Relevant bibliographies by topics / Schedule maintenance

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
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Academic literature on the topic 'Schedule maintenance'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Schedule maintenance"

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Washimkar, P. V., V. S. Deshpande, J. P. Modak, and A. V. Nasery. "Formulation of Preventive Maintenance Schedule for Dragline System." International Journal of Engineering and Technology 3, no. 4 (2011): 396–99. http://dx.doi.org/10.7763/ijet.2011.v3.259.

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Butler, A. W. "The Evolution of Locomotive and Rolling Stock Maintenance Schedules." Proceedings of the Institution of Mechanical Engineers, Part D: Transport Engineering 202, no. 1 (January 1988): 33–43. http://dx.doi.org/10.1243/pime_proc_1988_202_153_02.

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The paper introduces the maintenance schedule as an integral part of the controls for planned maintenance and reviews the historical development of locomotive maintenance schedules and specifications within British Railways (BR). The events which led to the publication of maintenance schedules for locomotives and rolling stock in a variety of formats and content are explained, culminating in the 1985 decision to produce all such future documents to one standard, taking account of simplification of use at BR depots, the new maintenance policy and the benefits of computerization. The purpose of the maintenance schedule and its supporting documents is redefined in relation to the new maintenance strategy of BR and a new rationale introduced. The standard format is confirmed and the mechanisms for ensuring its maintenance explained. Future requirements associated with development of standard job descriptions, decoupling component overhauls from body overhauls assisted by a computerized maintenance schedule and the changes in overhaul specification promoted by BR's developing contract overhaul policy are described in detail.
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Cheng, Hongbo, Yufan Cao, Jiaxin Wang, Wei Zhang, and Han Zeng. "A preventive, opportunistic maintenance strategy for the catenary system of high-speed railways based on reliability." Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit 234, no. 10 (November 4, 2019): 1149–55. http://dx.doi.org/10.1177/0954409719884215.

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The catenary is a vital component of the electrified railway system. It consists of many parts which are interrelated; the maintenance schedule of the catenary system should consider the influence of the interrelationship. In this study, a preventive, opportunistic maintenance method is proposed to schedule the maintenance process of the catenary system. First, the reliability of the key parts of the catenary is modeled using Weibull distribution. Second, a reliability margin is proposed to expand the maintenance time from point to interval, and the reliability margin is optimized to minimize the maintenance cost. Then, a preventive opportunistic maintenance schedule can be arranged on the basis of the optimal reliability margin. Case study results verify that the proposed preventive opportunistic maintenance method can reduce the number of maintenance schedules and can effectively save the maintenance cost.
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Paprocka, Iwona, Wojciech M. Kempa, Krzysztof Kalinowski, and Cezary Grabowik. "On Pareto Optimal Solution for Production and Maintenance Jobs Scheduling Problem in a Job Shop and Flow Shop with an Immune Algorithm." Advanced Materials Research 1036 (October 2014): 875–80. http://dx.doi.org/10.4028/www.scientific.net/amr.1036.875.

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In the paper a job shop and flow shop scheduling problems with availability time constraint for maintenance are considered. Unavailability time due to maintenance is estimated basing on information about predicted Mean Time To Failure/To First Failure and Mean Time of Repair of a machine. Maintenance actions are introduced into a schedule to keep the machine available in a good operation condition. The efficiency of predictive schedules (PS) is evaluated using criteria: makespan, flow time, total tardiness, idle time. The efficiency of reactive schedules (RSs) is evaluated using criteria: solution and quality robustness. For basic schedule generation Multi Objective Immune Algorithm is applied. For predictive scheduling Minimal Impact of Disturbed Operation on the Schedule is applied. After doing computer simulations for the job shop scheduling problem following question arises: do dominated Pareto optimal basic schedules achieve better PSs? Although a single Pareto-optimal solution is achieved on Pareto-optimal frontier three different schedules have the same quality in the flow shop scheduling problem. The question is: which schedule is the most robust solution?
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Lazarev, A. A., E. M. Grishin, S. A. Galakhov, and G. V. Tarasov. "Algorithms for locomotives maintenance schedule." IFAC-PapersOnLine 52, no. 13 (2019): 951–56. http://dx.doi.org/10.1016/j.ifacol.2019.11.317.

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Semaan, Nabil M., and Nabhan Yehia. "A stochastic detailed scheduling model for periodic maintenance of military rotorcraft." Aircraft Engineering and Aerospace Technology 91, no. 9 (October 7, 2019): 1195–204. http://dx.doi.org/10.1108/aeat-09-2018-0254.

