scholarly journals STANDARDISING MAINTENANCE JOBS TO IMPROVE GROUPING DECISION MAKING

2021 ◽  
Vol 1 ◽  
pp. 2701-2710
Author(s):  
Julie Krogh Agergaard ◽  
Kristoffer Vandrup Sigsgaard ◽  
Niels Henrik Mortensen ◽  
Jingrui Ge ◽  
Kasper Barslund Hansen ◽  
...  
Keyword(s):  
Decision Making ◽  
Tacit Knowledge ◽  
True Variance ◽  
Complex Decision ◽  
Maintenance Decision ◽  
Maintenance Activities

AbstractMaintenance decision making is an important part of managing the costs, effectiveness and risk of maintenance. One way to improve maintenance efficiency without affecting the risk picture is to group maintenance jobs. Literature includes many examples of algorithms for the grouping of maintenance activities. However, the data is not always available, and with increasing plant complexity comes increasingly complex decision requirements, making it difficult to leave the decision making up to algorithms.This paper suggests a framework for the standardisation of maintenance data as an aid for maintenance experts to make decisions on maintenance grouping. The standardisation improves the basis for decisions, giving an overview of true variance within the available data. The goal of the framework is to make it simpler to apply tacit knowledge and make right decisions.Applying the framework in a case study showed that groups can be identified and reconfigured and potential savings easily estimated when maintenance jobs are standardised. The case study enabled an estimated 7%-9% saved on the number of hours spent on the investigated jobs.

scholarly journals Coupling Fuzzy Multi-Criteria Decision-Making and Clustering Algorithm for MSW Landfill Site Selection (Case Study: Lanzhou, China)

2021 ◽  
Vol 10 (6) ◽  
pp. 403
Author(s):  
Jiamin Liu ◽  
Yueshi Li ◽  
Bin Xiao ◽  
Jizong Jiao
Keyword(s):  
Decision Making ◽  
Clustering Algorithm ◽  
Fuzzy Membership Function ◽  
Multi Criteria Decision Making ◽  
Landfill Site Selection ◽  
Spatial Distribution Characteristics ◽  
Criteria Weights ◽  
Spatial Decision Making ◽  
Complex Decision

The siting of Municipal Solid Waste (MSW) landfills is a complex decision process. Existing siting methods utilize expert scores to determine criteria weights, however, they ignore the uncertainty of data and criterion weights and the efficacy of results. In this study, a coupled fuzzy Multi-Criteria Decision-Making (MCDM) approach was employed to site landfills in Lanzhou, a semi-arid valley basin city in China, to enhance the spatial decision-making process. Primarily, 21 criteria were identified in five groups through the Delphi method at 30 m resolution, then criteria weights were obtained by DEMATEL and ANP, and the optimal fuzzy membership function was determined for each evaluation criterion. Combined with GIS spatial analysis and the clustering algorithm, candidate sites that satisfied the landfill conditions were identified, and the spatial distribution characteristics were analyzed. These sites were subsequently ranked utilizing the MOORA, WASPAS, COPRAS, and TOPSIS methods to verify the reliability of the results by conducting sensitivity analysis. This study is different from the previous research that applied the MCDM approach in that fuzzy MCDM for weighting criteria is more reliable compared to the other common methods.

Drifting the goals archetype: a systemic study

Kybernetes ◽  
2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Virupaxi Bagodi ◽  
Biswajit Mahanty
Keyword(s):  
Decision Making ◽  
Systems Thinking ◽  
Decision Makers ◽  
Content Type ◽  
Area Of Interest ◽  
Complex Decision Making ◽  
Complex Decision ◽  
Current Reality

