A system dynamics simulation model of chemical supply chain transportation risk management systems

Li, Chaoyu; Ren, Jun; Wang, Haiyan

doi:10.1016/j.compchemeng.2016.02.019

Cited by 54 publications

(34 citation statements)

References 51 publications

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“…Most of the existing methodologies lack inbuilt and practical techniques that take into consideration the complex interactions and dynamic feedback effects. A novel modelling and simulation method was introduced to address the dynamic risks effects [33][34][35][36][37][38].…”

Section: Literature Reviewmentioning

confidence: 99%

Dynamics Simulation for Process Risk Evolution on the Bunker Operation of an LNG-fueled Vessel with Catastrophe Mathematical Models

Xuan

et al. 2019

JMSE

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Liquefied nature gas (LNG) is a green energy. LNG-fueled vessels are extremely complex engineering systems. In view of the inherent hazardous properties of LNG fuel, LNG fueling is not only an important part, but it is also full of high risks in the operation of LNG-fueled vessels (LNGFVs). Therefore, it is necessary to study the risk factors, and the intrinsic relationship among them between the LNG and the vessel, and to simulate the system dynamics in the process of LNGFV operation. During the process of fueling of LNGFV, at every moment the vessel interacts with the energy and information of the surrounding environment. First, the impact of the three interactions of the fueling operation process, ship factors, and environmental factors were analyzed on the risk of fueling operation, and a complete node system was proposed as to the complex system dynamics mode. Second, by analyzing the boundary conditions of the system, the relationship of factors was established via the tools of system dynamics (SD). Based on the catastrophe theory (CA), the dynamics model for the fueling of LNG is set up to study the system’s risk mutation phenomenon. Third, combined with the simulation results of the case analysis, the risk evolution mode of the LNGFV during the fueling process was obtained, and constructive opinions were put forward for improving the safe fueling of the LNGFV. Application examples show that formal description of risk emergence and transition is a prerequisite for the quantitative analysis of the risk evolution mode. In order to prevent accidents, the coupling synchronization of risk emergence should be weakened, and meanwhile risk control should be implemented.

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Section: Literature Reviewmentioning

confidence: 99%

Dynamics Simulation for Process Risk Evolution on the Bunker Operation of an LNG-fueled Vessel with Catastrophe Mathematical Models

Xuan

et al. 2019

JMSE

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show abstract

“…Simulation and optimization models can be combined through some iterative procedures to achieve the best values for risk reduction by selecting a combination of mitigation strategies [2]. lt is significant that most of the existing methodologies of risk management in production logistics lack inbuilt and practical techniques that take into consideration the complex interactions and dynamic feedback properties, which can meaningfully affect the reliability of risk management results (25].…”

Section: Related Workmentioning

confidence: 99%

Risk information formalisation with graphs

Kamsu-Foguem

Tiako

2017

Computers in Industry

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“…The researchers used only a questionnaire as the input for various risk scenarios, which could be a source of bias. Thus, it would be necessary to use a more extensive data source [8].…”

Section: State Of the Artmentioning

confidence: 99%

Analyzing the Impact of Different Order Policies on the Supply Chain Performance

Stich¹,

Pause²,

Blum³

2017

Advances in Production Management Systems. The Path to Intelligent, Collaborative and Sustainable Manufacturing

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Influenced by the high dynamic of the markets and the steadily increasing demand for short delivery times the importance of supply chain optimization is growing. In particular, the order process plays a central role in achieving short delivery times and constantly needs to evaluate the trade-off between high inventory and the risk of stock-outs. However, analyzing different order strategies and the influence of various production parameters is difficult to achieve in industrial practice. Therefore, simulations of supply chains are used in order to improve processes in the whole value chain. The objective of this research is to evaluate two different order strategies (t,q, t,S) in a four-stage supply chain. In order to measure the performance of the supply chain the quantity of the backlog will be considered. A Design of Experiments approach is supposed to enhance the significance of the simulation results.

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A system dynamics simulation model of chemical supply chain transportation risk management systems

Cited by 54 publications

References 51 publications

Dynamics Simulation for Process Risk Evolution on the Bunker Operation of an LNG-fueled Vessel with Catastrophe Mathematical Models

Dynamics Simulation for Process Risk Evolution on the Bunker Operation of an LNG-fueled Vessel with Catastrophe Mathematical Models

Risk information formalisation with graphs

Analyzing the Impact of Different Order Policies on the Supply Chain Performance

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