We consider online scheduling on unrelated (heterogeneous) machines in a speed-
oblivious setting, where an algorithm is unaware of the exact job-dependent processing …

The study of online algorithms with machine-learned predictions has gained considerable
prominence in recent years. One of the common objectives in the design and analysis of …

Algorithms with predictions is a recent framework that has been used to overcome
pessimistic worst-case bounds in incomplete information settings. In the context of …

We study three two-stage optimization problems with a similar structure and different
objectives. In the first stage of each problem, the goal is to assign input jobs of positive sizes …

The study of online algorithms with machine-learned predictions has gained considerable
prominence in recent years. One of the common objectives in the design and analysis of …

We consider a new scheduling problem on parallel identical machines in which the number
of machines is initially not known, but it follows a given probability distribution. Only after all …

Speed-robust scheduling is the following two-stage problem of scheduling $ n $ jobs on $ m
$ uniformly related machines. In the first stage, the algorithm receives the value of $ m $ and …

Uncertainty surrounds us daily, indicating the need for effective decision-making strategies.
In recent years, the large amount of available data has accelerated the development of …

Unrelated machines are an abstraction of many scheduling environments appearing in
practical applications, where every job may be processed at a different speed on every …

Speed-robust scheduling is a two-stage scheduling problem with a makespan objective. We
are given processing times of n jobs, number of machines m and number of bags b. We …