PurposeManagerial decision-making is an area of interest to both academia and practitioners. Researchers found that managers often fail to manage complex decision-making tasks and system thinkers assert that generic structures known as systems archetypes help them to a great deal in handling such situations. In this paper, it is demonstrated that decision makers resort to lowering of goal (quick-fix) in order to resolve the gap between the goal and current reality in the “drifting the goals” systems archetype.Design/methodology/approachA real-life case study is taken up to highlight the pitfalls of “drifting the goals” systems archetype for a decision situation in the Indian two-wheeler industry. System dynamics modeling is made use of to obtain the results.FindingsThe decision makers fail to realize the pitfall of lowering the goal to resolve the gap between the goal and current reality. It is seen that, irrespective of current less-than-desirable performance, managers adopting corrective actions other than lowering of goals perform better in the long run. Further, it is demonstrated that extending the boundary and experimentation results in designing a better service system and setting benchmarks.Practical implicationsThe best possible way to avoid the pitfall is to hold the vision and not lower the long term goal. The managers must be aware of the pitfalls beforehand.Originality/valueSystems thinking is important in complex decision-making tasks. Managers need to embrace long-term perspective in decision-making. This paper demonstrates the value of systems thinking in terms of a case study on the “drifting the goals” systems archetype.

scholarly journals Decision-making on Implementation of IPO Under Topological Uncertainty

2015 ◽  
Vol 63 (1) ◽  
pp. 193-200 ◽  
Author(s):  
Karel Doubravský ◽  
Tomáš Meluzín ◽  
Mirko Dohnal
Keyword(s):  
Decision Making ◽  
Decision Trees ◽  
Initial Public Offering ◽  
Decision Makers ◽  
Structure Topology ◽  
Complex Decision Making ◽  
Flexible Integration ◽  
Complex Decision ◽  
Public Offering

IPO (Initial Public Offering) is a complex decision making task which is always associated with different types of uncertainty. Poor accuracies of available probabilities of lotteries e.g. quantification of investor interest is studied in the first part of this paper (Meluzín, Doubravský, Dohnal, 2012). However, IPO is often prohibitively ill-known. This paper takes into consideration the fact that decision makers cannot specify the structure/topology of the relevant decision tree. It means that one IPO task is specified by several (partially) different decision trees which comes from different sources e.g. from different teams of decision makers/experts. A flexible integration of those trees is based on fuzzy logic using the reconciliation (Meluzín, Doubravský, Dohnal, 2012). The developed algorithm is demonstrated by a case study which is presented in details. The IPO case integrates two partially different decision trees.

PLANNING A PRODUCT FOR TOURING DESTINATIONS THROUGH THE USE OF SPATIAL MATHEMATICAL ANALYSIS

2006 ◽  
Vol 12 (2) ◽  
pp. 71-82
Author(s):  
Chris Vassiliadis ◽  
John Mylonakis
Keyword(s):  
Decision Making ◽  
Spatial Analysis ◽  
Mathematical Models ◽  
Mathematical Analysis ◽  
Nearest Neighbor ◽  
Northern Greece ◽  
Railway Stations ◽  
Complex Decision Making ◽  
Complex Decision

Mathematical models provide spatial analysis to help complex decision-making and can be successfully applied to product planning in tourism. This paper presents a case study, and suggests one process by which planning agencies may evaluate the railway stations in the Northern Greece network. Six geographical points of distinction are identified for promotion based on linear-nearest neighbor analysis and the connectivity index. A functional diagram evaluates each point based on infrastructure, natural and cultural attractions. Finally, these indicators suggest marketing considerations, which may lend support to Management or stakeholders’ discussions to maximize the geographic points.

Key Techniques in Intelligent Predictive Maintenance (IPdM) – A Framework of Intelligent Faults Diagnosis and Prognosis System (IFDaPS)

2014 ◽  
Vol 1039 ◽  
pp. 490-505 ◽  
Author(s):  
Ke Sheng Wang
Keyword(s):  
Decision Making ◽  
Frequency Domain ◽  
Wavelet Packet ◽  
Remaining Useful Life ◽  
Predictive Maintenance ◽  
Ann Model ◽  
Diagnosis And Prognosis ◽  
Faults Diagnosis ◽  
Maintenance Decision

