TY - JOUR
T1 - Multiobjective scheduling algorithm for flexible manufacturing systems with Petri nets
AU - Mejía, Gonzalo
AU - Pereira, Jordi
N1 - Funding Information:
The authors would like to thank Diego Vásquez, alumni of the M.Sc. program in Industrial Engineering of the Pontificia Universidad Católica de Valparaíso (Chile) for his valuable help. J. Pereira also acknowledges the support of the National Commission for Scientific and Technological Research, CONICYT, through the grant FONDECYT N. 1191624 “Assembly line balancing for industry 4.0”. Also, the authors want to express their gratitude to the anonymous reviewers for their suggestions. The final manuscript has been clearly improved by their contribution. Conflicts of interest: The authors declare no conflicts of interest.
Funding Information:
The authors would like to thank Diego Vásquez, alumni of the M.Sc. program in Industrial Engineering of the Pontificia Universidad Católica de Valparaíso (Chile) for his valuable help. J. Pereira also acknowledges the support of the National Commission for Scientific and Technological Research, CONICYT, through the grant FONDECYT N. 1191624 “Assembly line balancing for industry 4.0”. Also, the authors want to express their gratitude to the anonymous reviewers for their suggestions. The final manuscript has been clearly improved by their contribution.
Publisher Copyright:
© 2020 The Society of Manufacturing Engineers
PY - 2020/1
Y1 - 2020/1
N2 - In this work, we focus on general multi-objective scheduling problems that can be modeled using a Petri net framework. Due to their generality, Petri nets are a useful abstraction that captures multiple characteristics of real-life processes. To provide a general solution procedure for the abstraction, we propose three alternative approaches using an indirect scheme to represent the solution: (1) a genetic algorithm that combines two objectives through a weighted fitness function, (2) a non dominated sorting genetic algorithm (NSGA-II) that explicitly addresses the multi-objective nature of the problem and (3) a multi-objective local search approach that simultaneously explores multiple candidate solutions. These algorithms are tested in an extensive computational experiment showing the applicability of this general framework to obtain quality solutions.
AB - In this work, we focus on general multi-objective scheduling problems that can be modeled using a Petri net framework. Due to their generality, Petri nets are a useful abstraction that captures multiple characteristics of real-life processes. To provide a general solution procedure for the abstraction, we propose three alternative approaches using an indirect scheme to represent the solution: (1) a genetic algorithm that combines two objectives through a weighted fitness function, (2) a non dominated sorting genetic algorithm (NSGA-II) that explicitly addresses the multi-objective nature of the problem and (3) a multi-objective local search approach that simultaneously explores multiple candidate solutions. These algorithms are tested in an extensive computational experiment showing the applicability of this general framework to obtain quality solutions.
KW - Machine scheduling
KW - Multi-objective optimization
KW - Petri nets
UR - http://www.scopus.com/inward/record.url?scp=85077919810&partnerID=8YFLogxK
U2 - 10.1016/j.jmsy.2020.01.003
DO - 10.1016/j.jmsy.2020.01.003
M3 - Article
AN - SCOPUS:85077919810
SN - 0278-6125
VL - 54
SP - 272
EP - 284
JO - Journal of Manufacturing Systems
JF - Journal of Manufacturing Systems
ER -