Dynamic rule refinement in knowledge-based data mining systems
Decision Support Systems - Special issue on decision support in the new millennium
Computers play the beer game: can artificial agents manage supply chains?
Decision Support Systems - Special issue: Formal modeling and electronic commerce
Web-based Supply Chain Management
Information Systems Frontiers
Market protocols for decentralized supply chain formation
Market protocols for decentralized supply chain formation
Decentralized Mechanism Design for Supply Chain Organizations Using an Auction Market
Information Systems Research
Using multi-agent simulation and learning to design new businessprocesses
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Outsourcer selection and order tracking in a supply chain by mobile agents
Computers and Industrial Engineering
An algorithm portfolio based solution methodology to solve a supply chain optimization problem
Expert Systems with Applications: An International Journal
Chromosome refinement for optimising multiple supply chains
Information Sciences: an International Journal
Complex adaptive supply chain network: the state of the art
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
Hi-index | 12.06 |
Recent trend in eCommerce applications toward effectively reducing supply chain costs-including spatial, temporal, and monetary resources-has spurred interest among researchers as well as practitioners to efficiently utilize supply chains. One of the least studied of these views is adaptive or dynamic configuration of supply chains. This problem is relatively new since faster communications over the Internet or by any other means and the willingness to utilize it for effective management of supply chains did not exist a few decades ago. The proposed framework addresses the problem of supply chain configuration. We incorporate machine-learning techniques to develop a dynamically configurable supply chain framework, and evaluate its effectiveness with respect to comparable static supply chains. Specifically, we consider the case where several parts go into the production of a product. A single supplier or a combination of suppliers could supply these parts. The proposed framework automatically forms the supply chain dynamically as per the dictates of incoming orders and the constraints from suppliers upstream.