TY - JOUR
T1 - Resource cost aware scheduling
AU - Carrasco, Rodrigo A.
AU - Iyengar, Garud
AU - Stein, Cliff
N1 - Publisher Copyright:
© 2018 Elsevier B.V.
PY - 2018/9/1
Y1 - 2018/9/1
N2 - We are interested in the scheduling problem where there are several different resources that determine the speed at which a job runs and we pay depending on the amount of each resource that we use. This work is an extension of the resource dependent job processing time problem and the energy aware scheduling problems. We develop a new constant factor approximation algorithm for resource cost aware scheduling problems: the objective is to minimize the sum of the total cost of resources and the total weighted completion time in the one machine non-preemptive setting, allowing for arbitrary precedence constraints and release dates. Our algorithm handles general job-dependent resource cost functions. We also analyze the practical performance of our algorithms, showing that it is significantly superior to the theoretical bounds and in fact it is very close to optimal. The analysis is done using simulations and real instances, which are left publicly available for future benchmarks. We also present additional heuristic improvements and we study their performance in other settings.
AB - We are interested in the scheduling problem where there are several different resources that determine the speed at which a job runs and we pay depending on the amount of each resource that we use. This work is an extension of the resource dependent job processing time problem and the energy aware scheduling problems. We develop a new constant factor approximation algorithm for resource cost aware scheduling problems: the objective is to minimize the sum of the total cost of resources and the total weighted completion time in the one machine non-preemptive setting, allowing for arbitrary precedence constraints and release dates. Our algorithm handles general job-dependent resource cost functions. We also analyze the practical performance of our algorithms, showing that it is significantly superior to the theoretical bounds and in fact it is very close to optimal. The analysis is done using simulations and real instances, which are left publicly available for future benchmarks. We also present additional heuristic improvements and we study their performance in other settings.
KW - Approximation algorithms
KW - Resource aware scheduling
KW - Scheduling
KW - Speed-scaling
UR - http://www.scopus.com/inward/record.url?scp=85044289572&partnerID=8YFLogxK
U2 - 10.1016/j.ejor.2018.02.059
DO - 10.1016/j.ejor.2018.02.059
M3 - Article
AN - SCOPUS:85044289572
SN - 0377-2217
VL - 269
SP - 621
EP - 632
JO - European Journal of Operational Research
JF - European Journal of Operational Research
IS - 2
ER -