuristic Algorithm ◽  
Annealing Algorithm

AbstractThis work proposes a hybrid heuristic algorithm to solve the bus rapid transit (BRT) intelligent scheduling problem, which is a combination of the genetic algorithm, simulated annealing algorithm, and fitness scaling method. The simulated annealing algorithm can increase the local search ability of the genetic algorithm, so as to accelerate its convergence speed. Fitness scaling can reduce the differences between individuals in the early stage of the algorithm, to prevent the genetic algorithm from falling into a local optimum through increasing the diversity of the population. It can also increase the selection probability of outstanding individuals, and speed up the convergence at the late stage of the algorithm, by increasing the differences between individuals. Using real operational data of BRT Line 1 in a city of Zhejiang province, the new scheduling scheme can be obtained through algorithm simulation. The passengers’ total waiting time in a single way will be reduced by 40 h on average under the same operating cost compared with the original schedule scheme in a day.

scholarly journals Job rotation and human–robot collaboration for enhancing ergonomics in assembly lines by a genetic algorithm

2021 ◽  
Author(s):  
Michela Dalle Mura ◽  
Gino Dini
Keyword(s):  
Genetic Algorithm ◽  
Manufacturing Industry ◽  
Assembly Line ◽  
Human Factor ◽  
Assembly Line Balancing ◽  
Point Of View ◽  
Organizational Strategies ◽  
Job Rotation ◽  
Key Factor ◽  
Human Robot Collaboration

AbstractCurrently, the largest percentage of the employed workforce in the manufacturing industry is involved in the assembly process, making ergonomics a key factor when dealing with assembly-related problems. During these processes, repetitive tasks and heavy component handling are frequent for workers, who may result overloaded from an energetic point of view, thus affecting several aspects not only relating to the human factor but also to potentially reduced productivity. Different organizational strategies and technological solutions could be adopted to overcome these drawbacks. For these purposes, the present paper proposes a genetic algorithm for solving the typical problem of assembly line balancing, taking into account job rotation and human–robot collaboration for enhancing ergonomics of workers. The objectives of the problem are related to both economic aspects and human factor: (i) the cost for implementing the assembly line is minimized, evaluated on the basis of the number of workers and differentiated by skill levels and on equipment installed on workstations, including collaborative robots, and (ii) the energy load variance among workers is also minimized, so as to smooth their energy expenditure in performing the assigned assembly operations, calculated according to their movements, physiological characteristics, job rotations and degree of collaboration with robots. The paper finally presents and discusses the application of the developed tool to an industrial assembly case.

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