Decision on SIG-2026-0420
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MSOM Conference
May 8, 2026, 5:15:41 PM (7 days ago) May 8
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08-May-2026
Re: SIG-2026-0420, "Online Allocation with Diffusion: Managing Humanitarian Logistics Under Victim Migration"
SIG Day Decision: Reject
Dear Author (this is to ensure anonymity):
We received many excellent submissions for the Healthcare Operations Management SIG-Day Conference. Unfortunately, we were unable to accept all of them to be included in the program, and we are sorry to say that your paper was not accepted to the SIG-Day conference.
If you also submitted an extended abstract of your paper to the main MSOM Conference, a decision on that submission will come separately.
Sincerely,
Healthcare Operations;SIG Co-Chairs
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Referee: 1
Please summarize the paper briefly. - Rev: The authors study an online allocation model under the random permutation setting. In each time period, a different amount of resource arrives, and the decision maker has to allocate this resource to N demand points. Different from classical settings, the residual demands in the N points could move from one to another, via a diffusion process. The sequential arrival of demands capture supply uncertainty in humanitarian logistics, while the diffusion models how refugees move from a place to another. The authors propose an online algorithm that achieves a non trivial performance guarantee, as compared to blindly applying existing tools.
Referee: 2
Please summarize the paper briefly. - Rev: The paper introduces an online allocation with diffusion (OAD) model for post-disaster emergency resource allocation that jointly accounts for uncertain relief supply arrivals and dynamic demand shifts caused by victim migration between demand nodes. Its main technical contribution is a reduction framework that exploits the rapid mixing of the diffusion matrix to replace the original high-dimensional, state-dependent constraints with a single aggregate inventory constraint, enabling a provably near-optimal competitive ratio and sublinear regret. Numerical experiments confirm that the resulting algorithm (DLOAD) outperforms greedy baselines across a range of network sizes, time horizons, and diffusion parameters.-
Referee: 1
Comments to the Author (old)
(There are no comments.)
Referee: 2
Comments to the Author (old)
(There are no comments.)-
Re: SIG-2026-0420, "Online Allocation with Diffusion: Managing Humanitarian Logistics Under Victim Migration"
SIG Day Decision: Reject
Dear Author (this is to ensure anonymity):
We received many excellent submissions for the Healthcare Operations Management SIG-Day Conference. Unfortunately, we were unable to accept all of them to be included in the program, and we are sorry to say that your paper was not accepted to the SIG-Day conference.
If you also submitted an extended abstract of your paper to the main MSOM Conference, a decision on that submission will come separately.
Sincerely,
Healthcare Operations;SIG Co-Chairs
---------------------
Referee: 1
Please summarize the paper briefly. - Rev: The authors study an online allocation model under the random permutation setting. In each time period, a different amount of resource arrives, and the decision maker has to allocate this resource to N demand points. Different from classical settings, the residual demands in the N points could move from one to another, via a diffusion process. The sequential arrival of demands capture supply uncertainty in humanitarian logistics, while the diffusion models how refugees move from a place to another. The authors propose an online algorithm that achieves a non trivial performance guarantee, as compared to blindly applying existing tools.
Referee: 2
Please summarize the paper briefly. - Rev: The paper introduces an online allocation with diffusion (OAD) model for post-disaster emergency resource allocation that jointly accounts for uncertain relief supply arrivals and dynamic demand shifts caused by victim migration between demand nodes. Its main technical contribution is a reduction framework that exploits the rapid mixing of the diffusion matrix to replace the original high-dimensional, state-dependent constraints with a single aggregate inventory constraint, enabling a provably near-optimal competitive ratio and sublinear regret. Numerical experiments confirm that the resulting algorithm (DLOAD) outperforms greedy baselines across a range of network sizes, time horizons, and diffusion parameters.-
Referee: 1
Comments to the Author (old)
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Referee: 2
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(There are no comments.)-
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