Building an effective resilient supply chain system (RSCS) is critical and necessary to reduce the risk of supply chain disruptions in unexpected scenarios such as COVID-19 pandemic and trade wars. To overcome the imp...Building an effective resilient supply chain system (RSCS) is critical and necessary to reduce the risk of supply chain disruptions in unexpected scenarios such as COVID-19 pandemic and trade wars. To overcome the impact of insufficient raw material supply on the supply chain in mass disruption scenarios, this study proposes a novel RSCS considering product design changes (PDC). An RSCS domain model is first developed from the perspective of PDC based on a general conceptual framework, i.e., function-context-behavior-principle-state-structure (FCBPSS), which can portray complex systems under unpredictable situations. Specifically, the interaction among the structure, state and behavior of the infrastructure system and substance system is captured, and then a quantitative analysis of the change impact process is presented to evaluate the resilience of both the product and supply chain. Next, a case study is conducted to demonstrate the PDC strategy and to validate the feasibility and effectiveness of the RSCS domain model. The results show that the restructured RSCS based on the proposed strategy and model can remedy the huge losses caused by the unavailability of raw materials.展开更多
Qualitative reasoning uses incomplete knowledge to compute a description of the possible behaviors for dynamic systems. A standard qualitative simulation（QSIM） algorithm frequently results in a large number of incom...Qualitative reasoning uses incomplete knowledge to compute a description of the possible behaviors for dynamic systems. A standard qualitative simulation（QSIM） algorithm frequently results in a large number of incomprehensible behavioral descriptions and the simulation for complex systems frequently is intractable. Two model de- composition methods are proposed in this paper to eliminate or decrease the insujficiency of this algorithm. Using a directed graph to represent the qualitative model, the strongly connected graph based theory and genetic algorithm based model decomposition are proposed to decompose the model. A new simple system model is reconstructed by subgraphs and causal relations when the system directed graph is decomposed completely. Each sub-graph is viewed as a separate system and will be simulated separately, and the simulation result of causally upstream subsystem is used to constrain the behavior of downstream subsystems. The model decomposition algorithm provides a promising paradigm for qualitative simulation whose complexity is driven by the complexity of the problem specification rather than the inference mechanism used.展开更多
基金supported in part by the National Key Research and Development Program of China(Grant No.2020YFB1708200)the National Natural Science Foundation of China(Grant Nos.62173076and 71971027).
文摘Building an effective resilient supply chain system (RSCS) is critical and necessary to reduce the risk of supply chain disruptions in unexpected scenarios such as COVID-19 pandemic and trade wars. To overcome the impact of insufficient raw material supply on the supply chain in mass disruption scenarios, this study proposes a novel RSCS considering product design changes (PDC). An RSCS domain model is first developed from the perspective of PDC based on a general conceptual framework, i.e., function-context-behavior-principle-state-structure (FCBPSS), which can portray complex systems under unpredictable situations. Specifically, the interaction among the structure, state and behavior of the infrastructure system and substance system is captured, and then a quantitative analysis of the change impact process is presented to evaluate the resilience of both the product and supply chain. Next, a case study is conducted to demonstrate the PDC strategy and to validate the feasibility and effectiveness of the RSCS domain model. The results show that the restructured RSCS based on the proposed strategy and model can remedy the huge losses caused by the unavailability of raw materials.
文摘Qualitative reasoning uses incomplete knowledge to compute a description of the possible behaviors for dynamic systems. A standard qualitative simulation（QSIM） algorithm frequently results in a large number of incomprehensible behavioral descriptions and the simulation for complex systems frequently is intractable. Two model de- composition methods are proposed in this paper to eliminate or decrease the insujficiency of this algorithm. Using a directed graph to represent the qualitative model, the strongly connected graph based theory and genetic algorithm based model decomposition are proposed to decompose the model. A new simple system model is reconstructed by subgraphs and causal relations when the system directed graph is decomposed completely. Each sub-graph is viewed as a separate system and will be simulated separately, and the simulation result of causally upstream subsystem is used to constrain the behavior of downstream subsystems. The model decomposition algorithm provides a promising paradigm for qualitative simulation whose complexity is driven by the complexity of the problem specification rather than the inference mechanism used.
