Online Lead Time Quotation with Predictions

Published Online:https://doi.org/10.1287/ijoc.2025.1104

References

  • Aminian MR, Ma W, Xin L (2023) Online job selection: Reward rate vs. remaining value. Preprint, submitted December 1, https://doi.org/10.2139/ssrn.4644495.Google Scholar
  • An L, Li AA, Moseley B, Visotsky G (2024) Best of many in both worlds: Online resource allocation with predictions under unknown arrival model. Preprint, submitted February 21, https://arxiv.org/abs/2402.13530.Google Scholar
  • Andelman N, Mansour Y, Zhu A (2003) Competitive queueing policies for QoS switches. SODA’03: Proc. 14th Annual ACM-SIAM Sympos. Discrete Algorithms (Society for Industrial and Applied Mathematics, Philadelphia), 761–770.Google Scholar
  • Balakrishnan A, Geunes J (2003) Production planning with flexible product specifications: An application to specialty steel manufacturing. Oper. Res. 51(1):94–112.LinkGoogle Scholar
  • Balseiro S, Kroer C, Kumar R (2022) Single-leg revenue management with advice. Oper. Res. 74(1):243–259.Google Scholar
  • Bamas E, Maggiori A, Svensson O (2020) The primal-dual method for learning augmented algorithms. Adv. Neural Inform. Processing Systems 33:20083–20094.Google Scholar
  • Cho WH, Henderson S, Shmoys D (2022) Scheduling with predictions. Preprint, submitted December 20, https://arxiv.org/abs/2212.10433.Google Scholar
  • Golrezaei N, Jaillet P, Zhou Z (2023) Online resource allocation with convex-set machine-learned advice. Preprint, submitted June 21, https://arxiv.org/abs/2306.12282.Google Scholar
  • Grigorescu E, Lin YS, Song M (2024) Learning-augmented algorithms for online concave packing and convex covering problems. Preprint, submitted November 13, https://arxiv.org/abs/2411.08332.Google Scholar
  • Grigorescu E, Lin YS, Silwal S, Song M, Zhou S (2022) Learning-augmented algorithms for online linear and semidefinite programming. Adv. Neural Inform. Processing Systems 35:38643–38654.CrossrefGoogle Scholar
  • Hajek B (2001) On the competitiveness of on-line scheduling of unit-length packets with hard deadlines in slotted time. Proc. 2001 Conf. Inform. Sci. Systems (Institute of Electrical and Electronics Engineers, Piscataway, NJ), 434–438.Google Scholar
  • Holweg M, Pil FK (2001) Successful build-to-order strategies start with the customer. MIT Sloan Management Rev. 43(1):74.Google Scholar
  • Huo T (2024) Online and dynamic algorithms for revenue management applications. PhD thesis, National University of Singapore, Singapore.Google Scholar
  • Huo T, Cheung WC (2026) Online lead time quotation with predictions. https://doi.org/10.1287/ijoc.2025.1104.cd, https://github.com/INFORMSJoC/2025.1104.Google Scholar
  • Jin B, Ma W (2022) Online bipartite matching with advice: Tight robustness-consistency tradeoffs for the two-stage model. Adv. Neural Inform. Processing Systems 35:14555–14567.CrossrefGoogle Scholar
  • Keskinocak P (1997) Satisfying Customer Due Dates Effectively (Carnegie Mellon University, Pittsburgh).Google Scholar
  • Keskinocak P, Ravi R, Tayur S (2001) Scheduling and reliable lead-time quotation for orders with availability intervals and lead-time sensitive revenues. Management Sci. 47(2):264–279.LinkGoogle Scholar
  • Kesselman A, Lotker Z, Mansour Y, Patt-Shamir B, Schieber B, Sviridenko M (2001) Buffer overflow management in QoS switches. STOC’01: Proc. 33rd Annual ACM Sympos. Theory Comput. (Association for Computing Machinery, New York), 520–529.Google Scholar
  • Lattanzi S, Lavastida T, Moseley B, Vassilvitskii S (2020) Online scheduling via learned weights. SODA’20: Proc. 31st Annual ACM-SIAM Sympos. Discrete Algorithms (Society for Industrial and Applied Mathematics, Philadelphia), 1859–1877.Google Scholar
  • Lavastida T, Moseley B, Ravi R, Xu C (2021) Using predicted weights for ad delivery. SIAM Conf. Appl. Comput. Discrete Algorithms (ACDA21) (Society for Industrial and Applied Mathematics, Philadelphia), 21–31.Google Scholar
  • Lykouris T, Vassilvitskii S (2021) Competitive caching with machine learned advice. J. ACM 68(4):1–25.CrossrefGoogle Scholar
  • Ma W, Simchi-Levi D, Zhao J (2021) Dynamic pricing (and assortment) under a static calendar. Management Sci. 67(4):2292–2313.LinkGoogle Scholar
  • Purohit M, Svitkina Z, Kumar R (2018) Improving online algorithms via ML predictions. Adv. Neural Inform. Processing Systems 31:9684–9693.Google Scholar
  • Rohatgi D (2020) Near-optimal bounds for online caching with machine learned advice. SODA’20: Proc. 31st Annual ACM-SIAM Sympos. Discrete Algorithms (Society for Industrial and Applied Mathematics, Philadelphia), 1834–1845.Google Scholar
  • Slotnick SA (2011) Optimal and heuristic lead-time quotation for an integrated steel mill with a minimum batch size. Eur. J. Oper. Res. 210(3):527–536.CrossrefGoogle Scholar
  • Stein C, Wei HT (2023) Learning-augmented online packet scheduling with deadlines. Preprint, submitted May 11, https://arxiv.org/abs/2305.07164.Google Scholar
  • Veselỳ P (2021) Packet scheduling: Plans, monotonicity, and the golden ratio. ACM SIGACT News 52(2):72–84.CrossrefGoogle Scholar
  • Welling TL, Noel LQ, Ismail A (2021) Identifying potentials and impacts of lead-time based pricing in semiconductor supply chains with discrete-event simulation. 2021 Winter Simulation Conf. (WSC) (IEEE, Piscataway, NJ), 1–12.Google Scholar
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