Due to the COVID-19 pandemic, the global supply chain is disrupted at an unprecedented scale under uncertain and unknown trends of labor shortage, high material prices, and changing travel or trade regulations. To stay competitive, enterprises desire agile and dynamic response strategies to quickly react to disruptions and recover supply-chain functions. Although both centralized and multi-agent approaches have been studied, their implementation requires prior knowledge of disruptions and agent-rule-based reasoning. In this paper, we introduce a model-based multi-agent framework that enables agent coordination and dynamic agent decision-making to respond to supply chain disruptions in an agile and effective manner. Through a small-scale simulated case study, we showcase the feasibility of the proposed approach under several disruption scenarios that affect a supply chain network differently, and analyze performance trade-offs between the proposed distributed and centralized methods.

HAPTIC is individuals - agents or humans - within them to establish grounded in game theory and probabilistic modeling. It has successful relationships with their partners. In Supply been proved that HAPTIC agents learn other agents' behaviors Chain Management (SCM), establishing trust improves the reliably using direct observations. One shortcoming of chances of a successful supply chain relationship, and increases HAPTIC is that it does not support reported observations.

HAPTIC is grounded in game theory and probabilistic modeling. It has been proved that My thesis contributes to the field of multi-agent HAPTIC agents learn other agents' behaviors reliably using systems by proposing a novel trust-based decision direct observations. One shortcoming of HAPTIC is that it model for supply chain management.

Many real-world applications, such as Supply Chain Management (SCM), can be modeled using multi-agent systems. One shortcoming of current SCM models is that their trust models are ad hoc and do not have a strong theoretical basis. We propose a trust model for SCM that is grounded in probabilistic game theory. In this model, trust can be gained through direct interactions, and/or by asking for information from other trustworthy agents. We will use this model to simulate and study supply chain market behavior.

One of the critical factors for a successful cooperative relationship in a supply chain partnership is trust. Many real-world applications, such as Supply Chain Management (SCM), can be modeled using multi-agent systems. One shortcoming of current SCM models is that their trust models are ad hoc and do not have a strong theoretical basis. As a result, they are unable to model subtleties in agent behavior that can be used to build a more accurate trust model. We propose a trust model for SCM that is grounded in probabilistic game theory. In this model, trust can be gained through direct interactions and/or by asking for information from other trustworthy agents. We will use this model to simulate and study supply chain market behavior.