assembly line balancing using artificial inteligence
114049093
Assembly Line Balancing using Artificial Neural Network: A Case Study of Tricycle Assembly Line
This study reports the use of Artificial Neural Network in balancing an existing single model assembly line of Boulous Enterprises Limited. A multilayer perceptron, with the help of online training was utilized, due to its ability to accommodate large dataset. The results obtained showed that standard cycle time of 576 seconds in the existing line was reduced to 526 seconds. Also, the average idle time was reduced from 105 seconds to 56 seconds, and the output of tricycles produced per day was increased from 50 to 55. The results clearly showed that a better balanced line was obtained with the use of Artificial Neural Network. Keywords Line Balancing, bottlenecks, Idle Time, Efficiency
Anthony I Unuigbe Henry Unuigbe B Ehebhamen C Ekhareafo
212834456
Artificial Neural Network for Assembly Line Balancing
This study examines an assembly line balancing using artificial neural network. An organization that balances the unique workloads must respect the limits and restrictions that hinder the assembly. To optimize the very specific operations, balancing an assembly line may require different methods, including: genetic algorithm, heuristic approach, simulation techniques, the ant colony optimization (ACO) etc. but in this study, artificial neural networks was applied to solving problems of assembly line balancing. This study also explores the characteristics of the assembly line and the classification of the assembly balancing problems, suggesting as an artificial neural network solve.
American International Journal of Sciences and Engineering Research
Pius Charles Ucheagwu Johnmary Ugochukwu Okeke Christian I Okonta Efosa Osamuyimwen
57242627
A mathematical model and artificial bee colony algorithm for the lexicographic bottleneck mixed model assembly line balancing problem
Typically, the total number of required workstations are minimised for a given cycle time (this problem is referred to as type 1) or cycle time is minimised for a given number of workstations (this problem is referred to as type 2) in traditional balancing of assembly lines. However, variation in workload distributions of workstations is an important indicator of the quality of the obtained line balance. This needs to be taken into account to improve the reliability of an assembly line against unforeseeable circumstances, such as breakdowns or other failures. For this aim, a new problem, called lexicographic bottleneck mixed model assembly line balancing problem (LB MALBP) is presented and formalised. The lexicographic bottleneck objective, which was recently proposed for the simple single model assembly line system in the literature, is considered for a mixed model assembly line system. The mathematical model of the LB MALBP is developed for the first time in the literature and coded in GAMS solver, and optimal solutions are presented for some small scale test problems available in the literature. As it is not possible to get optimal solutions for the large scale instances, an artificial bee colony algorithm is also implemented for the solution of the LB MALBP. The solution procedures of the algorithm are explored illustratively. The performance of the algorithm is also assessed using derived well known test problems in this domain and promising results are observed in reasonable CPU times.
J. Intell. Manuf.
Ibrahim Kucukkoc Kadir Buyukozkan Sule Itir Satoglu David Zhengwen Zhang
212572855
An Artificial Immune System Approach for Type E Assembly Line Balancing Problem
The manufacturing industry has evolved in the past decades, due to the competition of the global economy, where the market demands high quality and customized products, and meeting lowest possible costs are paramount. One of the many processes in manufacturing is the assembly line operation, namely the assembly line balancing (ALB) problem. ALB problem is dependent on optimum resource utilization in order to improve production output, reduces costs, and shortens production lead times. In these recent years, various approaches have been proposed to solve the complexity of assembly line balancing operations, which composed of exact, heuristic, and metaheuristic approaches. However, little work had been done to solve the type E assembly line balancing problem. This paper proposed an approach using artificial immune system (AIS) algorithm, namely as the artificial immune cell (AIC) approach, for solving type E assembly line balancing problem. An initialization mechanism through the bone marrow model and probabilistic clonal selection mechanism had encouraged efficient exploration and exploitation of the solution space. The computational results over 242 instances of 24 datasets had demonstrated the efficiency of the proposed AIC approach by achieving high quality solutions (up to 85.94% optimum solutions was obtained) Also, the results were statistically justified by comparing with multi rule multi objective simulated annealing (MRMOSA) priority based genetic approach (PriGA) two phased genetic approach (2P GA) and assignment genetic approach (MAGA)
Mohd Nor Akmal Khalid Umi Kalsom Yusof Zarul Fitri Zaaba
198986368
Minimize Idle Time in Two Sided Assembly Line Balancing Using Exact Search Approach
Growing interests of customers in customized products and increasing competitions between companies to configure and balance their manufacturing systems more effectively within limited space and time. Two sided assembly lines are usually constructed to produce large sized high volume products such as buses, trucks, automobiles, and some domestic products In this paper, the proposed mathematical model is applied to solve benchmark problems of two sided assembly line balancing problem to maximize the workload on each workstation which tends to increase the compactness in the beginning workstations which also helps to minimize the length and idle time of the line. Since the problem is well known as np hard problem a lingo solver based solution method is proposed to solve the problem. Through computational experiments, the performance of the proposed Lingo model is checked with various benchmark problem instances. Based on the computational results, idle time in mated stations and the length of the mated station's line is decreased.
MSIE 2019
Ashish Yadav Sunil Agrawal
211091773
Single Model Assembly Line Balancing Using Enhanced Genetic Algorithm
Assembly line balancing (ALB) is used in many industries to minimize the number of stations, improve the efficiency and work load balance among stations. Enhanced Genetic Algorithm (EGA) is proposed using precedence preservative crossover and scramble mutation techniques, aiming to minimize two objectives; number of stations as a primary objective and smoothing index as a secondary objective. Benchmark problems were selected from the literature, used to test the efficacy of the algorithm and to compare the results with the well known algorithm SALOME. The results showed high efficacy while handling two objectives. It outperformed SALOME in work load balance and efficiency. On the other, EGA outperformed the Genetic Algorithm (GA) developed by [1] in minimizing the number of stations.
