| dc.description.abstract | Assembly line balancing (ALB) is one of the most critical activities before mass produc
tion, which refers to balancing the assembly tasks to workstations with constraints. Due to
the nature of the manufacturing environment, fuzziness and randomness may co-occur. In
this work, we model the processing time of the task as fuzzy-stochastic variables. Specif
ically, we propose the fuzzy random variables modelled as triangular fuzzy number, in
which each value follows normal distribution. Additionally, a new ranking method is pro
posed to rank these numbers. After that, a mathematical model is introduced to solve the
ALB problem with fuzzy random variables. This mathematical model is then used as the
post-processing after obtaining the near-optimal solution from genetic algorithm. This al
gorithm is called matheuristic. The results show that our proposed approach outperform
the mixed-integer programming model in computational time and solution. Additionally,
it gives signifcantly better performance than metaheuristics such as Genetic Algorithm
and Particle Swarm Optimization in a reasonable time. | en_US |
