TY - JOUR
T1 - Mathematical model and metaheuristics for simultaneous balancing and sequencing of a robotic mixed-model assembly line
AU - Li, Zixiang
AU - Janardhanan, Mukund Nilakantan
AU - Tang, Qiuhua
AU - Nielsen, Peter
PY - 2018
Y1 - 2018
N2 - This article presents the first method to simultaneously balance and sequence robotic mixed-model assembly lines (RMALB/S), which involves three sub-problems: task assignment, model sequencing and robot allocation. A new mixed-integer programming model is developed to minimize makespan and, using CPLEX solver, small-size problems are solved for optimality. Two metaheuristics, the restarted simulated annealing algorithm and co-evolutionary algorithm, are developed and improved to address this NP-hard problem. The restarted simulated annealing method replaces the current temperature with a new temperature to restart the search process. The co-evolutionary method uses a restart mechanism to generate a new population by modifying several vectors simultaneously. The proposed algorithms are tested on a set of benchmark problems and compared with five other high-performing metaheuristics. The proposed algorithms outperform their original editions and the benchmarked methods. The proposed algorithms are able to solve the balancing and sequencing problem of a robotic mixed-model assembly line effectively and efficiently.
AB - This article presents the first method to simultaneously balance and sequence robotic mixed-model assembly lines (RMALB/S), which involves three sub-problems: task assignment, model sequencing and robot allocation. A new mixed-integer programming model is developed to minimize makespan and, using CPLEX solver, small-size problems are solved for optimality. Two metaheuristics, the restarted simulated annealing algorithm and co-evolutionary algorithm, are developed and improved to address this NP-hard problem. The restarted simulated annealing method replaces the current temperature with a new temperature to restart the search process. The co-evolutionary method uses a restart mechanism to generate a new population by modifying several vectors simultaneously. The proposed algorithms are tested on a set of benchmark problems and compared with five other high-performing metaheuristics. The proposed algorithms outperform their original editions and the benchmarked methods. The proposed algorithms are able to solve the balancing and sequencing problem of a robotic mixed-model assembly line effectively and efficiently.
KW - Assembly line balancing
KW - co-evolutionary algorithm
KW - model sequencing
KW - robotic assembly line
KW - simulated annealing
UR - http://www.scopus.com/inward/record.url?scp=85025677048&partnerID=8YFLogxK
U2 - 10.1080/0305215X.2017.1351963
DO - 10.1080/0305215X.2017.1351963
M3 - Journal article
AN - SCOPUS:85025677048
SN - 0305-215X
VL - 50
SP - 877
EP - 893
JO - Engineering Optimization
JF - Engineering Optimization
IS - 5
ER -