Designing a Procurement Mechanism based on Q-Learning with an Action-Selection Policy based on PSO Algorithm

In this paper, tender problems in an automobile company for procuring needed items from potential suppliers have been resolved by the learning algorithm Q.
In this case the purchaser with respect to proposals received from potential providers, including price and delivery time is proposed; order the needed parts to suppliers assigns.
The buyer’s objective is minimizing the procurement costs through learning from previous tenders.
We consider this problem as a markov decision problem in which each action is depend on the last state and last action. To resolve this problem, a type of reinforcement learning algorithms (Q-Learning) is developed; in which the particle swarm optimization algorithm is applied to select the optimal action as an optimal action-selection policy in Q-Learning algorithm. In comparison to this algorithm in which the action-selection policy is greedy, this proposed algorithm is more effective and efficient.