We consider a scheduling problem where a set of jobs is distributed over parallel machines. The processing time of any job is dependent on the usage of a scarce renewable resource, e.g., personnel. An amount of k units of that resource can be allocated to the jobs at any time, and the more of...

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 consider parallel, identical machine scheduling problems where the jobs are subject to precedence constraints, release dates, and the processing times of jobs are governed by independent probability distributions. The objective is to minimize the expected value of the total weighted...

We consider several parallel machine scheduling settings with the objective to minimize the schedule makespan. The most general of these settings is unrelated parallel machine scheduling. We assume that, in addition to its machine dependence, the processing time of any job is dependent on the...

The purpose of this paper is to present examples which show that deterministic and stochastic scheduling problems often have a surprisingly different behavior. In particular, it demonstrates some seemingly counterintuitive properties of optimal scheduling policies for stochastic machine...

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...

The property of an allocation rule to be implementable in dominant strategies by a unique payment scheme is called revenue equivalence. In this paper we give a characterization of revenue equivalence based on a graph theoretic interpretation of the incentive compatibility constraints. The...

In this paper, we survey different models, techniques, and some recent results to tackle machine scheduling problems within a distributed setting. In traditional optimization, a central authority is asked to solve a (computationally hard) optimization problem. In contrast, in distributed...

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 consider the problem to price (digital) items in order to maximize the revenue obtainable from a set of bidders. We suggest a natural monotonicity constraint on bundle prices, show that the problem remains NP-hard, and we derive a PTAS. We also discuss a special case, the highway pricing problem.