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As a practical popular methodology for dealing with fuzziness and uncertainty in Multiple Criteria Decision-Making (MCDM), Fuzzy AHP (FAHP) has been applied to a wide range of applications. As of the time of writing there is no state of the art survey of FAHP, we carry out a literature review of 190 application papers (i.e., applied research papers), published between 2004 and 2016, by classifying them on the basis of the area of application, the identified theme, the year of publication, and so forth. The identified themes and application areas have been chosen based upon the latest state-of-the-art survey of AHP conducted by [Vaidya, O., & Kumar, S. (2006). Analytic hierarchy process: An overview of applications. European Journal of operational research, 169(1), 1–29.]. To help readers extract quick and meaningful information, the reviewed papers are summarized in various tabular formats and charts. Unlike previous literature surveys, results and findings are made available through an online (and free) testbed, which can serve as a ready reference for those who wish to apply, modify or extend FAHP in various applications areas. This online testbed makes also available one or more fuzzy pairwise comparison matrices (FPCMs) from all the reviewed papers (255 matrices in total). In terms of results and findings, this survey shows that: (i) FAHP is used primarily in the Manufacturing, Industry and Government sectors; (ii) Asia is the torchbearer in this field, where FAHP is mostly applied in the theme areas of Selection and Evaluation; (iii) a significant amount of research papers (43% of the reviewed literature) combine FAHP with other tools, particularly with TOPSIS, QFD and ANP (AHP’s variant); (iv) Chang’s extent analysis method, which is used for FPCMs’ weight derivation in FAHP, is still the most popular method in spite of a number of criticisms in recent years (considered in 57% of the reviewed literature). [ABSTRACT FROM AUTHOR]

Abstract: The switched Ethernet networks are more and more deployed in industry. The Spanning Tree Protocol implemented in the switches enables management of the link connectivity. But the reconfiguration time of the Spanning Tree Protocol (STP) when link failure occurs is not adapted to satisfy industrial constraints. The objective of this paper is to propose a method based only on standard, mitigating the probability of disconnection between nodes having hard real-time properties. The approach developed in this paper consists of duplicating frames and of forwarding them on different paths. These paths are optimized and specified by using genetic algorithms. OPNET simulations show the interest of this proposal on a particular Networked Control System. [Copyright &y& Elsevier]

Maintenance planning and scheduling are an essential part of manufacturing companies to prevent machine breakdowns and increase machine uptime, along with production efficiency. One of the biggest challenges is to effectively address uncertainty (e.g., unexpected machine failures, variable time to repair). Multiple approaches have been used to solve the maintenance scheduling problem, including dispatching rules (DR), metaheuristics and simheuristics, or most recently reinforcement learning (RL). However, to the best of our knowledge, no study has ever studied to what extent these techniques are effective when faced with different levels of uncertainty. To overcome this gap in research, this paper presents an approach by analyzing the impact of categorized levels of uncertainty, specifically high and low, on the failure distribution and time to repair. Upon the formalization of the maintenance scheduling problem, the experiments conducted are performed in simulated scenarios with different degrees of uncertainty, and also considering a real-life manufacturing use case. The results indicate that rescheduling based on a genetic algorithm (GA) simheuristic outperforms RL and DR in terms of total machine uptime, but not in terms of the mean time to repair when configured with high re-optimization frequencies (i.e., hourly re-optimization), but rapidly underperforms when the re-optimization frequency decreases. Furthermore, our study demonstrates that GA-simheuristic is highly computationally demanding compared to RL and rule-based policies. • Problem of dynamic maintenance scheduling under uncertainty is assessed. • Reinforcement Learning, Genetic Algorithms and Dispatching rules were compared. • Exploration of uncertainty factors such as time to repair and failure distribution. • Artificial and real-based scenarios were evaluated. [ABSTRACT FROM AUTHOR]

Today’s largest and fastest growing companies’ assets are no longer physical, but rather digital (software, algorithms...). This is all the more true in the manufacturing, and particularly in the maintenance sector where quality of enterprise maintenance services are closely linked to the quality of maintenance data reporting procedures. If quality of the reported data is too low, it can results in wrong decision-making and loss of money. Furthermore, various maintenance experts are involved and directly concerned about the quality of enterprises’ daily maintenance data reporting (e.g., maintenance planners, plant managers...), each one having specific needs and responsibilities. To address this Multi-Criteria Decision Making (MCDM) problem, and since data quality is hardly considered in existing expert maintenance systems, this paper develops a maintenance reporting quality assessment (MRQA) dashboard that enables any company stakeholder to easily – and in real-time – assess/rank company branch offices in terms of maintenance reporting quality. From a theoretical standpoint, AHP is used to integrate various data quality dimensions as well as expert preferences. A use case describes how the proposed MRQA dashboard is being used by a Finnish multinational equipment manufacturer to assess and enhance reporting practices in a specific or a group of branch offices. [ABSTRACT FROM AUTHOR]