Related Experiment Video
Updated: May 9, 2025

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits
Published on: September 27, 2019
Modeling and resilience analysis of multi-group supply chain network
Yuanyuan Liang1, Yongxiang Xia1, Yang Wang2
1School of Communication Engineering, Hangzhou Dianzi University, Hangzhou 310018, China.
Abstract:
Modern supply chains are highly complex and have a networked structure, known as supply chain networks (SCNs). Traditional models for SCNs are constructed in a multi-level structure, where each level contains nodes of the same type, such as second-tier suppliers, first-tier suppliers, manufacturers, and retailers. However, in real-world SCNs, enterprises at the same level may produce different types of products. An enterprise's function is defined by the type of the product it produces. Based on this fact, this paper proposes a new SCN model that groups nodes based on their functions, called the multi-group supply chain network (MGSCN) model. In order to study the resilience of the proposed MGSCN, we build an underload cascading failure model, which takes the recovery strategy during the failure process into account. The simulation result indicates that the ungrouped SCN underestimates the impact of cascading failures. The proposed MGSCN clearly shows that reducing the number of functional groups can effectively enhance the resilience of MGSCNs. In addition, increasing the upper bound or reducing the lower bound of node capacity can also improve the resilience of MGSCNs. The MGSCN model proposed in this paper and the research on its resilience can provide a valuable reference for the management and optimization of real-world SCNs.
Related Concept Videos
Design Example: Analyzing Capacity Contours for Flood Risk Assessment
Multicompartment Models: Overview
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
Response Surface Methodology
The process of RSM involves several key steps:
Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches
The model approach uses mathematical models to describe changes in drug concentration over time. Pharmacokinetic models help characterize drug behavior in patients, predict drug concentration in the body fluids, calculate optimum dosage regimens, and evaluate the risk of toxicity. However, ensuring that the model fits the experimental data accurately...
Mechanistic Models: Compartment Models in Individual and Population Analysis
Distributed Loads: Problem Solving

