Knowledge-based disturbance propagation in manufacturing systems: A case study

Bayar, Nawel; Hajri-Gabouj, Sonia; Darmoul, Saber

doi:10.1109/aset.2018.8379900

Cited by 2 publications

(1 citation statement)

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“…At some point, a disturbance occurs, in which Module_02 fails, affecting the performance of the configuration and its ability to meet the orders on time. The consequences of the disturbance can be determined using approaches, such as described in [42]. Now, a reconfiguration decision has to be made.…”

Section: Disturbance Occurrencementioning

confidence: 99%

A Multi-Criteria Decision Framework Considering Different Levels of Decision-Maker Involvement to Reconfigure Manufacturing Systems

et al. 2020

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Reconfigurable Manufacturing Systems (RMSs) rely on a set of technologies to quickly adapt the manufacturing system capacity and/or functionality to meet unexpected disturbances, such as fluctuation/uncertainty of demand and/or unavailability/unreliability of resources. At the operational stage, such disturbances raise new production requirements and risks, which call upon Decision-Makers (DMs) to analyze the opportunity to move from a running configuration to another more competitive one. Such a decision is generally based on an evaluation of a multitude of criteria, and several multi-criteria decision-making (MCDM) approaches have been suggested to help DMs with the reconfiguration process. Most existing MCDM approaches require some assignment of weights to the criteria, which is not a trivial task. Unfortunately, existing studies on MCDM for an RMS have not provided guidelines to weigh the evaluation criteria. This article fills in this gap by offering a framework to set up such weights. We provide a comprehensive set of quantitative indicators to evaluate the reconfiguration decisions during the operation of the RMS. We suggest three weighting methods that are convenient to different levels of DM expertise and desired degree of involvement in the reconfiguration process. These weighting methods are based on (1) intuitive weighting, (2) revised Simos procedural weighting combined with the Technique for Order of Preferences by Similarity to Ideal Solution (TOPSIS), and (3) DM independent weighting using ELECTRE IV. The implementation of the suggested framework and a comparison of the suggested methods carried out on an industrial case study are described herein.

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Section: Disturbance Occurrencementioning

confidence: 99%