Distributed rational decision making
Multiagent systems
Algorithm for optimal winner determination in combinatorial auctions
Artificial Intelligence
Distributed coordination of project schedule changes: an agent-based compensatory negotiation approach
Supply Chain Coordination by Means of Automated Negotiations
HICSS '04 Proceedings of the Proceedings of the 37th Annual Hawaii International Conference on System Sciences (HICSS'04) - Track 3 - Volume 3
Protocols for Negotiating Complex Contracts
IEEE Intelligent Systems
Multi-plant production scheduling in SMEs
Robotics and Computer-Integrated Manufacturing
The Contract Net Protocol: High-Level Communication and Control in a Distributed Problem Solver
IEEE Transactions on Computers
Agent-based distributed manufacturing control: A state-of-the-art survey
Engineering Applications of Artificial Intelligence
Agent-based negotiation and decision making for dynamic supply chain formation
Engineering Applications of Artificial Intelligence
Bus maintenance scheduling using multi-agent systems
Engineering Applications of Artificial Intelligence
Robotics and Computer-Integrated Manufacturing
A game theoretic approach to decentralized multi-project scheduling
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
Evaluating a new communication protocol for real-time distributed control
Robotics and Computer-Integrated Manufacturing
Agent-based modeling of supply chains for distributed scheduling
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
An efficient automated negotiation strategy for complex environments
Engineering Applications of Artificial Intelligence
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Simultaneously running multiple projects are quite common in industries. These projects require local (always available to the concerned project) and global (shared among the projects) resources that are available in limited quantity. The limited availability of the global resources coupled with compelling schedule requirements at different projects leads to resource conflicts among projects. Effectively resolving these resource conflicts is a challenging task for practicing managers. This paper proposes a novel distributed multi-agent system using auctions based negotiation (DMAS/ABN) approach for resolving the resource conflicts and allocating multiple different types of shared resources amongst multiple competing projects. The existing multi-agent system (MAS) using auction makes use of exact methods (e.g. dynamic programming relaxation) for solving winner determination problem to resolve resource conflicts and allocation of single unit of only one type of shared resource. Consequently these methods fail to converge for some multi-project instances and unsuitable for real life large problems. In this paper the multi-unit combinatorial auction is proposed and winner determination problem is solved by efficient new heuristic. The proposed approach can solve complex large-sized multi-project instances without any limiting assumptions regarding the number of activities, shared resources or the number of projects. Additionally our approach further allows to random project release-time of projects which arrives dynamically over the planning horizon. The DMAS/ABN is tested on standard set of 140 problem instances. The results obtained are benchmarked against the three state-of-the-art decentralized algorithms and two existing centralized methods. For 82 of 140 instances DMAS/ABN found new best solutions with respect to average project delay (APD) and produced schedules on an average 16.79% (with maximum 57.09%) lower APD than all the five methods for solving the same class of problems.