Measurement of resilience in job shop system
Resilience in relation to the maintenance and management of job shop systems has not yet received significant consideration or adequate study. Yet resilience is increasing in usage within engineering fields, although its application varies from one system to another. The matrices for resilience depend on the structure of the system and the failure factors. Machine stability is significant to the industry because of the need to build quality and to minimize production loss resulting from machine breakdown. A disruptive event on the machine leads to full loss of production in the job shop. In order to mitigate the impact of machine break down, it is essential to pinpoint a new resilience definition, measurements, and a new model design and evaluate the resilience using analysis tools. In a job shop, machines are considered most important, and they are always the most susceptible to disruptions during different kinds of operations. A disruptive event in a machine causes errors in the machine workload, operations dynamic, and the job shop system. This dissertation proposes a new definition for resilience in a job shop and analysis frameworks. The report includes the resilience curve, modeling, estimations, quantification, and measurement techniques.