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Purpose The purpose of this paper is to develop a stochastic detailed schedule for a preventive/scheduled/periodic maintenance program of a military aircraft, specifically a rotorcraft or helicopter. Design/methodology/approach The new model, entitled the military “periodic aviation maintenance stochastic schedule” (PAM-SS), develops a stochastic detailed schedule for a PUMA SA 330SM helicopter for the 50-h periodic inspection, using cyclic operation network (CYCLONE) and Monte Carlo simulation (MCS) techniques. The PAM-SS model identifies the different periodic inspection tasks of the maintenance schedule, allocates the resources required for each task, evaluates a stochastic duration of each inspection task, evaluates the probability of occurrence for each breakdown or repair, develops the CYCLONE model of the stochastic schedule and simulates the model using MCS. Findings The 50-h maintenance stochastic duration follows a normal probability distribution and has a mean value of 323 min and a standard deviation of 23.7 min. Also, the stochastic maintenance schedule lies between 299 and 306 min for a 99 per cent confidence level. Furthermore, except the pilot and the electrical team (approximately 90 per cent idle), all other teams are around 40 per cent idle. A sensitivity analysis is also performed and yielded that the PAM-SS model is not sensitive to the number of technicians in each team; however, it is highly sensitive to the probability of occurrence of the breakdowns/repairs. Practical implications The PAM-SS model is specifically developed for military rotorcrafts, to manage the different resources involved in the detailed planning and scheduling of the periodic/scheduled maintenance, mainly the 50-h inspection. It evaluates the resources utilization (idleness and queue), the stochastic maintenance duration and identifies backlogs and bottlenecks. Originality/value The PAM-SS tackles military aircraft planning and scheduling in a stochastic methodology, considering uncertainties in all inspection task durations and breakdown or repair durations. The PAM-SS, although developed for rotorcrafts can be further developed for any other type of military aircraft or any other scheduled maintenance program interval.
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Karima, Halim Qista, and Fauzan Romadlon. "Optimizing the Preventive Maintenance Scheduling Based on Dynamic Deterministic Demand in The Cement Manufacturing." Jurnal Ilmiah Teknik Industri 20, no. 1 (July 2, 2021): 109–18. http://dx.doi.org/10.23917/jiti.v20i1.13894.

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Preventive maintenance is a planned and scheduled maintenance method that is carried out before a machine failure occurs. The maintenance schedule can be determined based on experience, historical data, or recommendations. Selecting the maintenance schedule greatly affects the production system. The Clin machine in cement manufacturing has an important role in the cement production process. During treatment, the client machine cannot produce clinker, so it is necessary to plan a production system to meet the demand. This paper aims to design an optimization model for determining the preventive maintenance schedule for cement manufacturing by considering the production process and inventory control. Mathematical models with binary options are used to model that system. The model supports showing the optimal preventive maintenance schedule for the ciln machines with a binary decision each period. This mathematical model describes the interaction of production planning, inventory control, and scheduling of total maintenance on a kiln machine. The goal of this system is to determine the optimal preventive maintenance schedule with minimum costs. In addition, the system's output is the optimal production and inventory decision rule for each period. Based on the analysis and simulation of the model with the deterministic and dynamic demand, the optimal preventive maintenance schedule is in the 9th and 21st periods. The kiln machines are maintained every July with minimal costs. The model scenario shows the interaction of the variables and the sensitivity of the production capacity and demand to the decision rule of the variable.
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Mohd Tahir, Abdullah, Jamrin Jasmin, Alfero Sualin, Noor Ajian Mohd-Lair, and Abdul Kadir bin Abdul Rahman. "The Preventive Maintenance Schedule: A Case Study at Aman Sales and Services Sdn. Bhd. and Ladang Sabah Palm Oil Mill." Applied Mechanics and Materials 660 (October 2014): 995–99. http://dx.doi.org/10.4028/www.scientific.net/amm.660.995.

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Maintenance is crucial to system performance and reliability of a machine for an industry. Machine maintenance system in small and medium industries in Malaysia usually consists of manual file management system, which records all the conducted maintenance works. However, a maintenance system should be more complex to include the scheduling of maintenance activities. This paper presents the findings of studies on the implementation of preventive maintenance schedule conducted at Aman Sales And Services Sdn. Bhd. and Ladang Sabah Palm Oil Mill both located in Sabah, Malaysia. In this study, data form previous maintenance works or activities were collected from both companies. The data were then analyzed. The analysis indicates that reduction in time can be obtained by combining similar or repetitive tasks guided by recommendation from the machines’ manuals. This analysis provides foundation for developing an improved preventive maintenance schedule for each company. The new preventive maintenance schedules are found to be able to reduce the breakdown time ranging from as low as 25% to as high as 50%. This reduction signifies the efficiency of the proposed maintenance schedule.
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Alhilman, Judi, Fransiskus Tatas Dwi Atmaji, and Valinouski Aulia. "Maintenance System of Universal Goss Printing Machine based on failure data using RCM and RCS method." International Journal of Innovation in Enterprise System 2, no. 02 (July 30, 2018): 57–62. http://dx.doi.org/10.25124/ijies.v2i02.26.

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Over time a machine will get experience a decrease in reliability, causing the engine to be damaged at the time of operation, thus disrupting the production line. To maintain a machine remains reliable then a good maintenance system is required. In this research, we will use Reliability Centered Maintenance (RCM) and Reliability Centered Spare (RCS) analysis on the critical system of Goss Universal printing machine based on engine failure data. The result of RCM analysis obtained the optimal preventive maintenance schedule and the type of treatment, while based on the RCS analysis obtained spare part needs following the maintenance schedule. With the result of this analysis, is expected where the machine will keep good and will continue to operate without a sudden breakdown under the production schedule's need. Based on RCM analysis for each critical subsystem obtained interval preventive maintenance for transfer roller 127.60 hours, Ink fountain roller 24.45 hours, ink form roller 29.23 hours respectively, and the wash-up device is no scheduled maintenance. For spare parts inventory strategies the result using RCS method are: transfer roller104 units, ink fountain roller requires 32 units, ink form roller 36 units and are holding spare policy required, and a wash-up device no holding spare parts. Keywords— Failure data, Maintenance System, RCM, RCS
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Fauzi, Z. A. M., and M. A. Mansor. "Development of Dynamic Programming Algorithm for Maintenance Scheduling Problem: A Conceptual Paper." International Journal of Engineering Technology and Sciences 5, no. 1 (June 6, 2018): 34–43. http://dx.doi.org/10.15282/ijets.v5i1.2823.

