Department of Computer Science
On-demand e-supply chain integration: A multi-agent constraint-based approach
With e-business emerging as a key enabler to drive supply chains, the focus of supply chain management has been shifted from production efficiency to customer-driven and partnership synchronization approaches. This strategic shift depends on the match between the demands and offerings that deliver the services. To achieve this, we need to coordinate the flow of information among the services, and link their business processes under various constraints. Existing approaches to this problem have relied on complete information of services and resources, and have failed to adequately address the dynamics and uncertainties of the operating environments. The real-world situation is complicated as a result of undetermined requirements of services involved in the chain, unpredictable solutions contributed by service providers, and dynamic selection and aggregation of solutions to services. This paper examines an agent-mediated approach to on-demand e-business supply chain integration. Each agent works as a service broker, exploring individual service decisions as well as interacting with each other for achieving compatibility and coherence among the decisions of all services. Based on the framework, a prototype has been implemented with simulated experiments highlighting the effectiveness of the approach.
Intelligent agents, Supply chain integration, Electronic business, Constraints management
Source Publication Title
Expert Systems with Applications
This work was supported by a RGC Central Allocation Group Research Grant (HKBU 2/03/C) from Hong Kong Government, and a Seed Funding for Basic Research (200611159216) from the University of Hong Kong. The author thank the editors and reviewers of Expert Systems with Applications, and Professor Kuldeep Kumar for their constructive comments on this paper.
Link to Publisher's Edition
Wang, M., Liu, J., Wang, H., Cheung, W., & Xie, X. (2008). On-demand e-supply chain integration: A multi-agent constraint-based approach. Expert Systems with Applications, 34 (4), 2683-2692. https://doi.org/10.1016/j.eswa.2007.05.041