PENGEMBANGAN ALGORITMA SIMULATED ANNEALING UNTUK PERANCANGAN LINTAS PERAKITAN PRODUK CAMPURAN DAN PERAKITANNYA MENGGUNAKAN KOLABORASI MANUSIA-ROBOT
PT JVC Electronics Indonesia (JEIN) is a company in the electronics industry that has utilized automation in its assembly lines with the application of humanrobot collaboration (HRC). The use of collaborative robot has been chosen by PT JEIN as an effort to survive in the market in order to meet...
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| Format: | Final Project |
| Language: | Indonesia |
| Online Access: | https://digilib.itb.ac.id/gdl/view/68399 |
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| Institution: | Institut Teknologi Bandung |
| Language: | Indonesia |
| Summary: | PT JVC Electronics Indonesia (JEIN) is a company in the electronics industry
that has utilized automation in its assembly lines with the application of humanrobot
collaboration (HRC). The use of collaborative robot has been chosen by PT
JEIN as an effort to survive in the market in order to meet customer needs. The
large variety of products and the general characteristic of electronic products
that have a short lifespan make companies need to adjust and re-design product
assembly lines fast.
This study aims to develop a simulated annealing algorithm for mixed model
assembly line balancing problems with human-robot collaboration is simulated
annealing. The simulated annealing algorithm consists of 2 (two) main
procedures, which are the outer loop and the inner loop. The simulated annealing
algorithm that is developed in this study has 3 (three) sub-procedures included in
the inner loop which are the procedure of switching task, switching resources
procedure, and procedure for minimizing the number of workstations.
Experimental analysis using fractional factorial design is conducted to determine
the algorithm parameters that had a significant effect on the objective value. Two
parameters are found to be significant on the objective value: the number of
temperature reduction (????) and the number of iterations at each temperature (????).
The simulated annealing algorithm was able to generate feasible solutions for 8
(eight) experimental data with 3 (three) data obtained optimal results and 5 (five)
data obtained local optimum results. Experiments resulted in an average gap of
12.33% for the analytical method and 95.39% for the computation time. If
computational time is set at 86400 seconds, an average gap of 22.87% were
obtained with respect to each local optimum value.
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