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Maintenance is one of the important methods that can be used to ensure the machines and equipment can operate within the best condition. A good scheduling for maintenance will maintain the proper performance since all resources will be utilised effectively as the underused of the resource can be reduced. The schedule created ensures the particular task was done within the time provided. What, when, where and how the certain operation will be done will be stated to make sure the planned activity going smoothly without any delays. However, the uncertainty that happened during the maintenance needs to be taken into account to create the fully optimised schedule. This change will affect the schedule developed and caused delays to the scheduled task. Once the beginning task is delayed, the following work will also be postponed. Thus, a mathematical model for maintenance scheduling for repairable and inspection of the machines and equipment was proposed to deal with the uncertainty that happened. This dynamic model will create the new schedule for the maintenance scheduling and help the manager in the decision-making process on where to assign the maintenance team when the uncertainty happened.
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Dissertations / Theses on the topic "Schedule maintenance"

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PELAJO, JONAS CALDARA. "OPTIMAL WIND FARM MAINTENANCE SCHEDULE MODEL." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2018. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=33532@1.

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PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO
CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO
Os parques eólicos devem periodicamente desligar suas turbinas para realizar as manutenções agendadas. Uma vez que esta interrupção afeta a geração de energia e qualquer déficit na produção deve ser coberto por compras de energia no mercado spot, determinar o tempo ótimo para iniciar o trabalho de manutenção em um parque eólico é fundamental para maximizar sua receita, considerando que é função tanto da velocidade do vento esperada como dos preços spot da eletricidade. Neste trabalho, desenvolvemos um modelo para determinar o momento ideal para manutenção em um parque eólico. Analisamos uma janela de oportunidade no período mais provável do ano e realizamos atualizações semanais das velocidades esperadas do vento e previsões de preços de energia. As velocidades do vento são previstas com um modelo ARIMA enquanto os preços spot são simulados sob o modelo de programação estocástica dupla Newave. A decisão de adiar a manutenção para uma data futura é modelada como uma opção real americana. Testamos dois modelos com dados reais de um parque eólico no Nordeste brasileiro e comparamos nossos resultados com a prática atual e com o agendamento de manutenção considerando informações perfeitas para determinar os benefícios do modelo. Os resultados sugerem que esses modelos podem oferecer vantagens significativas em relação a uma decisão de parada que escolhe aleatoriamente uma semana para começar a manutenção dentro da janela de oportunidade e está perto da data de parada ideal, considerando o modelo de informação perfeita.
Wind farms must periodically take their turbines offline in order to perform scheduled maintenance repairs. Since this interruption impacts the generation of energy and any shortfall in production must be covered by energy purchases in the spot market, determining the optimal time to start maintenance work at a wind farm is key to maximizing your revenue, which is a function of both the expected wind speeds and electricity spot prices. In this study we develop a model to determine the optimal maintenance schedule in a wind farm. We analyze a window of opportunity in the most likely period of the year and perform weekly updates of expected wind speeds and energy price forecasts. Wind speeds are forecasted with an ARIMA model, while spot prices are simulated under the Newave dual stochastic programing model. The decision to defer maintenance to a future date is modeled as an American real option. We test two models with actual data from a wind farm in the Brazilian Northeast, and compare our results with current practice and with maintenance scheduling considering perfect information in order to determine the benefits of the model. The results suggest that the models may provide significant advantages over a stopping decision that randomly chooses a week to begin maintenance within the opportunity window and is close to the ideal optimal stopping date considering perfect model.
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Tabikh, Mohamad, and Ammar Khattab. "Scheduled maintenance policy for minimum cost : a case study." Thesis, Linnéuniversitetet, Institutionen för teknik, TEK, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-13668.

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This report evaluate the maintenance policies that been applied within specific industrial company, Taken into considerations all corrective and preventive maintenance costs ,in addition to optimise best preventive maintenance schedule for minimum cost. Dynamate Intralog AB was the surveyed company that been encountered high maintenance cost compatible with less productivity, therefore obtaining maintenance schedule policy for minimum cost was the best solution for their problem, then by calculating their corrective and preventive maintenance cost the optimum time was acquired. Finally, the maintenance schedule approve that organized maintenance based on optimum time enhance the productivity and minimize the company maintenance cost.
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Bae, Ki-Hwan. "Integrated Airline Operations: Schedule Design, Fleet Assignment, Aircraft Routing, and Crew Scheduling." Diss., Virginia Tech, 2010. http://hdl.handle.net/10919/29811.

