Assessing the accuracy of externalities prediction in a LCFS-PR M/G/1 queue under partial information

Royi Jacobovic*, Nikki Levering

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Consider a LCFS-PR M/G/1 queue and assume that at time t=0, there are n+1 customers c1,c2,...,cn+1 who arrived in that order. In addition, at time t=0 there is an additional customer c with service requirement x>0 who makes an admission request. At time t=0, the system's manager should decide whether to let c join the system or not. To this end, the manager wants to evaluate the externalities generated by c, i.e., the additional waiting time that c1,c2,…,cn+1 will suffer as a consequence of the admission of c. We assume that at the decision epoch the manager knows only n, x, the remaining service time of cn+1 (who was getting service just before c had made his admission request) and the total workload at t=0. In a previous work by Jacobovic, Levering and Boxma (2023), it was shown that the manager can compute the expected externalities (i.e., the natural predictor for the externalities value) but not their variance (i.e., the conventional measure of the predictor's accuracy). Motivated by this problem, in the current work, we study a convex piecewise-linear program which yields the spectrum of variance values which are consistent with the manager's information.

Original languageEnglish
Article number107205
JournalOperations Research Letters
Volume57
DOIs
StatePublished - Nov 2024

Funding

FundersFunder number
Israel Science Foundation3739/24
Horizon 2020945045
Nederlandse Organisatie voor Wetenschappelijk Onderzoek024.002.003
Labex CIMIANR-11-LABX-0040

    Keywords

    • Convex piecewise-linear programming
    • Explicit optimal solution
    • Externalities
    • M/G/1

    Fingerprint

    Dive into the research topics of 'Assessing the accuracy of externalities prediction in a LCFS-PR M/G/1 queue under partial information'. Together they form a unique fingerprint.

    Cite this