The paper introduces a model for online parallel machine scheduling, where any single machine is run on the basis of a locally optimal sequencing policy. Jobs choose the machine on which they want to be processed themselves, and in addition, any job owns a piece of private information, namely...

We study the online version of the classical parallel machine scheduling problem to minimize the total weighted completion time from a new perspective: We assume a strategic setting, where the data of each job j, namely its release date r(j) , its processing time p(j) and its weight w(j) is only...

The paper introduces a model for online parallel machine scheduling, where any single machine is run on the basis of a locally optimal sequencing policy. Jobs choose the machine on which they want to be processed themselves, and in addition, any job owns a piece of private information, namely...

This paper provides a characterization of dominant strategy mechanisms with quasi-linear utilities and multi-dimensional types for a variety of preference domains. These characterizations are in terms of a monotonicity property on the underlying allocation rule.

We study the design of optimal mechanisms in a setting where job-agents compete for being processed by a service provider that can handle one job at a time. Each job has a processing time and incurs a waiting cost. Jobs need to be compensated for waiting. We consider two models, one where only...

We study the online version of the classical parallel machine scheduling problem to minimize the total weighted completion time from a new perspective: We assume a strategic setting, where the data of each job j, namely its release date r(j) , its processing time p(j) and its weight w(j) is only...

We investigate the algorithmic performance of Vickrey-Clarke-Groves mechanisms in the single item case. We provide a formal definition of a Vickrey algorithm for this framework, and give a number of examples of Vickrey algorithms. We consider three performance criteria, one corresponding to a...