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Air transportation offers both passenger and freight services that are essential for economic growth and development. In a highly competitive environment, airline companies have to control their operating costs by managing their flights, aircraft, and crews effectively. This motivates the extensive use of analytical techniques to solve complex problems related to airline operations planning, which includes schedule design, fleet assignment, aircraft routing, and crew scheduling. The initial problem addressed by airlines is that of schedule design, whereby a set of flights having specific origin and destination cities as well as departure and arrival times is determined. Then, a fleet assignment problem is solved to assign an aircraft type to each flight so as to maximize anticipated profits. This enables a decomposition of subsequent problems according to the different aircraft types belonging to a common family, for each of which an aircraft routing problem and a crew scheduling or pairing problem are solved. Here, in the aircraft routing problem, a flight sequence or route is built for each individual aircraft so as to cover each flight exactly once at a minimum cost while satisfying maintenance requirements. Finally, in the crew scheduling or pairing optimization problem, a minimum cost set of crew rotations or pairings is constructed such that every flight is assigned a qualified crew and that work rules and collective agreements are satisfied. In practice, most airline companies solve these problems in a sequential manner to plan their operations, although recently, an increasing effort is being made to develop novel approaches for integrating some of the airline operations planning problems while retaining tractability. This dissertation formulates and analyzes three different models, each of which examines a composition of certain pertinent airline operational planning problems. A comprehensive fourth model is also proposed, but is relegated for future research. In the first model, we integrate fleet assignment and schedule design by simultaneously considering optional flight legs to select along with the assignment of aircraft types to all scheduled legs. In addition, we consider itinerary-based demands pertaining to multiple fare-classes. A polyhedral analysis of the proposed mixed-integer programming model is used to derive several classes of valid inequalities for tightening its representation. Solution approaches are developed by applying Benders decomposition method to the resulting lifted model, and computational experiments are conducted using real data obtained from a major U.S. airline (United Airlines) to demonstrate the efficacy of the proposed procedures as well as the benefits of integration. A comparison of the experimental results obtained for the basic integrated model and for its different enhanced representations reveals that the best modeling strategy among those tested is the one that utilizes a variety of five types of valid inequalities for moderately sized problems, and further implements a Benders decomposition approach for relatively larger problems. In addition, when a heuristic sequential fixing step is incorporated within the algorithm for even larger sized problems, the computational results demonstrate a less than 2% deterioration in solution quality, while reducing the effort by about 21%. We also performed an experiment to assess the impact of integration by comparing the proposed integrated model with a sequential implementation in which the schedule design is implemented separately before the fleet assignment stage based on two alternative profit maximizing submodels. The results obtained demonstrate a clear advantage of utilizing the integrated model, yielding an 11.4% and 5.5% increase in profits in comparison with using the latter two sequential models, which translates to an increase in annual profits by about $28.3 million and $13.7 million, respectively. The second proposed model augments the first model with additional features such as flexible flight times (i.e., departure time-windows), schedule balance, and demand recapture considerations. Optional flight legs are incorporated to facilitate the construction of a profitable schedule by optimally selecting among such alternatives in concert with assigning the available aircraft fleet to all the scheduled legs. Moreover, network effects and realistic demand patterns are effectively represented by examining itinerary-based demands as well as multiple fare-classes. Allowing flexibility on the departure times of scheduled flight legs within the framework of an integrated model increases connection opportunities for passengers, hence yielding robust schedules while saving fleet assignment costs. A provision is also made for airlines to capture an adequate market share by balancing flight schedules throughout the day. Furthermore, demand recapture considerations are modeled to more realistically represent revenue realizations. For this proposed mixed-integer programming model, which integrates the schedule design and fleet assignment processes while considering flexible flight times, schedule balance, and recapture issues, along with optional legs, itinerary-based demands, and multiple fare-classes, we perform a polyhedral analysis and utilize the Reformulation-Linearization Technique in concert with suitable separation routines to generate valid inequalities for tightening the model representation. Effective solution approaches are designed by applying Benders decomposition method to the resulting tightened model, and computational results are presented to demonstrate the efficacy of the proposed procedures. Using real data obtained from United Airlines, when flight times were permitted to shift by up to 10 minutes, the estimated increase in profits was about $14.9M/year over the baseline case where only original flight legs were used. Also, the computational results indicated a 1.52% and 0.49% increase in profits, respectively, over the baseline case, while considering two levels of schedule balance restrictions, which can evidently also enhance market shares. In addition, we measured the effect of recaptured demand with respect to the parameter that penalizes switches in itineraries. Using values of the parameter that reflect 1, 50, 100, or 200 dollars per switched passenger, this yielded increases in recaptured demand that induced additional profits of 2.10%, 2.09%, 2.02%, and 1.92%, respectively, over the baseline case. Overall, the results obtained from the two schedule balance variants of the proposed integrated model that accommodate all the features of flight retiming, schedule balance, and demand recapture simultaneously, demonstrated a clear advantage by way of $35.1 and $31.8 million increases in annual profits, respectively, over the baseline case in which none of these additional features is considered. In the third model, we integrate the schedule design, fleet assignment, and aircraft maintenance routing decisions, while considering optional legs, itinerary-based demands, flexible flight retimings, recapture, and multiple fare-classes. Instead of utilizing the traditional time-space network (TSN), we formulate this model based on a flight network (FN) that provides greater flexibility in accommodating integrated operational considerations. In order to consider through-flights (i.e., a sequence of flight legs served by the same aircraft), we append a set of constraints that matches aircraft assignments on certain inbound legs into a station with that on appropriate outbound legs at the same station. Through-flights can generate greater revenue because passengers are willing to pay a premium for not having to change aircraft on connecting flights, thereby reducing the possibility of delays and missed baggage. In order to tighten the model representation and reduce its complexity, we apply the Reformulation-Linearization Technique (RLT) and also generate other classes of valid inequalities. In addition, since the model possesses many equivalent feasible solutions that can be obtained by simply reindexing the aircraft of the same type that depart from the same station, we introduce a set of suitable hierarchical symmetry-breaking constraints to enhance the model solvability by distinguishing among aircraft of the same type. For the resulting large-scale augmented model formulation, we design a Benders decomposition-based solution methodology and present extensive computational results to demonstrate the efficacy of the proposed approach. We explored four different algorithmic variants, among which the best performing procedure (Algorithm A1) adopted two sequential levels of Benders partitioning method. We then applied Algorithm A1 to perform several experiments to study the effects of different modeling features and algorithmic strategies. A summary of the results obtained is as follows. First, the case that accommodated both mandatory and optional through-flight leg pairs in the model based on their relative effects on demands and enhanced revenues achieved the most profitable strategy, with an estimated increase in expected annual profits of $2.4 million over the baseline case. Second, utilizing symmetry-breaking constraints in concert with compatible objective perturbation terms greatly enhanced problem solvability and thus promoted the detection of improved solutions, resulting in a $5.8 million increase in estimated annual profits over the baseline case. Third, in the experiment that considers recapture of spilled demand from primary itineraries to other compatible itineraries, the different penalty parameter values (100, 50, and 1 dollars per re-routed passenger) induced average respective proportions of 3.2%, 3.4%, and 3.7% in recaptured demand, resulting in additional estimated annual profits of $3.7 million, $3.8 million, and $4.0 million over the baseline case. Finally, incorporating the proposed valid inequalities within the model to tighten its representation helped reduce the computational effort by 11% on average, while achieving better solutions that yielded on average an increase in estimated annual profits of $1.4 million. In closing, we propose a fourth more comprehensive model in which the crew scheduling problem is additionally integrated with fleet assignment and aircraft routing. This integration is important for airlines because crew costs are the second largest component of airline operating expenses (after fuel costs), and the assignment and routing of aircraft plus the assignment of crews are two closely interacting components of the planning process. Since crews are qualified to typically serve a single aircraft family that is comprised of aircraft types having a common cockpit configuration and crew rating, the aircraft fleeting and routing decisions significantly impact the ensuing assignment of cockpit crews to flights. Therefore it is worthwhile to investigate new models and solution approaches for the integrated fleeting, aircraft routing, and crew scheduling problem, where all of these important inter-dependent processes are handled simultaneously, and where the model can directly accommodate various work rules such as imposing a specified minimum and maximum number of flying hours for crews on any given pairing, and a minimum number of departures at a given crew base for each fleet group. However, given that the crew scheduling problem itself is highly complex because of the restrictive work rules that must be heeded while constructing viable duties and pairings, the formulated integrated model would require further manipulation and enhancements along with the design of sophisticated algorithms to render it solvable. We therefore recommend this study for future research, and we hope that the modeling, analysis, and algorithmic development and implementation work performed in this dissertation will lend methodological insights into achieving further advances along these lines.
Ph. D.
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Schoen, Pepijn. "Improving robustness of ITER maintenance schedules." Thesis, Linnéuniversitetet, Institutionen för datavetenskap (DV), 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-30529.

