Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Enabling agents to work together
Communications of the ACM
Distributed intelligent executive information systems
Decision Support Systems - Special issue on executive information systems
Parallel genetic algorithms with local search
Computers and Operations Research - Special issue: artificial intelligence, evolutionary programming and operations research
Foundations of distributed artificial intelligence
Foundations of distributed artificial intelligence
A distributed decision support system for strategic planning
Decision Support Systems - Special issue: intelligent agents as a basis for decision support systems
Information distortion in a supply chain: the bullwhip effect
Management Science - Special issue on frontier research in manufacturing and logistics
Future trends in model management systems: parallel and distributed extensions
Decision Support Systems
Value of Information in Capacitated Supply Chains
Management Science
An agent-based framework for building decision support systems
Decision Support Systems - Special issue on decision support technologies for complex and open organizations
Efficient mechanisms for the supply of services in multi-agent environments
Decision Support Systems - Special issue on information and computational economics
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
The Value of Information Sharing in a Two-Level Supply Chain
Management Science
Supply Chain Inventory Management and the Value of Shared Information
Management Science
| Hi-index | 0.00 |
Supply chain management critically affects businesses' abilities to obtain and sustain competitive advantages. This paper presents a systematic approach to tackle the issue of the bullwhip effect in supply chain management. First, we proposed a multi-agent supply chain framework and compare it with the traditional sequential supply chain framework. Secondly, we hypothesise that the multi-agent supply chain framework would be more effective in alleviating the bullwhip effect than the sequential supply chain framework. Finally, we describe a case study that was conducted to test the proposed hypothesis. The findings suggested the multi-agent supply chain framework outperformed the sequential supply chain in alleviating the bullwhip effect. Supply chain management critically affects businesses' abilities to obtain and sustain competitive advantages. This paper presents a systematic approach to tackle the issue of the bullwhip effect in supply chain management. First, we proposed a multi-agent supply chain framework and compare it with the traditional sequential supply chain framework. Secondly, we hypothesise that the multi-agent supply chain framework would be more effective in alleviating the bullwhip effect than the sequential supply chain framework. Finally, we describe a case study that was conducted to test the proposed hypothesis. The findings suggested the multi-agent supply chain framework outperformed the sequential supply chain in alleviating the bullwhip effect.