We study the supervisory control of supply chain operation modelled by (timed) State Tree Structures (STS). We model each agent involved in a supply chain using holons. Three operational models, make-to-order, make-to-stock, and assemble-to-order are considered. A strong assumption on the original STS theory is weakened to allow events shared among agents to be located at different levels. A supervisor is synthesized for the example of a mattress supply chain with make-to-stock operation under certain specifications. Moreover, a new version of the Timed STS framework is developed to allow events to have an upper time bound i.e. deadline. With Timed STS framework, more specifications requiring time measurement can be modeled and a supervisory control is synthesized for the timed model of a supply chain. For a nonempty supervisory synthesis result, the maximum time for the inventory periodic review rate, and the minimum cycle time for customer order satisfaction are achieved.

We study the supervisory control of supply chain operation modelled by (timed) State Tree Structures (STS). We model each agent involved in a supply chain using holons. Three operational models, make-to-order, make-to-stock, and assemble-to-order are considered. A strong assumption on the original STS theory is weakened to allow events shared among agents to be located at different levels. A supervisor is synthesized for the example of a mattress supply chain with make-to-stock operation under certain specifications. Moreover, a new version of the Timed STS framework is developed to allow events to have an upper time bound i.e. deadline. With Timed STS framework, more specifications requiring time measurement can be modeled and a supervisory control is synthesized for the timed model of a supply chain. For a nonempty supervisory synthesis result, the maximum time for the inventory periodic review rate, and the minimum cycle time for customer order satisfaction are achieved.

It is well known that the optimal nonblocking supervisory control problem of timed discrete event systems is NP-hard, subject in particular to state space explosion that is exponential in the number of system components. In this thesis, we propose to manage complexity by organizing the system as a Timed State Tree Structure (TSTS). TSTS are an adaptation of STS to timed Supervisory Control Theory (SCT). Based on TSTS we present an e±cient recursive symbolic algorithm that can perform nonblocking supervisory control design for systems of state size 10^12 and higher. Failure diagnosis is the process of detecting and identifying deviations of a system from its normal behavior using the information available through sensors. A method for fault diagnosis of the TSTS model is proposed. A state based diagnoser is constructed for each timed holon of TSTS. Fault diagnosis is accomplished using the state estimates provided by the timed holon diagnosers. The diagnosers may communicate among each other in order to update their state estimates. At any given time, only a subset of the diagnosers are operational, and as a result, instead of the entire model of the system, only the models of the timed holons associated with the operational diagnosers are used. It is shown that the computational complexity of constructing and storing the transition systems required for diagnosis in the proposed approach is polynomial in the number of system components, whereas in the original monolithic approach the computational complexity is exponential.

It is well known that the optimal nonblocking supervisory control problem of timed discrete event systems is NP-hard, subject in particular to state space explosion that is exponential in the number of system components. In this thesis, we propose to manage complexity by organizing the system as a Timed State Tree Structure (TSTS). TSTS are an adaptation of STS to timed Supervisory Control Theory (SCT). Based on TSTS we present an e±cient recursive symbolic algorithm that can perform nonblocking supervisory control design for systems of state size 10^12 and higher. Failure diagnosis is the process of detecting and identifying deviations of a system from its normal behavior using the information available through sensors. A method for fault diagnosis of the TSTS model is proposed. A state based diagnoser is constructed for each timed holon of TSTS. Fault diagnosis is accomplished using the state estimates provided by the timed holon diagnosers. The diagnosers may communicate among each other in order to update their state estimates. At any given time, only a subset of the diagnosers are operational, and as a result, instead of the entire model of the system, only the models of the timed holons associated with the operational diagnosers are used. It is shown that the computational complexity of constructing and storing the transition systems required for diagnosis in the proposed approach is polynomial in the number of system components, whereas in the original monolithic approach the computational complexity is exponential.