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In the experimental ITER fusion reactor, currently under construction in Cadarache, France, components close to the plasma will get activated and contaminated over time. Therefore, these components will need to be maintained remotely in a so called hot cell facility. The maintenance will be executed according to step-by-step procedures, with every procedure meticulously validated and formally described in an Operation Sequence Description (OSD). To support formal validation, OSDs are to be captured in an ITER-dedicated Structured Language (SL), a form of domain specific programming language. The Remote Handling Study Centre (RHSC), located at FOM institute DIFFER, Nieuwegein, The Netherlands, has an ITER Remote Handling Control Room representative setup which connects to a virtual ITER Hot Cell back-end environment. In this setup, Remote Handling maintenance scenarios and procedures can be tested under realistic conditions. In this thesis, a Structured Language for ITER maintenance is proposed, implemented as a prototype to capture several realistic operational sequences from existing projects, and integrated in the RHSC workflow. The Structured Language programs are converted to schedulable tasks, by inferring their resource usage and time duration from the structured task descriptions. These tasks are then scheduled using a proposed set of scheduling heuristics. The resulting schedule is subsequently submitted to many simulated executions, with random variation in the execution time of individual tasks. The variation that is applied in the execution time, is based on representative task execution times was measured for a recent and repetitive set of maintenance tasks performed at JET. It is shown that inserting a certain amount of slack in the schedule improves its robustness. ITER maintenance scheduling should account for uncertainty by including slack in the resulting planning.
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Pedraz, Ana Mana. "Stochastic optimization of aero-engine maintenance schedules." Thesis, Kingston University, 2010. http://eprints.kingston.ac.uk/20875/.

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In this thesis the maintenance scheduling optimization problem of a multi-component system is formulated as a multi-objective stochastic dynamic problem, which is solved by first transforming the multi-objective formulation into a single-objective one via the weighted-sum method, and then using an iterated two-stage stochastic linear programming approach to solve the stochastic problem. The main application is the maintenance of a commercial gas turbine aero-engine. The model determines optimal opportunistic replacement schedules considering both deterministic and stochastic engine configurations. The proposed methodology provides maintenance planners with a decision tool that simultaneously minimizes the expected total cost of providing the maintenance service, and the expected total number of service interruptions due to maintenance during a finite horizon. The analysis presented in this thesis considers the indentured configuration of a modular aero-engine and investigates the sensitivity of the solutions to variations in a number of critical parameters.
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Polesný, Ondřej. "Informační systém pro správu kurzů s automatickou tvorbou rozvrhů." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2013. http://www.nusl.cz/ntk/nusl-236404.

