TY - GEN
T1 - Energy-efficient rescheduling for the flexible machining systems with random machine breakdown and urgent job arrival
AU - Kou, Yang
AU - Li, Congbo
AU - Li, Li
AU - Tang, Ying
AU - Li, Xiaoou
N1 - Funding Information:
*This work is supported in part by the National Key R&D Program of China (No.2017YFF0207903), the Chongqing Technology Innovation and Application Program (No. cstc2018jszx-cyzdX0183) and the Fundamental Research Funds for the Central Universities of China (No. cqu2018CDHB1B07) Yang Kou and Congbo Li are with State Key Laboratory of Mechanical Transmission, Chongqing University, China (Phone: 0086-13752908261, email: congboli@cqu.edu.cn) Li Li is with College of engineering and technology, Southwest University, China (email: cqulily@163.com) Ying Tang is with Department of Electrical and Computer Engineering, Rowan University, Glassboro, USA and Institute of Smart Education, Qingdao Academy of Intelligent Industries, Qingdao, China (email: tang@rowan.edu) Xiaoou Li is with Department of Computer Science CINVESTAV-IPN Mexico (email: lixo@cs.cinvestav.mx).
PY - 2019/10
Y1 - 2019/10
N2 - This paper investigated a dynamic rescheduling problem for a flexible machining system with random machine breakdown and urgent job arrivals. The energy consumption characteristics of the machining system is explicitly analyzed by considering multiple flexibilities with related to process routes and machine tool selection as well as dynamic events. Then a multi-objective optimization model of dynamic rescheduling is presented to take minimum energy consumption and minimum makespan as objectives, which is solved by a MOGSA algorithm. Case studies with random urgent job arrival and machine breakdown are implemented and the experimental results show that the proposed approach is effective for energy saving through rescheduling.
AB - This paper investigated a dynamic rescheduling problem for a flexible machining system with random machine breakdown and urgent job arrivals. The energy consumption characteristics of the machining system is explicitly analyzed by considering multiple flexibilities with related to process routes and machine tool selection as well as dynamic events. Then a multi-objective optimization model of dynamic rescheduling is presented to take minimum energy consumption and minimum makespan as objectives, which is solved by a MOGSA algorithm. Case studies with random urgent job arrival and machine breakdown are implemented and the experimental results show that the proposed approach is effective for energy saving through rescheduling.
UR - http://www.scopus.com/inward/record.url?scp=85076759824&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85076759824&partnerID=8YFLogxK
U2 - 10.1109/SMC.2019.8914436
DO - 10.1109/SMC.2019.8914436
M3 - Conference contribution
AN - SCOPUS:85076759824
T3 - Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
SP - 409
EP - 414
BT - 2019 IEEE International Conference on Systems, Man and Cybernetics, SMC 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 IEEE International Conference on Systems, Man and Cybernetics, SMC 2019
Y2 - 6 October 2019 through 9 October 2019
ER -