A Bi-objective Model for the Cloud Manufacturing Configuration Design with Resilience and Disruption Risks

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21730%2F22%3A00363946" target="_blank" >RIV/68407700:21730/22:00363946 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1016/j.ifacol.2022.10.139" target="_blank" >https://doi.org/10.1016/j.ifacol.2022.10.139</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.ifacol.2022.10.139" target="_blank" >10.1016/j.ifacol.2022.10.139</a>

Result language
angličtina
Original language name
A Bi-objective Model for the Cloud Manufacturing Configuration Design with Resilience and Disruption Risks
Original language description
Given the fourth industrial revolution (i.e., Industry 4.0), a cloud manufacturing (CMfg) system is introduced as a modern service- and customer-centered manufacturing paradigm. This system incorporates the distributed manufacturing corporations to collaborate as an interconnected system and share their production capabilities or resources without any stoppages. In this regard, this paper develops a novel two-stage bi-objective mathematical model for designing resilient CMfg configuration by maximizing the platform's profit and maximizing the resilience level of the designed CMfg network. Three primary resilience indicators, including design quality, proactive capability, and reactive capability, are considered in the proposed model. Moreover, to evaluate the resilience level of the designed CMfg network, a new objective function is developed. An E-constraint augmented (AUGMECON2) method is used to solve the bi-objective model. Furthermore, a simple maximum-likelihood sampling (MLS) method is used to reduce the number of possible scenarios. Finally, some in-depth analyses are carried out to illustrate and validate the performance of the proposed solution approach Copyright (C) 2022 The Authors.
Czech name
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Czech description
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Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project
<a href="/en/project/LL1902" target="_blank" >LL1902: Powering SMT Solvers by Machine Learning</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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