An Agent-Based Reverse Pricing Model for Reducing Bullwhip Effect in Supply Chains
نویسندگان
چکیده
This paper addresses the problem of increasing order variances in multi-tier supply chains. The majority of current approaches for reducing this problem, namely the bullwhip effect, rely on information sharing and/or cooperative planning in inter-organizational systems. Due to multiple barriers in implementing these approaches, we maintain the local autonomy of the participants in the supply chain and provide a multiagent-oriented solution to the problem. In particular, we design an agent-based reverse pricing model for matching supply and demand between independent agents. We adopt reverse pricing for operational procurement decisions and matchmaking that can be automated to a large extent. We evaluate our proposal by conducting a simulation study using a multiagent-based simulation system, and show that the novel approach results in a significant reduction of the bullwhip effect.
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