Intelligent predictive maintenance (IPdM) is a maintenance strategy that makes maintenance decisions automatically and dynamically based on Artificial Intelligence and Data mining techniques through condition monitoring of machines, equipment and production processes. IPdM system consists of the following main modules: sensor and data acquisition, signal and data processing, feature extractions, maintenance decision-making, key performance indicators, maintenance scheduling optimization and feedback control and compensation. Among them, the most important part of IPdM is maintenance decision-making, which includes diagnostics and prognostics. This paper proposes a framework of intelligent faults diagnosis and prognosis system (IFDaPS) and discuss some key technologies for implement IPdM policy in manufacturing and industries. A case study focus on the vibration signals collected from the sensors mounted on a pressure blower for critical components monitoring. We decompose the pre-processed signals into several signals using Wavelet Packet Decomposition (WPD), and then the signals are transformed to frequency domain using Fast Fourier Transform (FFT). The features extracted from frequency domain are used to train Artificial Neural Network (ANN). Trained ANN model is able to identify the fault of the components and predict its Remaining Useful Life (RUL). The case study demonstrates how to implement the proposed framework and intelligent technologies for IPdM and the result indicates its higher efficiency and effectiveness comparing to traditional methods.

scholarly journals Decision-Making Tool for Enhancing the Sustainable Management of Cultural Institutions: Season Content Programming at Palau De La Música Catalana

2020 ◽  
Vol 12 (14) ◽  
pp. 5785
Author(s):  
Maria del Mar Casanovas-Rubio ◽  
Carolina Christen ◽  
Luz María Valarezo ◽  
Jaume Bofill ◽  
Nela Filimon ◽  
...  
Keyword(s):  
Decision Making ◽  
Sustainable Management ◽  
Qualitative Interviews ◽  
Real Data ◽  
Cultural Institutions ◽  
Cultural Institution ◽  
Proposed Model ◽  
Complex Decision ◽  
Cultural Sector

There has been an increasing relevance of the cultural sector in the economic and social development of different countries. However, this sector continues without much input from multi-criteria decision-making (MDCM) techniques and sustainability analysis, which are widely used in other sectors. This paper proposes an MCDM model to assess the sustainability of a musical institution’s program. To define the parameters of the proposed model, qualitative interviews with relevant representatives of Catalan cultural institutions and highly recognized professionals in the sector were performed. The content of the 2015–2016 season of the ‘Palau de la Música Catalana’, a relevant Catalan musical institution located in Barcelona, was used as a case study to empirically test the method. The method allows the calculation of a season value index (SVI), which serves to make more sustainable decisions on musical season programs according to the established criteria. The sensitivity analysis carried out for different scenarios shows the robustness of the method. The research suggests that more complex decision settings, such as MCDM methods that are widely used in other sectors, can be easily applied to the sustainable management of any type of cultural institution. To the best of the authors’ knowledge, this method was never applied to a cultural institution and with real data.

scholarly journals The Adoption of Prognostic Technologies in Maintenance Decision Making: A Multiple Case Study

Procedia CIRP ◽  
2015 ◽  
Vol 38 ◽  
pp. 171-176 ◽  
Author(s):  
W.W. Tiddens ◽  
A.J.J. Braaksma ◽  
T. Tinga
Keyword(s):  
Decision Making ◽  
Multiple Case Study ◽  
Multiple Case ◽  
Maintenance Decision

scholarly journals MULTI-CRITERIA MODEL BASED ON LINGUISTIC NEUTROSOPHIC NUMBERS: THE SELECTION OF UNMANNED AIRCRAFT

2020 ◽  
Vol 14 (1) ◽  
Author(s):  
Goran Ćirović ◽  
Dragan Pamučar ◽  
Nataša Popović-Miletić
Keyword(s):  
Decision Making ◽  
Forest Fires ◽  
Unmanned Aircraft ◽  
Decision Makers ◽  
New Approach ◽  
Complex Decision Making ◽  
Complex Decision ◽  
Linguistic Neutrosophic Numbers ◽  
Selection Of

The paper presents a new approach in treating uncertainty and subjectivity in the decision making process based on the modification of Multi Attributive Border Approximation area Comparison (MABAC) and an Objective-Subjective (OS) model by applying linguistic neutrosophic numbers (LNN) instead of traditional numerical values. By integrating these models with linguistic neutrosophic numbers it was shown that it is possible to a significant extent to eliminate subjective qualitative assessments and assumptions by decision makers in complex decision-making conditions. On this basis, a new hybrid LNN OS-MABAC model was formed. This model was tested and validated on a case-study of the selection of optimal unmanned aircraft intended to combat forest fires.

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