iBundle: an efficient ascending price bundle auction
Proceedings of the 1st ACM conference on Electronic commerce
Single Machine Scheduling with Release Dates
SIAM Journal on Discrete Mathematics
Optimal On-Line Algorithms for Single-Machine Scheduling
Proceedings of the 5th International IPCO Conference on Integer Programming and Combinatorial Optimization
Iterative Combinatorial Auctions: Theory and Practice
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Truthful Mechanisms for One-Parameter Agents
FOCS '01 Proceedings of the 42nd IEEE symposium on Foundations of Computer Science
Mechanism design for online real-time scheduling
EC '04 Proceedings of the 5th ACM conference on Electronic commerce
Adaptive limited-supply online auctions
EC '04 Proceedings of the 5th ACM conference on Electronic commerce
Online Scheduling of a Single Machine to Minimize Total Weighted Completion Time
Mathematics of Operations Research
A Smart Market for Industrial Procurement with Capacity Constraints
Management Science
Online auctions with re-usable goods
Proceedings of the 6th ACM conference on Electronic commerce
Models and Algorithms for Stochastic Online Scheduling
Mathematics of Operations Research
Algorithmic Game Theory
(Almost) optimal coordination mechanisms for unrelated machine scheduling
Proceedings of the nineteenth annual ACM-SIAM symposium on Discrete algorithms
LP-based online scheduling: from single to parallel machines
Mathematical Programming: Series A and B
Decentralization and mechanism design for online machine scheduling
SWAT'06 Proceedings of the 10th Scandinavian conference on Algorithm Theory
Coordination mechanisms for selfish scheduling
WINE'05 Proceedings of the First international conference on Internet and Network Economics
On-line scheduling to minimize average completion time revisited
Operations Research Letters
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Traditional optimization models assume a central decision maker who optimizes a global system performance measure. However, problem data is often distributed among several agents, and agents make autonomous decisions. This gives incentives for strategic behavior of agents, possibly leading to suboptimal system performance. Furthermore, in dynamic environments, machines are locally dispersed and administratively independent. Examples are found both in business and engineering applications. We investigate such issues for a parallel machine scheduling model where jobs arrive online over time. Instead of centrally assigning jobs to machines, each machine implements a local sequencing rule and jobs decide for machines themselves. In this context, we introduce the concept of a myopic best-response equilibrium, a concept weaker than the classical dominant strategy equilibrium, but appropriate for online problems. Our main result is a polynomial time, online mechanism that---assuming rational behavior of jobs---results in an equilibrium schedule that is 3.281-competitive with respect to the maximal social welfare. This is only slightly worse than state-of-the-art algorithms with central coordination.