An introduction to genetic algorithms
An introduction to genetic algorithms
Multiagent systems: a modern approach to distributed artificial intelligence
Multiagent systems: a modern approach to distributed artificial intelligence
Competitive and Cooperative Inventory Policies in a Two-Stage Supply Chain
Management Science
Agent-based computational economics: modeling economies as complex adaptive systems
Information Sciences—Informatics and Computer Science: An International Journal
A Supply Network Model with Base-Stock Control and Service Requirements
Operations Research
Market-based recommendation: Agents that compete for consumer attention
ACM Transactions on Internet Technology (TOIT)
A Comparative Study of Game Theoretic and Evolutionary Models of Bargaining for Software Agents
Artificial Intelligence Review
Exploring bidding strategies for market-based scheduling
Decision Support Systems - Special issue: The fourth ACM conference on electronic commerce
Bargaining with incomplete information
Annals of Mathematics and Artificial Intelligence
Price prediction in a trading agent competition
Journal of Artificial Intelligence Research
A software framework for automated negotiation
Software Engineering for Multi-Agent Systems III
Hi-index | 0.00 |
In this paper, we look at a supply chain of commodity goods where customer demand is uncertain and partly based on reputation, and where raw material replenishment is uncertain in both the amount that is available, as well as the price to pay. Successful participation in such supply chains requires a good inventory management strategy. Actors must find a balance between inventory costs and client satisfaction: structurally high inventory costs reduces the profit, but customers that are faced with a depleted supplier will lose confidence and next time purchase from a competitor. This paper presents a model and a simulation environment to learn successful strategies for participation in this type of supply chains. We combine evolutionary algorithms with logistic theories, and use them in a case in a petrochemical setting. We show that software agents are capable of learning basic and more complex strategies, and that complex learned strategies perform better than basic learned strategies.