Robust and resilient joint periodic maintenance planning and scheduling in a multi-factory network under uncertainty: A case study

2022 ◽  
Vol 217 ◽  
pp. 108113
Author(s):  
Hamed Jafar-Zanjani ◽  
Mostafa Zandieh ◽  
Mani Sharifi
Keyword(s):  
Maintenance Planning ◽  
Planning And Scheduling ◽  
Periodic Maintenance

scholarly journals Predicting maintenance work hours in maintenance planning

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Waqas Khalid ◽  
Simon Holst Albrechtsen ◽  
Kristoffer Vandrup Sigsgaard ◽  
Niels Henrik Mortensen ◽  
Kasper Barslund Hansen ◽  
...  
Keyword(s):  
Industrial Applications ◽  
Research Literature ◽  
Work Hours ◽  
Machine Learning Algorithms ◽  
Maintenance Planning ◽  
Planning And Scheduling ◽  
Content Type ◽  
Work Hour ◽  
Maintenance Work

PurposeCurrent industry practices illustrate there is no standard method to estimate the number of hours worked on maintenance activities; instead, industry experts use experience to guess maintenance work hours. There is also a gap in the research literature on maintenance work hour estimation. This paper investigates the use of machine-learning algorithms to predict maintenance work hours and proposes a method that utilizes historical preventive maintenance order data to predict maintenance work hours.Design/methodology/approachThe paper uses the design research methodology utilizing a case study to validate the proposed method.FindingsThe case study analysis confirms that the proposed method is applicable and has the potential to significantly improve work hour prediction accuracy, especially for medium- and long-term work orders. Moreover, the study finds that this method is more accurate and more efficient than conducting estimations based on experience.Practical implicationsThe study has major implications for industrial applications. Maintenance-intensive industries such as oil and gas and chemical industries spend a huge portion of their operational expenditures (OPEX) on maintenance. This research will enable them to accurately predict work hour requirements that will help them to avoid unwanted downtime and costs and improve production planning and scheduling.Originality/valueThe proposed method provides new insights into maintenance theory and possesses a huge potential to improve the current maintenance planning practices in the industry.

Scheduling Maintenance Technicians in a Railway Depot

2021 ◽  
Author(s):  
Tiago Alves ◽  
António R. Andrade
Keyword(s):  
Programming Model ◽  
Crew Scheduling ◽  
Mixed Integer ◽  
Rolling Stock ◽  
Maintenance Planning ◽  
Labor Costs ◽  
Planning And Scheduling ◽  
Scheduling Model ◽  
Maintenance Technicians

Abstract This paper presents a mathematical programming model that optimizes the daily schedule of maintenance technicians in a railway depot. The aim of the model is the minimization of the associated labor costs, while assigning the different technicians and skills required for each maintenance task. A case study of a Portuguese train operating company is explored, including many technical constraints imposed by the company. A mixed-integer linear programming model is formulated and applied to the case study, while observing the rolling stock schedule and the maintenance tactical plan. The optimized solution shows that the maintenance team could be shortened, as some workers are not necessary to carry out all maintenance actions, suggesting the need for more flexible maintenance crew scheduling and associated labor conditions. The present model is integrated within a tactical maintenance planning model, which finds a feasible annual maintenance plan for the entire fleet, and an operational maintenance scheduling model, which assigns train units to service tasks and schedules the maintenance tasks within the rolling stock. Together, the three models provide a decision framework that can support maintenance planning and scheduling decisions. Finally, the present maintenance crew scheduling model adds a key aspect to the literature: the skills of maintenance technicians.

scholarly journals Integration of maintenance scheduling and planning for large-scale asset fleets

2021 ◽  
Author(s):  
Frederik Schulze Spüntrup ◽  
Giancarlo Dalle Ave ◽  
Lars Imsland ◽  
Iiro Harjunkoski
Keyword(s):  
Large Scale ◽  
Programming Model ◽  
Maintenance Scheduling ◽  
Maintenance Planning ◽  
Short Term ◽  
Planning And Scheduling ◽  
Time Linear ◽  
Time Grid

AbstractLarge fleets of engineering assets that are subject to ongoing degradation are posing the challenge of how and when to perform maintenance. For a given case study, this paper proposes a formulation for combined scheduling and planning of maintenance actions. A hierarchical approach and a two-stage approach (with either uniform or non-uniform time grid) are considered and compared to each other. The resulting discrete-time linear programming model follows the Resource Task Network framework. Asset deterioration is considered linearly and tackled with an enumerator-based formulation. Advantages of the model are its computational efficiency, scalability, extendability and adaptability. The results indicate that combined maintenance planning and scheduling can be solved in appropriate time and with appropriate accuracy. The decision-support that is delivered helps the choice of the specific maintenance action to perform and proposes when to conduct it. The paper makes a case for the benefits of optimally combining long-term planning and short-term scheduling in industrial-sized problems into one system.

scholarly journals A Taxonomy of Railway Track Maintenance Planning and Scheduling: A Review and Research Trends

2021 ◽  
pp. 107827
Author(s):  
Mahdieh Sedghi ◽  
Osmo Kauppila ◽  
Bjarne Bergquist ◽  
Erik Vanhatalo ◽  
Murat Kulahci
Keyword(s):  
Research Trends ◽  
Railway Track ◽  
Maintenance Planning ◽  
Planning And Scheduling ◽  
Track Maintenance

Design and Validation of a Simulation-Based Modular Planning and Scheduling System of Semiconductor Fabrication Facilities

2011 ◽  
Vol 48-49 ◽  
pp. 378-381
Author(s):  
Li Li ◽  
Fei Qiao
Keyword(s):  
Simulation Modeling ◽  
General Structure ◽  
Structure Model ◽  
Planning And Scheduling ◽  
Scheduling System ◽  
Information Layer ◽  
Simulation Based ◽  
Semiconductor Fabrication ◽  
Control Modules

A simulation-based modular planning and scheduling system developed for semiconductor fabrication facilities (SFFs) is discussed. Firstly, the general structure model (GSM) for SFFs, composed of a configurable definition layer, a physical layer, a process information layer and a planning and scheduling layer, is proposed. Secondly, a data-based dynamic simulation modeling method is given. Thirdly, a simulation-based modular planning and scheduling system (SMPSS) for SFFs, including model modules, release control modules, scheduling modules and rescheduling modules, is designed and developed. Finally, a case study is used to demonstrate the effectiveness of

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