TY - GEN
T1 - A Mix Integer Programming Model for Bi-objective Single Machine with Total Weighted Tardiness and Electricity Cost under Time-of-use Tariffs
AU - Kurniawan, B.
AU - Gozali, A. A.
AU - Weng, W.
AU - Fujimura, S.
N1 - Funding Information:
The authors wish to thank to anonymous referees for their valuable feedbacks to improve this paper. This research is supported by Indonesia Endowment Fund for Education (LPDP Indonesia).
Publisher Copyright:
© 2018 IEEE.
Copyright:
Copyright 2019 Elsevier B.V., All rights reserved.
PY - 2019/1/9
Y1 - 2019/1/9
N2 - With the rapid growth of electricity demand, many governments around the world have implemented the energy-conscious policy such as time-of-use policy. This paper addresses a bi-objective single machine scheduling with the total tardiness and electricity cost minimization under time-of-use tariffs. The problem is formulated as a mixed integer programming model. The CPLEX solver solves a small size instance to validate the model. We also describe the procedure to obtain the set of non-dominated solution using commercial solver. The complexity of the model is tested on several problem instances. The results show that the problem is hard to solve even for medium size instances. Hence, we propose a genetic algorithm with random insertion to obtain the set of Pareto solutions for large instances.
AB - With the rapid growth of electricity demand, many governments around the world have implemented the energy-conscious policy such as time-of-use policy. This paper addresses a bi-objective single machine scheduling with the total tardiness and electricity cost minimization under time-of-use tariffs. The problem is formulated as a mixed integer programming model. The CPLEX solver solves a small size instance to validate the model. We also describe the procedure to obtain the set of non-dominated solution using commercial solver. The complexity of the model is tested on several problem instances. The results show that the problem is hard to solve even for medium size instances. Hence, we propose a genetic algorithm with random insertion to obtain the set of Pareto solutions for large instances.
KW - Bi-objective
KW - electricity cost
KW - single machine
KW - time-of-use
KW - total weighted tardiness
UR - http://www.scopus.com/inward/record.url?scp=85061791752&partnerID=8YFLogxK
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U2 - 10.1109/IEEM.2018.8607420
DO - 10.1109/IEEM.2018.8607420
M3 - Conference contribution
AN - SCOPUS:85061791752
T3 - IEEE International Conference on Industrial Engineering and Engineering Management
SP - 137
EP - 141
BT - 2018 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2018
PB - IEEE Computer Society
T2 - 2018 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2018
Y2 - 16 December 2018 through 19 December 2018
ER -