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

M. F. F. Ab. Rashid; N. M. Z. Nik Mohamed; A. N. Mohd Rose

doi:10.15282/jmes.13.4.2019.13.0469

Authors

M. F. F. Ab. Rashid Faculty of Mechanical & Manufacturing Engineering, Universiti Malaysia Pahang, 26600 Pekan, Pahang, Malaysia, Phone: +6094246321; Fax: +6094246222
N. M. Z. Nik Mohamed Faculty of Mechanical & Manufacturing Engineering, Universiti Malaysia Pahang, 26600 Pekan, Pahang, Malaysia, Phone: +6094246321; Fax: +6094246222
A. N. Mohd Rose Faculty of Mechanical & Manufacturing Engineering, Universiti Malaysia Pahang, 26600 Pekan, Pahang, Malaysia, Phone: +6094246321; Fax: +6094246222

DOI:

https://doi.org/10.15282/jmes.13.4.2019.13.0469

Keywords:

Manufacturing system, Artificial Bee Colony, Assembly sequence planning, Assembly line balancing

Abstract

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.

References

Mastura M, Sapuan S, Mansor M. A framework for prioritizing customer requirements in product design: Incorporation of FAHP with AHP. Journal of Mechaical Engineerig and Sciences. 2015; 9: 1655–1670.

Horng S-C, Lin S-S. Embedding advanced harmony search in ordinal optimization to maximize throughput rate of flow line. Arabian Journal for Science and Engineering. 2018; 43: 1015–1031.

Marian RM. Optimisation of assembly sequences using genetic algorithm. University of South Australia, 2003.

Andrew-Munot M, Yassin A, Shazali ST, et al. Analysis of production planning activities in remanufacturing system. Journal of Mechanical and Engineering Sciences. 2018; 12: 3548–3565.

Abdullah MA, Ab. Rashid MFF, Ponnambalam SG, Ghazali, Z. Energy efficient modeling and optimization for assembly sequence planning using moth flame optimization. Assembly Automation. 2019; 39: 356–368.

Rashid MFF, Hutabarat W, Tiwari A. A review on assembly sequence planning and assembly line balancing optimisation using soft computing approaches. Int Journal of Advance Manufacturing Technology. 2012; 59: 335–349.

Akpinar S, Elmi A, Bektaş T. Combinatorial Benders cuts for assembly line balancing problems with setups. European Journal of Operational Research. 2017; 259: 527–537.

Álvarez-Miranda E, Pereira J. On the complexity of assembly line balancing problems. Computer and Operations Research. 2019; 108: 182–186.

Boysen N, Fliedner M, Scholl A. A classification of assembly line balancing problems. European Journal of Operational Research. 2007; 183: 674–693.

Wang HS, Che ZH, Chiang CJ. A hybrid genetic algorithm for multi-objective product plan selection problem with ASP and ALB. Expert System with Applications. 2012; 39: 5440–5450.

Ab Rashid MFF, Mohamed NMZN, Rose ANM, Kor KY. Simulation study of a vehicle production line for productivity improvement. Journal of Mechanical Engineering and Sciences. 2015; 8: 1283–1292.

Lu C, Yang Z. Integrated assembly sequence planning and assembly line balancing with ant colony optimization approach. International Journal of Advanced Manufacturing Technology. 2016; 83: 243–256.

Ouaarab A, Ahiod B, Yang X-S. Discrete cuckoo search algorithm for the travelling salesman problem. Neural Computing and Applications. 2013; 24: 1659–1669.

Ab Rashid MFF, Hutabarat W, Tiwari A. Multi-objective discrete particle swarm optimisation algorithm for integrated assembly sequence planning and assembly line balancing. Proceeding Inst. Mech. Eng. Part B: Journal of Engineering Manufacture. 2018; 232: 1444–1459.

Tseng H-E, Tang C-E. A sequential consideration for assembly sequence planning and assembly line balancing using the connector concept. International Journal of Production Research. 2006; 44: 97–116.

Tseng H-E, Chen M-H, Chang C-C, Wang W-P. Hybrid evolutionary multi-objective algorithms for integrating assembly sequence planning and assembly line balancing. International Journal of Production Research. 2008; 46: 5951–5977.

Chen R, Lu K, Yu S. A hybrid genetic algorithm approach on multi-objective of assembly planning problem. Engineering Applications of Artificial Intelligence. 2002; 15: 447–457.

Yang Z, Lu C, Zhao HW. An Ant Colony Algorithm for Integrating Assembly Sequence Planning and Assembly Line Balancing. Applied Mechanics and Materials. 2013; 397–400: 2570–2573.

Lutfy OF. Adaptive Direct Inverse Control Scheme Utilizing a Global Best Artificial Bee Colony to Control Nonlinear Systems. Arabian Journal for Science and Engineering. 2018; 43: 2873–2888.

Liu F, Sun Y, Wang G, Wu T-T. An Artificial Bee Colony Algorithm Based on Dynamic Penalty and Lévy Flight for Constrained Optimization Problems. Arab Journal for Science and Engineering. Epub ahead of print January 2018.

Kalayci C, Gupta S. Artificial Bee Colony Algorithm for Solving Sequence-dependent Disassembly Line Balancing Problem. Expert Systems with Applications. 2013; 40: 7231–7241.

Li J-Q, Pan Q-K, Tasgetiren MF. A discrete artificial bee colony algorithm for the multi-objective flexible job-shop scheduling problem with maintenance activities. Applied Mathematical Modelling. 2014; 38: 1111–1132.

Zhang F, Li L, Liu J, Chu X. Artificial Bee Colony Optimization for Yard Truck Scheduling and Storage Allocation Problem. Springer, Cham, 908–917.

Zhong F, Li H, Zhong S. An improved artificial bee colony algorithm with modified-neighborhood-based update operator and independent-inheriting-search strategy for global optimization. Engineering Application of Artificial Intelligence. 2017; 58: 134–156.

Huang F, Wang L, Yang C. A new improved artificial bee colony algorithm for ship hull form optimization. Engineering Optimization. 2016; 48: 672–686.

Battaïa O, Dolgui A. A taxonomy of line balancing problems and their solution approaches. International Journal of Production Economics. 2013; 142: 259–277.

Karaboga D. An idea based on honey bee swarm for numerical optimization. Kayseri, Turkey, 2005.

Mirjalili S, Mirjalili SM, Lewis A. Grey Wolf Optimizer. Adv Eng Softw 2014; 69: 46–61.

Herzner G, Schmitt T, Linsenmair KE, Strohm E. Prey recognition by females of the European beewolf and its potential for a sensory trap. Animal Behaviour. 2005; 70: 1411–1418.

Scholl A. Benchmark Data Sets by Scholl. Assembly Line Balancing Data Dets & Research Topics, http://assembly-line-balancing.mansci.de/salbp/benchmark-data-sets-1993/ (1993).