TY - GEN
T1 - Equitable Scheduling on a Single Machine
AU - Heeger, Klaus
AU - Hermelin, Danny
AU - Mertzios, George B.
AU - Molter, Hendrik
AU - Niedermeier, Rolf
AU - Shabtay, Dvir
N1 - Funding Information:
*Supported by DFG RTG 2434 “Facets of Complexity”. †Supported by the EPSRC grant EP/P020372/1. ‡Supported by the DFG, project MATE (NI 369/17).
Funding Information:
Supported by DFG RTG 2434 "Facets of Complexity". Supported by the EPSRC grant EP/P020372/1. Supported by the DFG, project MATE (NI 369/17).
Publisher Copyright:
Copyright © 2021, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
PY - 2021/1/1
Y1 - 2021/1/1
N2 - We introduce a natural but seemingly yet unstudied generalization of the problem of scheduling jobs on a single machine so as to minimize the number of tardy jobs. Our generalization lies in simultaneously considering several instances of the problem at once. In particular, we have n clients over a period of m days, where each client has a single job with its own processing time and deadline per day. Our goal is to provide a schedule for each of the m days, so that each client is guaranteed to have their job meet its deadline in at least k ≤ m days. This corresponds to an equitable schedule where each client is guaranteed a minimal level of service throughout the period of m days. We provide a thorough analysis of the computational complexity of three main variants of this problem, identifying both efficient algorithms and worst-case intractability results.
AB - We introduce a natural but seemingly yet unstudied generalization of the problem of scheduling jobs on a single machine so as to minimize the number of tardy jobs. Our generalization lies in simultaneously considering several instances of the problem at once. In particular, we have n clients over a period of m days, where each client has a single job with its own processing time and deadline per day. Our goal is to provide a schedule for each of the m days, so that each client is guaranteed to have their job meet its deadline in at least k ≤ m days. This corresponds to an equitable schedule where each client is guaranteed a minimal level of service throughout the period of m days. We provide a thorough analysis of the computational complexity of three main variants of this problem, identifying both efficient algorithms and worst-case intractability results.
KW - Computer Science - Discrete Mathematics
KW - Computer Science - Data Structures and Algorithms
UR - http://www.scopus.com/inward/record.url?scp=85102548712&partnerID=8YFLogxK
M3 - Conference contribution
T3 - 35th AAAI Conference on Artificial Intelligence, AAAI 2021
SP - 11818
EP - 11825
BT - 35th AAAI Conference on Artificial Intelligence, AAAI 2021
PB - Association for the Advancement of Artificial Intelligence
T2 - 35th AAAI Conference on Artificial Intelligence, AAAI 2021
Y2 - 2 February 2021 through 9 February 2021
ER -