A case study of energy efficient assembly sequence planning problem

1st International Postgraduate Conference on Mechanical Engineering

工业技术（总论）;机械制造

Abdullah, M.A.^1 ; Rashid, M. F. F. Ab^1 ; Ghazali, Z.^1 ; Rose, A.N.M.^1

Faculty of Mechanical Engineering, Universiti Malaysia Pahang, Pekan, Pahang

26600, Malaysia^1

关键词: Ant Colony Optimization (ACO);    Assembly sequence planning;    Assembly sequence planning problem;    Computational approach;    Computational experiment;    Environmental aspects;    Least significant differences;    Manufacturing industries;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/469/1/012013/pdf
DOI  :  10.1088/1757-899X/469/1/012013

学科分类：工业工程学

来源: IOP

【 摘 要 】

Energy efficiency has become an important issue in manufacturing industry, since it is one of the biggest energy consumers in the world. Despite the importance of energy efficiency, it is much obvious that the research in assembly sequence that focus on environmental aspect is still lacking. In Assembly Sequence Planning (ASP), the research on problem optimization is mainly demanded for the effective computational approach to determine the best assembly sequence. This paper presents a case study from an electronic product assembly that considers the energy utilization during assembly process. In particular, the case study focuses to reduce the idle energy utilization in assembly process. The case study was optimized using newly proposed Moth-Flame Optimization (MFO) and then being compared with well-frequent used algorithms including Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA). The result of the computational experiment test was divided into comparison of assembly layout between MFO proposed layout and existing layout. Besides, the statistical test involving Analysis of Variance (ANOVA) and post-hoc test of Fisher's Least Significant Difference (LSD) were then conducted. The proposed MFO performed better in terms of the best minimum fitness (0.401681), average fitness (0.415308), standard deviation of fitness (0.022601), with appropriate computational time and power consumed. In meantime, the results also indicated that the case study was suitable in the development of energy efficient model for ASP.