Integrated assembly sequence planning and assembly line balancing with ant colony optimization approach

2015 ◽  
Vol 83 (1-4) ◽  
pp. 243-256 ◽  
Author(s):  
Cong Lu ◽  
Zhuo Yang
Keyword(s):  
Ant Colony Optimization ◽  
Assembly Line ◽  
Assembly Line Balancing ◽  
Assembly Sequence Planning ◽  
Ant Colony ◽  
Line Balancing ◽  
Optimization Approach ◽  
Assembly Sequence ◽  
Sequence Planning

An Ant Colony Algorithm for Integrating Assembly Sequence Planning and Assembly Line Balancing

2013 ◽  
Vol 397-400 ◽  
pp. 2570-2573 ◽  
Author(s):  
Zhuo Yang ◽  
Cong Lu ◽  
Hong Wang Zhao
Keyword(s):  
Ant Colony Algorithm ◽  
Assembly Line ◽  
Assembly Line Balancing ◽  
Assembly Sequence Planning ◽  
Ant Colony ◽  
Line Balancing ◽  
Assembly Planning ◽  
Planning Problem ◽  
Assembly Sequence ◽  
Sequence Planning

Assembly sequence planning (ASP) and assembly line balancing (ALB) problems are two essential problems in the assembly optimization. This paper proposes an ant colony algorithm for integrating assembly sequence planning and assembly line balancing, to deal with the two problems on parallel, and resolve the possible conflict between two optimization goals. The assembly sequence planning problem and the assembly line balancing problem are discussed, the process of the proposed ant colony algorithm is investigated. The results can provide a set of solutions for decision department in assembly planning.

scholarly journals Multi-objective discrete particle swarm optimisation algorithm for integrated assembly sequence planning and assembly line balancing

2016 ◽  
Vol 232 (8) ◽  
pp. 1444-1459 ◽  
Author(s):  
Mohd Fadzil Faisae Ab Rashid ◽  
Windo Hutabarat ◽  
Ashutosh Tiwari
Keyword(s):  
Assembly Line ◽  
Particle Swarm ◽  
Assembly Line Balancing ◽  
Particle Swarm Optimisation ◽  
Assembly Sequence Planning ◽  
Line Balancing ◽  
Assembly Sequence ◽  
Discrete Particle ◽  
Sequence Planning ◽  
Multi Objective

In assembly optimisation, assembly sequence planning and assembly line balancing have been extensively studied because both activities are directly linked with assembly efficiency that influences the final assembly costs. Both activities are categorised as NP-hard and usually performed separately. Assembly sequence planning and assembly line balancing optimisation presents a good opportunity to be integrated, considering the benefits such as larger search space that leads to better solution quality, reduces error rate in planning and speeds up time-to-market for a product. In order to optimise an integrated assembly sequence planning and assembly line balancing, this work proposes a multi-objective discrete particle swarm optimisation algorithm that used discrete procedures to update its position and velocity in finding Pareto optimal solution. A computational experiment with 51 test problems at different difficulty levels was used to test the multi-objective discrete particle swarm optimisation performance compared with the existing algorithms. A statistical test of the algorithm performance indicates that the proposed multi-objective discrete particle swarm optimisation algorithm presents significant improvement in terms of the quality of the solution set towards the Pareto optimal set.

scholarly journals A modified artificial bee colony algorithm to optimise integrated assembly sequence planning and assembly line balancing

2019 ◽  
Vol 13 (4) ◽  
pp. 5905-5921
Author(s):  
M. F. F. Ab. Rashid ◽  
N. M. Z. Nik Mohamed ◽  
A. N. Mohd Rose
Keyword(s):  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Assembly Line ◽  
Assembly Line Balancing ◽  
Assembly Sequence Planning ◽  
Line Balancing ◽  
Benchmark Problems ◽  
Assembly Sequence ◽  
Sequence Planning ◽  
Bee Colony

Assembly Sequence Planning (ASP) and Assembly Line Balancing (ALB) are traditionally optimised independently. However recently, integrated ASP and ALB optimisation has become more relevant to obtain better quality solution and to reduce time to market. Despite many optimisation algorithms that were proposed to optimise this problem, the existing researches on this problem were limited to Evolutionary Algorithm (EA), Ant Colony Optimisation (ACO), and Particle Swarm Optimisation (PSO). This paper proposed a modified Artificial Bee Colony algorithm (MABC) to optimise the integrated ASP and ALB problem. The proposed algorithm adopts beewolves predatory concept from Grey Wolf Optimiser to improve the exploitation ability in Artificial Bee Colony (ABC) algorithm. The proposed MABC was tested with a set of benchmark problems. The results indicated that the MABC outperformed the comparison algorithms in 91% of the benchmark problems. Furthermore, a statistical test reported that the MABC had significant performances in 80% of the cases.

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