There has been recent interest in using machine-learned predictions to improve the worst-
case guarantees of online algorithms. In this paper we continue this line of work by studying …

Motivated by the success of using black-box predictive algorithms as subroutines for online
decision-making, we develop a new framework for designing online policies given access to …

Cloud manufacturing (CMfg) emerges as a promising manufacturing paradigm, where
service composition (SC) is a critical process concentrating on matching tasks and services …

This chapter introduces the random-order model in online algorithms. In this model, the input
is chosen by an adversary, then randomly permuted before being presented to the …

A large body of work in Computer Science and Operations Research study online algorithms
for stochastic resource allocation problems. The most common assumption is that the online …

We take a unifying approach to single selection optimal stopping problems with random
arrival order and independent sampling of items. In the problem we consider, a decision …

The knapsack problem is one of the classical problems in combinatorial optimization: Given
a set of items, each specified by its size and profit, the goal is to find a maximum profit …

We study the performance of a proportional weights algorithm for online capacitated bipartite
matching modeling the delivery of impression ads. The algorithm uses predictions on the …

We study the online facility location problem with uniform facility costs in the random-order
model. Meyerson's algorithm [FOCS'01] is arguably the most natural and simple online …

The rapid development of online platforms has inspired a wide range of applications for
timely resources allocations, such as the hotel booking, the cargo logistics, the cloud servers …