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This thesis analyzes the requirements of the dance school on the software produced and designs all layers required for implementation of this product. The product manages courses, their attendees, lectors, places where the courses are held and related financial operations. It also includes an interface for automatical generation of schedules for courses according to the preferences of the dance school and course attendees. This thesis also includes detailed model of database structure, object layer model, user interface designs and describes their implementation. Furthermore this thesis includes description of communication between all parties participating in created solution and feedback from users.
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Harunuzzaman, Mohammad. "Optimization of standby safety system maintenance schedules under reliability constraints /." The Ohio State University, 1994. http://rave.ohiolink.edu/etdc/view?acc_num=osu1487850665559726.

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Peschiera, Franco. "Exact and heuristic methods to optimize maintenances and flight schedules of military aircraft." Thesis, Toulouse, ISAE, 2020. http://www.theses.fr/2020ESAE0034.

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Cette thèse étudie le problème de planification de vol et de la maintenancedes avions militaires. D’abord, nous étudions la complexité de ce problème d’optimisation.Puis, nous proposons un modèle de programmation linéaire en nombres entiers (PLNE) pourle résoudre. Nous construisons un générateur d’instances et une heuristique pour générer dessolutions initiales. Ensuite, nous appliquons l’Apprentissage Automatique pour améliorer laperformance des modèles PLNE en utilisant des coupes valides générées à partir des conditionsinitiales et des coupes apprises à partir de la prédiction des caractéristiques de solutionsoptimales. Ces coupes sont appliquées à un nouveau modèle PLNE. Le résultat est une réductiondu temps de résolution avec peu de pertes d’optimalité et de faisabilité par rapport auxméthodes matheuristiques alternatives. Finalement, nous présentons une nouvelle matheuristiquepour résoudre efficacement des grandes instances. La méthode utilise une descente àvoisinage variable qui combine la programmation dynamique (DP) et l’horizon glissant. LaDP exploite une représentation en graphe de l’espace des solutions de chaque avion. Le résultatest des solutions rapides et presque optimales, et un passage à l’échelle efficace pourdes instances de très grande taille
This thesis studies the long term Military Flight and Maintenance Planningproblem. First, we evaluate the complexity of this optimisation problem. Then we propose aMixed Integer Programming (MIP) model to solve it. We develop an instance generator anda heuristic to generate initial solutions. Furthermore, we apply Machine Learning to improvethe performance of the MIP model by using valid cuts generated on the basis of initialconditions and learned cuts based on the prediction of characteristics of optimal solutions.These cuts are applied to a new MIP model. This results in reductions in the solutiontime with little losses in optimality and feasibility in comparison to alternative matheuristicmethods. Finally, we present a new matheuristic to efficiently solve large instances. Themethod employs a Variable Neighborhood Descent that combines Dynamic Programming(DP) and Rolling Horizon neighborhoods. The DP is applied to a graph representation of thesolution space for a single aircraft. This results in fast good quality solutions and an efficientscaling for very large instances
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Ahmadi, Alireza. "An assessment of operational consequences of failures to support aircraft scheduled maintenance program development." Licentiate thesis, Luleå : Luleå University of Technology, 2007. http://epubl.luth.se/1402-1757/2007/71.

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Aboulfath, Fouzia. "Optimal maintenance schedules for a fleet of vehicles under the constraint of a single repair facility." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1995. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/MQ51529.pdf.

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Books on the topic "Schedule maintenance"

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Trust, Ulster Historic Churches. Maintenance schedule for churches and good housekeeping guide. [S.l.]: Ulster Historic Churches Trust, 2000.

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ReVelle, Jack B. Home builder's guide to continuous improvement: Schedule, quality, customer satisfaction, cost, and safety. Boca Raton: Taylor & Francis, 2010.

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New York (State). Dept. of Audit and Control. Division of Management Audit. New York State Housing Finance Agency, housing project repair program work is significantly behind schedule, causing higher costs. [Albany, N.Y: The Office, 1991.

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Office, General Accounting. Navy maintenance: Cost growth and schedule overrun problems continue at the shipyards : report to the Chairman, Committee on Armed Services, House of Representatives. Washington, D.C: The Office, 1990.

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Office, General Accounting. Vehicle emissions inspection and maintenance program is behind schedule: Report to the Chairman, Subcommittee on Oversight and Investigations, Committee on Energy and Commerce, House of Representatives. Washington, D.C: U.S. General Accounting Office, 1985.

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Office, General Accounting. Federal research: Super collider is over budget and behind schedule : report to Congressional requesters. Washington, D.C: U.S. General Accounting Office, 1993.

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Aman, Amril. On-line maintenance of optimal schedules for a single machine. Cambridge, Mass: Sloan School of Management, Massachusetts Institute of Technology, 1991.

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Aboulfath, Fouzia. Optimal maintenance schedules for a fleet of vehicles under the constraint of a single repair facility. Ottawa: National Library of Canada, 1995.

