Nonsystematic backtracking search
Nonsystematic backtracking search
Decentralized Multi-Echelon Supply Chains: Incentives and Information
Management Science
The Quantity Flexibility Contract and Supplier-Customer Incentives
Management Science
A General Framework for the Study of Decentralized Distribution Systems
Manufacturing & Service Operations Management
Adopt: asynchronous distributed constraint optimization with quality guarantees
Artificial Intelligence - Special issue: Distributed constraint satisfaction
Analysis of a Decentralized Supply Chain Under Partial Cooperation
Manufacturing & Service Operations Management
Depth-bounded discrepancy search
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Cooperative supply chain re-scheduling: the case of an engine supply chain
CDVE'09 Proceedings of the 6th international conference on Cooperative design, visualization, and engineering
Computers and Industrial Engineering
Discovering business intelligence from online product reviews: A rule-induction framework
Expert Systems with Applications: An International Journal
Engineering Applications of Artificial Intelligence
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This paper studies the case of a supply chain made up of autonomous facilities (represented by software agents). They need to coordinate their manufacturing operations in order to optimize customer satisfaction. Most of the coordination mechanisms used in practice can be described as heuristics. We show how they can be generalized to consider the entire coordination space, which can be represented as a tree. This reformulation of the coordination problem as a tree calls for its optimization using a distributed tree search algorithm (e.g. SyncBB). This allows for the exploration of alternative solutions by the agents while maintaining current business relationships, responsibilities and local decision-making algorithms. SyncBB provided great improvements in solution quality in comparison with current practice. The main contribution of this paper is MacDS, a novel method which permits agents to systematically search the solution space (thus look for the optimal solution) but aims at producing good solutions in a short period of time. It uses the concept of discrepancy so that agents collectively prioritize the parts of the tree to search first. Moreover, MacDS allows agents to work concurrently so as to speed up the search process. Use of this mechanism has improved the quality of solutions and computation time for both real industrial problems and generated problems.