Abstract
Disruptive events in complex manufacturing systems (CMS), characterised by labour-intensive processes and repetitive activities, render these systems vulnerable. In order to tackle this challenge, an approach for resilience-based system design optimisation is proposed. The approach: (i) introduces a dynamic multi-dimensional resilience metric; and (ii) formulates the resilience as a multi-objective optimisation problem to improve CMSs resilience by finding an optimal human resource allocation model, considering design factors including redundancy, resources capacity and roles. The case study, selected to test the validity of the presented approach, show improvement in resilience and efficiency, in terms of throughput, resources utilisation and restoration time.
Highlights
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Some of the threads may internally occur in CMSs are unexpected/late deliveries; unexpected orders; quality problems; and inventory-related issues
The design factors considered for the MOO problem formulation are the amount of resources, and inventory and storage capacities
Summary
Several research studies examine resilience across a wide spectrum of systems in the literature. Within the context of CMSs, optimisation of resilience by changing design factors can expedite the systems’ performance recovery and mitigate unwanted impacts [7]. Recovery activities such as resource allocation are employed to minimise the performance loss and recovery time. In order to tackle the aforementioned challenges and gain insights on how to design resilient CMSs, an approach for resilience-based system design optimisation, considering reduction in performance losses and restoration time with limited resources, is proposed. It is noted that CMSs consist of multiple subsystems able to operate simultaneously In this work, these subsystems are referred to as manufacturing phases comprising of activities. Some of the threads may internally occur in CMSs are unexpected/late deliveries; unexpected orders; quality problems; and inventory-related issues
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