Shady Magdy Anwar Ahmed Mahmoud Ali Mohamed Awad
115669978
An improved artificial bee colony algorithm with MaxTF heuristic rule for two sided assembly line balancing problem
Two sided assembly line is usually used for the assembly of large products such as cars, buses, and trucks. With the development of technical progress, the assembly line needs to be reconfigured and the cycle time of the line should be optimized to satisfy the new assembly process. Two sided assembly line balancing with the objective of minimizing the cycle time is called TALBP 2. This paper proposes an improved artificial bee colony (IABC) algorithm with the MaxTF heuristic rule. In the heuristic initialization process, the MaxTF rule defines a new task's priority weight. On the basis of priority weight, the assignment of tasks is reasonable and the quality of an initial solution is high. In the IABC algorithm, two neighborhood strategies are embedded to balance the exploitation and exploration abilities of the algorithm. The employed bees and onlooker bees produce neighboring solutions in different promising regions to accelerate the convergence rate. Furthermore, a well designed random strategy of scout bees is developed to escape local optima. The experimental results demonstrate that the proposed MaxTF rule performs better than other heuristic rules, as it can find the best solution for all the 10 test cases. A comparison of the IABC algorithm and other algorithms proves the effectiveness of the proposed IABC algorithm. The results also denote that the IABC algorithm is efficient and stable in minimizing the cycle time for the TALBP 2, and it can find 20 new best solutions among 25 large sized problem cases.
Xi Duan Bo Wu Youmin Hu Jing-biao Liu Jing Xiong
208731367
An effective artificial fish swarm optimization algorithm for two sided assembly line balancing problems
Abstract Two sided assembly lines are often used in assembly of large sized products, such as automobiles, buses and trucks. Compared to the traditional one sided assembly line, two sided assembly line has advantages of shorter line and higher utilization of fixture. However, normal balancing method is not applicable to solve the two sided assembly line balancing problem since the constraint conditions become more complicated. On the other hand, artificial fish swarm algorithm is a relatively new member of swarm intelligence based on swarm behaviors that were inspired from social behaviors of fish swarm in nature. As a typical application of behaviorism in artificial intelligence, artificial fish swarm algorithm can search for the global optimum. So it is a good candidate for developing new search algorithm for solving optimization problems in operational research. In this research, an effective discrete artificial fish swarm algorithm is developed to solve the cost oriented assembly line balancing problems which aims to minimize the construction cost and at the same time minimize the number of mate station. Through extensive computational experiments, the performance of the proposed artificial fish swarm algorithm is examined. The experimental results validate the effectiveness and efficiency of the proposed method.
Comput. Ind. Eng.
Yu Guang Zhong Zexiao Deng Ke Xu
126757994
Recent trend in mixed model assembly line balancing optimization using soft computing approaches
Purpose This paper aims to review and discuss four aspects of mixed model assembly line balancing (MMALB) problem mainly on the optimization angle. MMALB is a non deterministic polynomial time hard problem which requires an effective algorithm for solution. This problem has attracted a number of research fields: manufacturing, mathematics and computer science. Design/methodology/approach This paper review 59 published research works on MMALB from indexed journal. The review includes MMALB problem varieties, optimization algorithm, objective function and constraints in the problem. Findings Based on research trend, this topic is still growing with the highest publication number observed in 2016 and 2017. The review indicated that the future research direction should focus on human factors and sustainable issues in the problem modeling. As the assembly cost becomes crucial, resource utilization in the assembly line should also be considered. Apart from that, the growth of new optimization algorithms is predicted to influence the MMALB optimization, which currently relies on well established algorithms. Originality/value The originality of this paper is on the research trend in MMALB. It provides the future direction for the researchers in this field.
Engineering Computations
Muhamad Magffierah Razali N H Kamarudin Mohd Fadzil Faisae Ab Rashid Ahmad Nasser Mohd Rose
165076762
Integrated optimization of mixed model assembly sequence planning and line balancing using Multi objective Discrete Particle Swarm Optimization
Abstract Recently, interest in integrated assembly sequence planning (ASP) and assembly line balancing (ALB) began to pick up because of its numerous benefits, such as the larger search space that leads to better solution quality, reduced error rate in planning, and expedited product time to market. However, existing research is limited to the simple assembly problem that only runs one homogenous product. This paper therefore models and optimizes the integrated mixed model ASP and ALB using Multi objective Discrete Particle Swarm Optimization (MODPSO) concurrently. This is a new variant of the integrated assembly problem. The integrated mixed model ASP and ALB is modeled using task based joint precedence graph. In order to test the performance of MODPSO to optimize the integrated mixed model ASP and ALB, an experiment using a set of 51 test problems with different difficulty levels was conducted. Besides that, MODPSO coefficient tuning was also conducted to identify the best setting so as to optimize the problem. The results from this experiment indicated that the MODPSO algorithm presents a significant improvement in term of solution quality toward Pareto optimal and demonstrates the ability to explore the extreme solutions in the mixed model assembly optimization search space. The originality of this research is on the new variant of integrated ASP and ALB problem. This paper is the first published research to model and optimize the integrated ASP and ALB research for mixed model assembly problem.
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
Mohd Fadzil Faisae Ab Rashid Ashutosh Tiwari Windo Hutabarat