A Systematic Disturbance Analysis Method for Resilience Evaluation: A Case Study in Material Handling Systems

Abstract

With the development of intelligent manufacturing technology, the material handling system (MHS) faces larger resilience challenges that threaten the sustainability of the system. To evaluate system resilience, the disturbance that the system may experience and the system response need to be identified in advance. This paper proposes a systematic and innovative approach to performing resilience-related disturbance analysis, i.e., disturbance mode and effects analysis (DMEA). Using this method, the possible disturbance modes, their occurrence probabilities, and the quantitative effects on system performance can be collected in a bottom-up process, and the information can be applied to further resilience quantification. Moreover, a quantitative system resilience evaluation framework for the MHS based on DMEA and the Monte Carlo method is presented. Production is defined as the key performance index of the system and is monitored to reflect the resilience behavior of the system after the disturbance occurs. The resilience of a tire tread handing system is quantified in our case study, and the results show the effectiveness of our DMEA-based resilience evaluation method. We also find that a reasonable system configuration and maintenance strategy can effectively improve system resilience, and a trade-off can be made between resilience and cost.

System resilience distribution identification and analysis based on performance processes after disruptions

“…In the Monte Carlo simulation, it is assumed that the simulation error is required to be no more than ± ε at a confidence level of 1 − α . According to the central limit theorem, the number of simulations n should satisfy the following equation (see [ 25 , 27 ]): …”

“…Obtaining the resilience samples: n resilience values are calculated using the resilience measure in Eq 4 (see [ 27 ]), i.e., where Q ( t ) and Q 0 ( t ) represent the system performance at time t with and without disruption, respectively, t 0 is the disruption occurrence time, and T a is the maximum allowable recovery time. This measurement reflects the system’s average performance after a disruption within a certain time period.…”

“…3. Obtaining the resilience samples: n resilience values are calculated using the resilience measure in Eq 4 (see [27]), i.e.,…”

Detecting and Processing Anomalies in a Factory of the Future

“…The mitigation method of global reconfiguration provides a backup plan for dealing with anomalies if more specialized mitigation methods such as local reconfiguration do not provide sufficient mitigation solutions. This improves the resilience of a fleet of AGVs and helps to address the research gaps for resilient, sustainable material handling stressed in [61]. To the best of the authors' knowledge, this is the first study to address the subject of adversarial attacks in Industry 4.0.…”

Evaluation of Community Resilience in Rural China—Taking Licheng Subdistrict, Guangzhou as an Example

“…Research on resilience theory has moved on from discussions of concept connotation and construction of a theoretical framework to the establishment of a resilience evaluation system [38]. However, despite there being many frameworks for evaluating community resilience using qualitative analysis, currently, only a few frameworks use a quantitative approach in their research.…”