Scheduling of bus maintenance by the P-graph methodology

Adonyi, R.; Heckl, István; Olti, F.

doi:10.1007/s11081-013-9240-8

Cited by 9 publications

(3 citation statements)

References 13 publications

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“…It is solved heuristically and compared with the mixed-integer programming approach in (Haghani and Shafahi, 2002). More recently, Adonyi et al (2013) developed a solution approach for the bus maintenance planning problem in public transportation through a P -graph methodology. Their model is applied to a case study, with the following data: the probability of bus failure, the available and required number of buses, maintenance crew capacity and maintenance cost in different shifts.…”

Section: Related Literaturementioning

confidence: 99%

Simultaneous scheduling of maintenance crew and maintenance tasks in bus operating companies: a case study

Martins

Fernandes

Infante

et al. 2021

JQME

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PurposeThe purpose of this paper is to describe an integer linear programming model to schedule the maintenance crew and the maintenance tasks in a bus operating company.Design/methodology/approachThe proposed methodology relies on an integer linear programming model that finds feasible maintenance schedules. It minimizes the costs associated with maintenance crew and the costs associated with unavailability. The model is applied in a real-world case study of a Portuguese bus operating company. A constructive heuristic approach is put forward, based on solving the maintenance scheduling problem for each bus separately.FindingsThe heuristic finds better solutions than the exact methods (based on branch-and-bound techniques) in a much lower computational time.Practical implicationsThe results suggest the relevance of such heuristic approaches for maintenance scheduling in practice.Originality/valueThis proposed model is an effective decision-making support method that provides feasible maintenance schedules for the maintenance technicians and for the maintenance tasks in a fleet of buses. It also complies with several operational, technical and labour constraints.

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Section: Related Literaturementioning

confidence: 99%

Simultaneous scheduling of maintenance crew and maintenance tasks in bus operating companies: a case study

Martins

Fernandes

Infante

et al. 2021

JQME

View full text Add to dashboard Cite

show abstract

“…Maintenance scheduling is a combinatorial optimization problem for which several methods have been proposed, such as genetic algorithms, simulated annealing, integer programming, and branch-and-bound techniques (Adonyi et al, 2013). The majority of the relevant scientific literature deals with maintenance in manufacturing, but a portion concentrates specifically on bus maintenance.…”

Section: Literature Reviewmentioning

confidence: 99%

A GRASP with efficient neighborhood search for the integrated maintenance and bus scheduling problem

Hama

Yagiura

2018

JAMDSM

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The overall planning process undertaken by a bus company is traditionally composed of five sub-processes: timetabling, vehicle scheduling, maintenance scheduling, crew scheduling, and crew rostering. Solving the full optimization problem is believed to be computationally intractable, and therefore in practice the five sub-processes are usually optimized in sequence. In this paper, we present a model that integrates the problems of vehicle scheduling and maintenance scheduling. The objective is to minimize the differences in mileage between buses, the total distance traveled, and the daily differences in the number of maintenance tasks. We propose a heuristic algorithm using the framework of greedy randomized adaptive search procedure (GRASP), and we improve the neighborhood search procedure by using an ordered list of possible trips. We compare the neighborhood search procedure with and without this mechanism and show that the ordered list reduces the number of neighbors to be checked by more than 95%, and it reduces the time to obtain solutions of the same or better quality by an average of 70%. Through computational experiments on instances generated from real-world data, we show that the proposed algorithm finds solutions that are as good as or better than those obtained by a commercial solver in less than 5% of the time required by the latter.

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“…Reliability centred maintenance (RCM) is one of a well‐known approach for developing non‐linear asset maintenance strategy in water utilities. RCM is to attain maximum system reliability applying PM strategies and tactics that can be implemented to prevent specific system failures 7 . Figure 2 shows stepwise approach for the development of the RCM strategy.…”

Section: Introductionmentioning

confidence: 99%

A reliability‐cost optimisation model for maintenance scheduling of wastewater treatment's power generation engines

Jolfaei

Jin

Linden

et al. 2021

Quality & Reliability Eng

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Power generation engines in wastewater treatment plants (WWTPs) are critical and strategic assets as major elements of an effective energy management system. Therefore, water utilities seek a smart tool to optimise the maintenance program on overall cost and reliability of these assets. In the previous study, a reliability model with consideration of redundancy was developed to estimate failure modes, reliability and availability of power generation engines in a WWTP. This study examines a joint model of reliability index using the Weibull model, operating maintenance cost using the steepest descent method and quasi‐Newton algorithm. This research is the first application of this joint model to power generation engines in WWTPs. A genetic algorithm has been employed to analyse and validate the modelling results as a global optimisation method. Results of the optimal solution are compared with conventional maintenance regimes of these engines based on recommended original equipment manufacturer (OEM). Results show an average of 22.3% reduction in maintenance costs of three engines after the implementation of the proposed cost‐optimization model. In addition, the manufacturer's recommended maintenance regime is not an appropriate maintenance strategy due to differences between the assumed and actual operating conditions. Also, hazard and reliability estimate using historical failure, operating and maintenance cost data has been ignored in the recommended OEM maintenance strategy.

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Scheduling of bus maintenance by the P-graph methodology

Cited by 9 publications

References 13 publications

Simultaneous scheduling of maintenance crew and maintenance tasks in bus operating companies: a case study

Simultaneous scheduling of maintenance crew and maintenance tasks in bus operating companies: a case study

A GRASP with efficient neighborhood search for the integrated maintenance and bus scheduling problem

A reliability‐cost optimisation model for maintenance scheduling of wastewater treatment's power generation engines

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