Mitigating bullwhip effect in an agent-based supply chain through a fuzzy reverse ultimatum game negotiation module

Abstract

Supply Chain Management is frequently regarded as a distributed system whose productivity is mainly influenced by healthy interaction and cooperation among members. Examples of such inefficiencies can be seen in “Demand amplification” referring to asymmetric increase of demands among supply echelons due to both operational and behavioral causes. This paper addresses defective loops of non-cooperation as well as absence of information to manage the dynamics of demand amplification in a four-echelon supply chain; a new agent-based structure is suggested to facilitate the cooperation and coordination among major components and provide a structured context for interactive information sharing. Adequate motivation to share the required information is derived from an automated negotiation between retailer and manufacturer echelons in a four-echelon serial supply model. In doing so, the retailer agent would be encouraged to share customer demand information using Token-Based ordering policy, through a Reverse Ultimatum Game negotiation module. A novel fuzzy approach is suggested in order to cope with ambiguities involved in this negotiation; Numerical experiments prove that the proposed fuzzy negotiation mechanism can warrant the agreement among negotiating parties in nearly half the number of RUG iterations with 30% agreement share. This success in bringing the negotiation parties to an agreement results in the bullwhip effect decreasing by 30% in a four-echelon agent-based supply system.The proposed agent-based supply model which is designed to facilitate this cooperative decision-making process includes some unique innovative features: the knowledge base of the proposed system is able to retrieve and reuse the previous negotiation outcomes, through a gradual learning module. A combination of Case-Based Reasoning and Rule-Based inference mechanisms are applied to facilitate this, so prior cases will be stored in a Frame-Based structure. Eventually, the integrity of the proposed agent-based system is examined through combining Top-Down and Subsets integration approaches and some numerical experiments are provided to confirm the efficiency of the proposed agent-based structure in bullwhip effect management.