Scheduling in the Random-Order Model

Online Appointment Scheduling in the Random Order Model

We consider the following online appointment scheduling problem: Jobs of different processing times and weights arrive online step-by-step. Upon arrival of a job, its (future) starting date has to be determined immediately and irrevocably before the next job arrives, with the objective of minimizing the average weighted completion time. In this type of scheduling problem it is impossible to ach...

Online optimization in the random-order model

In an online problem, information is revealed incrementally and decisions have to be made before the full information is known. This occurs in various applications like, for example, resource allocation or online ad assignment. To analyze the performance of algorithms for online problems, it is classically assumed that there is a malicious adversary who always provides the worst-possible input....

Integrated model for pricing, delivery time setting, and scheduling in make-to-order environments

Usually, in make-to-order environments which work only in response to the customer’s orders, manufacturers for maximizing the profits should offer the best price and delivery time for an order considering the existing capacity and the customer’s sensitivity to both the factors. In this paper, an integrated approach for pricing, delivery time setting and scheduling of new arrival orders are prop...

Online Graph Edge-Coloring in the Random-Order Arrival Model

A classic theorem by Vizing asserts that if the maximum degree of a graph is ∆, then it is possible to color its edges, in polynomial time, using at most ∆+ 1 colors. However, this algorithm is offline, i. e., it assumes the whole graph is known in advance. A natural question then is how well we can do in the online setting, where the edges of the graph are revealed one by one, and we need to c...

Autoregressive Gaussian Random Vectors of First Order