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Office, General Accounting. Medicare: Laboratory fee schedules produced large beneficiary savings but no program savings : report to Congressional committees. Washington, D.C: The Office, 1987.

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Britain, Great. Schedule of Rates for Grounds Maintenance 1987. Stationery Office Books, 1987.

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Book chapters on the topic "Schedule maintenance"

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Tsumoto, Shusaku, Shoji Hirano, and Haruko Iwata. "Data-Oriented Maintenance of Schedule Management of Nursing Care." In Serviceology for Services, 169–77. Tokyo: Springer Japan, 2014. http://dx.doi.org/10.1007/978-4-431-54816-4_19.

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Crespo Márquez, Adolfo, Juan Francisco Gómez Fernández, Pedro Moreu de León, and Antonio Sola Rosique. "Online Reliability and Risk to Schedule the Preventive Maintenance in Network Utilities." In Advanced Maintenance Modelling for Asset Management, 245–61. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-58045-6_10.

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Zhang, Minchao, Xingyu Chen, Yue Hou, and Guiping Zhou. "Improved Multi-swarm PSO Based Maintenance Schedule of Power Communication Network." In Advances in Intelligent Systems and Computing, 955–63. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-14680-1_103.

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Wong, C. S., F. T. S. Chan, S. H. Chung, and B. Niu. "Minimizing Setup Time from Mold-Lifting Crane in Mold Maintenance Schedule." In Proceedings of the Institute of Industrial Engineers Asian Conference 2013, 739–47. Singapore: Springer Singapore, 2013. http://dx.doi.org/10.1007/978-981-4451-98-7_89.

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Weik, Martin H. "scheduled maintenance." In Computer Science and Communications Dictionary, 1523. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/1-4020-0613-6_16675.

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Bondavalli, Andrea, Ivan Mura, and Kishor S. Trivedi. "Dependability Modelling and Sensitivity Analysis of Scheduled Maintenance Systems." In Lecture Notes in Computer Science, 7–23. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/3-540-48254-7_2.

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Pant, Shashank, Zahra Sharif Khodaei, and Mohamad Ghazi Droubi. "Monitoring Tasks in Aerospace." In Structural Health Monitoring Damage Detection Systems for Aerospace, 5–14. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72192-3_2.

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AbstractApproximately up to one-fifth of the direct operating cost of a commercial civilian fixed-wing aircraft is projected to be due to inspection and maintenance alone. Managing aircraft health with minimal human intervention and technologies that can perform continuous or on-demand monitoring/evaluation of aircraft components without having to take the aircraft out of service can have a significant impact on increasing availability while reducing maintenance cost. The ambition of these monitoring technologies is to shift aircraft maintenance practice from planned maintenance (PM), where the aircraft is taken out of service for scheduled inspection/maintenance, to condition-based maintenance (CBM), where aircraft is taken out of service only when maintenance is required, while maintaining the required levels of safety. Structural health monitoring (SHM) techniques can play a vital role in progressing towards CBM practice. Therefore, this chapter aims to provide the reader with a brief overview of the different SHM techniques and their use, as well as, challenges in implementing them for aircraft applications.
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Porcarelli, Stefano, Felicita Di Giandomenico, Amine Chohra, and Andrea Bondavalli. "Tuning of Database Audits to Improve Scheduled Maintenance in Communication Systems." In Computer Safety, Reliability and Security, 238–48. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-45416-0_23.

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Yang, J. N., and Shiung Chen. "Fatigue Reliability of Structural Components under Scheduled Inspection and Repair Maintenance." In Probabilistic Methods in the Mechanics of Solids and Structures, 559–68. Berlin, Heidelberg: Springer Berlin Heidelberg, 1985. http://dx.doi.org/10.1007/978-3-642-82419-7_51.

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Kim, Dohhon, Jae-Hak Lim, and Ming J. Zuo. "Optimal schedules of two periodic preventive maintenance policies and their comparison." In Engineering Asset Lifecycle Management, 449–57. London: Springer London, 2010. http://dx.doi.org/10.1007/978-0-85729-320-6_50.

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Conference papers on the topic "Schedule maintenance"

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Dreyer, Samuel. "Advance Maintenance Planning and Schedule." In 2006 IEEE Autotestcon. IEEE, 2006. http://dx.doi.org/10.1109/autest.2006.283683.

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Cluever, Joseph, Thomas Esselman, and Paul Bruck. "Maintenance Optimization: Finding the Best Frequencies of Maintenance Activities." In ASME 2020 Pressure Vessels & Piping Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/pvp2020-21597.

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Abstract The nuclear industry has recently been shifting to value-based maintenance in order to keep nuclear power competitive in the power generation market. A key challenge in value-based maintenance is the optimization of a maintenance schedule. With most components having ten to fifteen available maintenances, the complexity of the optimization grows quite quickly. This paper presents a methodology for combining maintenance effectiveness, cost estimates, failure impacts, and overall reliability data to estimate an expected life cycle cost (LCC) for a component. The maintenance types are categorized into three types: monitoring, wear-rate reducing (e.g. oil change), or life-restoring (e.g. refurbishment). Each maintenance type has a different effect on problem detection, degradation rate, and future life expectancy. A Markov model uses the maintenance effects to estimate the distribution of what state of degradation a component is in at a specific time and concurrently, the component failures, maintenance costs, and failure impacts are tallied up in order to provide an expected life cycle cost for a given maintenance schedule. Optimization of the maintenance schedule is performed using the genetic algorithm where multiple maintenance schedules are simultaneously calculated, compared, and evolved in order to find the lowest expected life cycle costs. The genetic algorithm was selected as a suitable optimization algorithm for its ability to find relatively close approximations to the global optimum with relative ease while concurrently being able to handle non-smooth objective functions.
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Hunt, Bob, Bryn Turner, and Karen McRitchie. "Software Maintenance Implications on Cost and Schedule." In 2008 IEEE Aerospace Conference. IEEE, 2008. http://dx.doi.org/10.1109/aero.2008.4526688.

