TY - GEN
T1 - A genetic algorithm for unrelated parallel machine scheduling minimizing makespan cost and electricity cost under time-of-use (TOU) tariffs with job delay mechanism
AU - Kurniawan, B.
AU - Gozali, A. A.
AU - Weng, W.
AU - Fujimura, S.
N1 - Funding Information:
This research is supported by Indonesia Endowment Fund for Education (LPDP).
Publisher Copyright:
© 2017 IEEE.
Copyright:
Copyright 2018 Elsevier B.V., All rights reserved.
PY - 2018/2/9
Y1 - 2018/2/9
N2 - Unrelated parallel machine scheduling under time-of-use electricity price is addressed in this paper. In this setting, price of electricity can be different among various periods of the day. The objective is to minimize total cost consisting of makespan cost and electricity cost. Genetic algorithm (GA) is used to solve the unrelated parallel machine scheduling under time varying tariffs. Chromosome decoding, inspired by greedy total cost, is proposed to transform individual into feasible schedule. Furthermore, generated schedule from the individual is improved by job delay mechanism that shifts jobs to other periods to avoid high electricity cost. Finally, numerical experiment is conducted to implement the approach. Preliminary result shows that our proposed approach is effective and efficient to solve the corresponding problem.
AB - Unrelated parallel machine scheduling under time-of-use electricity price is addressed in this paper. In this setting, price of electricity can be different among various periods of the day. The objective is to minimize total cost consisting of makespan cost and electricity cost. Genetic algorithm (GA) is used to solve the unrelated parallel machine scheduling under time varying tariffs. Chromosome decoding, inspired by greedy total cost, is proposed to transform individual into feasible schedule. Furthermore, generated schedule from the individual is improved by job delay mechanism that shifts jobs to other periods to avoid high electricity cost. Finally, numerical experiment is conducted to implement the approach. Preliminary result shows that our proposed approach is effective and efficient to solve the corresponding problem.
KW - Genetic algorithm
KW - electricity cost
KW - makespan cost
KW - time-of-use tariffs
KW - unrelated parallel machine scheduling with job delay mechanism
UR - http://www.scopus.com/inward/record.url?scp=85045272386&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85045272386&partnerID=8YFLogxK
U2 - 10.1109/IEEM.2017.8289958
DO - 10.1109/IEEM.2017.8289958
M3 - Conference contribution
AN - SCOPUS:85045272386
T3 - IEEE International Conference on Industrial Engineering and Engineering Management
SP - 583
EP - 587
BT - 2017 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2017
PB - IEEE Computer Society
T2 - 2017 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2017
Y2 - 10 December 2017 through 13 December 2017
ER -