TY - GEN
T1 - Improved genetic algorithm for magnetic material two-stage multi-product production scheduling
T2 - 51st IEEE Conference on Decision and Control, CDC 2012
AU - Liu, Yefeng
AU - Chai, Tianyou
AU - Qin, S. Joe
AU - Pan, Quanke
AU - Yang, Shengxiang
PY - 2012/12
Y1 - 2012/12
N2 - In this paper an improved genetic algorithm (GA) was present for magnetic material two-stage, multi-product, production scheduling problem (TMPS) with parallel machines. TMPS was changed into molding-stage's multi-product production scheduling problem (MMPS) and the scheduling model was set up for the first time. A set of random solutions were explored first, better feasible solutions were obtained by GA. To shorten the solving time and improve solution accuracy, an improved GA was proposed. We improved GA's crossover operator, adopted heuristic greedy 3PM crossover operator (HG3PMCO) to reduce GA's computational time. Through contrast of computational results of MILP, general GA and improved GA, the improved GA has demonstrated its effectiveness and reliability in solving the molding sintering production scheduling problems and the MILP model set up for the first time is reasonable. At last, the improved genetic algorithm was used in molding stage and sintering stage TMPS of magnetic material. © 2012 IEEE.
AB - In this paper an improved genetic algorithm (GA) was present for magnetic material two-stage, multi-product, production scheduling problem (TMPS) with parallel machines. TMPS was changed into molding-stage's multi-product production scheduling problem (MMPS) and the scheduling model was set up for the first time. A set of random solutions were explored first, better feasible solutions were obtained by GA. To shorten the solving time and improve solution accuracy, an improved GA was proposed. We improved GA's crossover operator, adopted heuristic greedy 3PM crossover operator (HG3PMCO) to reduce GA's computational time. Through contrast of computational results of MILP, general GA and improved GA, the improved GA has demonstrated its effectiveness and reliability in solving the molding sintering production scheduling problems and the MILP model set up for the first time is reasonable. At last, the improved genetic algorithm was used in molding stage and sintering stage TMPS of magnetic material. © 2012 IEEE.
UR - http://www.scopus.com/inward/record.url?scp=84874243954&partnerID=8YFLogxK
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-84874243954&origin=recordpage
U2 - 10.1109/CDC.2012.6426459
DO - 10.1109/CDC.2012.6426459
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 978-1-4673-2065-8
T3 - Proceedings of the IEEE Conference on Decision and Control
SP - 2521
EP - 2526
BT - 51st IEEE Conference on Decision and Control
PB - IEEE
Y2 - 10 December 2012 through 13 December 2012
ER -