Modelling and optimisation of energy-efficient U-shaped robotic assembly line balancing problems

Within U-shaped assembly lines, the increase of labour costs and subsequent utilisation of robots has led to growing energy consumption, which is the current main expense of auto and electronics industries. However, there are limited researches concerning both energy consumption reduction and produc...

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Vydané v:International journal of production research Ročník 57; číslo 17; s. 5520 - 5537
Hlavní autori: Zhang, Zikai, Tang, Qiuhua, Li, Zixiang, Zhang, Liping
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: London Taylor & Francis 02.09.2019
Taylor & Francis LLC
Predmet:
artificial bee colony
assembly line balancing
Assembly lines
Energy consumption
energy consumption reduction
Fuel consumption
Integer programming
multi-objective optimisation
Multiple objective analysis
Multiplication
Optimization
Search algorithms
Swarm intelligence
U-shaped robotic assembly lines
ISSN:0020-7543, 1366-588X
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Shrnutí:Within U-shaped assembly lines, the increase of labour costs and subsequent utilisation of robots has led to growing energy consumption, which is the current main expense of auto and electronics industries. However, there are limited researches concerning both energy consumption reduction and productivity improvement on U-shaped robotic assembly lines. This paper first develops a nonlinear multi-objective mixed-integer programming model, reformulates it into a linear form by linearising the multiplication of two binary variables, and then refines the weight of multiple objectives so as to achieve a better approximation of true Pareto frontiers. In addition, Pareto artificial bee colony algorithm (PABC) is extended to tackle this new complex problem. This algorithm stores all the non-dominated solutions into a permanent archive set to keep all the good genes, and selects one solution from this set to overcome the strong local minima. Comparative experiments based on a set of newly generated benchmarks verify the superiority of the proposed PABC over four multi-objective algorithms in terms of generation distance, maximum spread, hypervolume ratio and the ratio of non-dominated solution.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 14
ISSN:0020-7543
1366-588X
DOI:10.1080/00207543.2018.1530479