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SHIN, SANG-WOOK, DAE-KYUNG KIM, and JAE-HAK LIM. "OPTIMAL SCHEDULE FOR PERIODIC IMPERFECT PREVENTIVE MAINTENANCE." In Proceedings of the 2004 Asian International Workshop (AIWARM 2004). WORLD SCIENTIFIC, 2004. http://dx.doi.org/10.1142/9789812702685_0059.

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Abdeltawab, Hussein, and Yasser A. R. I. Mohamed. "Batteries Maintenance Schedule Optimization for Profit Maximization." In 2021 IEEE XXVIII International Conference on Electronics, Electrical Engineering and Computing (INTERCON). IEEE, 2021. http://dx.doi.org/10.1109/intercon52678.2021.9532738.

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Zou, Jing, Qing Chang, Yong Lei, Guoxian Xiao, and Jorge Arinez. "Stochastic Maintenance Opportunity Windows for Serial Production Line." In ASME 2015 International Manufacturing Science and Engineering Conference. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/msec2015-9307.

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Timely performance of preventive maintenance (PM) tasks is a critical element of manufacturing systems. The current PM at most manufacturing plants is to conduct maintenance tasks during non-production shifts, breaks, or other scheduled downtime. This practice may introduce unnecessary extra labor and overhead costs. Therefore, there is need to look for hidden maintenance opportunities to perform PM tasks during normal production time without impacting throughput. Additional benefit of the hidden opportunity window is that it can be translated to hidden energy saving opportunity during which machines can be strategically shut down or turned to energy saving mode while PM can be performed. Since production schedules are always made beforehand, the development of opportunity window and the downtime schedule need future prediction about the production system. In this paper, a stochastic model of the downtime opportunity on serial production systems is developed. Based on the stochastic model, revised opportunity window and recovery time is defined, which can help on production control on deciding when and where to insert the downtime events and the duration of the downtime events for the incoming process.
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Umeda, Shota, Kenji Tamaki, Masahiro Sumiya, and Yoshito Kamaji. "Planned Maintenance Schedule Update Method for Predictive Maintenance of Semiconductor Plasma Etcher." In 2020 International Symposium on Semiconductor Manufacturing (ISSM). IEEE, 2020. http://dx.doi.org/10.1109/issm51728.2020.9377534.

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Allen, Cody W., Chad M. Holcomb, and Mauricio de Oliveira. "Estimating Recoverable Performance Degradation Rates and Optimizing Maintenance Scheduling." In ASME Turbo Expo 2018: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/gt2018-75267.

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Many of the components on a gas turbine are subject to fouling and degradation over time due to debris buildup. For example, axial compressors are susceptible to degradation as a result of debris buildup on compressor blades. Similarly, air cooled lube oil heat exchangers incur degradation as a result of debris buildup in the cooling air passageways. In this paper, we develop a method for estimating the degradation rate of a given gas turbine component that experiences recoverable degradation due to normal operation over time. We then establish an economic maintenance scheduling model which utilizes the derived rate and user input economic factors to provide a locally optimal maintenance schedule with minimized operator costs. The rate estimation method makes use of statistical methods combined with historical data to give an algorithm with which a performance loss rate can be extracted from noisy data measurements. The economic maintenance schedule is then derived by minimizing the cost model in user specified intervals and the final schedule results as a combination of the locally optimized schedules. The goal of the combination of algorithms is to maximize component output and efficiency, while minimizing maintenance costs. The rate estimation method is validated by simulation where the underlying noisy data measurements come from a known probability distribution. Then, an example schedule optimization is provided to validate the economic optimization model and show the efficacy of the combined methods.
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Tezuka, Masaru, Satoshi Munakata, and Mikiko Sawada. "Maintenance schedule optimization based on failure probability distribution." In 2015 International Conference on Industrial Engineering and Operations Management (IEOM). IEEE, 2015. http://dx.doi.org/10.1109/ieom.2015.7093732.

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Alves, Tiago, and António R. Andrade. "Scheduling Maintenance Technicians in a Railway Depot." In 2021 Joint Rail Conference. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/jrc2021-60425.

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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.
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Reports on the topic "Schedule maintenance"

1

Jung, Ho-Won, and Dennis R. Goldenson. CMM - Based Process Improvement and Schedule Deviation in Software Maintenance. Fort Belvoir, VA: Defense Technical Information Center, July 2003. http://dx.doi.org/10.21236/ada416418.

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Ritchie, R. J., J. C. Notestine, J. S. Schmidt, J. N. Irvin, and C. P. Vaziri. Prediction of Scheduled and Preventative Maintenance Workload. Fort Belvoir, VA: Defense Technical Information Center, January 1985. http://dx.doi.org/10.21236/ada153761.

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