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In this paper a new intelligent multi-agent system is proposed for finding the best ordering policy. The best ordering policy is the policy which minimizes the total cost of the supply chain that is the sum of all echelons' costs over all periods. The best ordering policy is obtained by a new window-base genetic algorithm. One limitation of the previous presented GA-based algorithms is the constraint...
The repeated game theory model of information sharing in supply chain is formulated in this paper. By the simulation experiments based on Multi-Agent, we analyze the influence of forming and maintaining the cooperation of information sharing in supply chain. It's found that the sharing costs and the punitive coefficient are the main factors influencing the level of cooperation in supply chain. In...
In this work we present a learning method based in temporal differences for learning the market prices for a supply chain manager in the TAC-SCM game scenario. The agent has two learning methods based in temporal differences. In one hand he learns the short term tendency of the market to adjust its prices proportionally to the rise or drop. He also learns a value of probability to elevate the prices...
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