On the coordination of maintenance scheduling for transportation fleets of many branches of a logistic service provider

Huang, Jia-Yen; Yao, Ming-Jong

doi:10.1016/j.camwa.2008.01.037

Cited by 12 publications

(8 citation statements)

References 17 publications

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“…• intelligent maintenance optimization systems for preventive maintenance (Kobbacy, 2004); • effi cient search algorithms to fi nd optimal solutions for the transportation fl eet maintenance scheduling problem (Huang and Yao, 2008); • total cost models to determine the policy that minimizes the total operating and maintenance cost of the transport fl eet (Goyal and Gunasekaran, 1992); • statistical analyses of past data on the rate of repair to determine laws to evaluate maintenance strategies (Pérès and Noyes, 2003); and • regression models to determine optimal planning of maintenance strategies (Ansell et al, 2003).…”

Section: Overcoming Traditional Maintenance Policy Limitations Througmentioning

confidence: 99%

Understanding short‐ and long‐term implications of “myopic” fleet maintenance policies: a system dynamics application to a city bus company

Bivona

Montemaggiore

2010

System Dynamics Review

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Financial losses recorded in city bus companies often force managers to implement restructuring strategies aimed at improving business results. However, such decisions may not produce the expected consequences for a number of reasons. Both the internal and external environment in which such companies operate can make the design and implementation of long-term sustainable policies quite diffi cult. In fact, the result may be that decision makers introduce "effective" policies in a company subsystem, while ignoring the mediumand long-term implications of such decisions in the performance of the whole company. With the aim to detect the main causes underlying "myopic" fl eet maintenance policies, an empirical analysis of two major Italian city bus companies has been conducted. This approach allowed the authors to make explicit the network of cause-and-effect relationships between different business subsystems and to build a system dynamics model to support city bus managers in designing and assessing alternative long-term sustainable strategies.

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Section: Overcoming Traditional Maintenance Policy Limitations Througmentioning

confidence: 99%

Understanding short‐ and long‐term implications of “myopic” fleet maintenance policies: a system dynamics application to a city bus company

Bivona

Montemaggiore

2010

System Dynamics Review

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“…Through neural network models, the behaviour of the historical series of phenomena is modelled without requiring a priori information about the series, by generating synthetic time series also adaptable to time series. Some cases of use of neural networks and stochastic models are reported in Campos et al 2010 Reis et al (2010), Araujo and Bezerra (2004), Huang and Yao (2008), Vujanović et al 2012, Gurney (1997.…”

Section: State-of-the-art Analysismentioning

confidence: 99%

Reserve Fleet Indexed to Exogenous Cost Variables

et al. 2019

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Identifying the optimal time to replace a passenger bus in a buses fleet has implications on the size of the reserve fleet. Such calculations rest on endogenous and exogenous economic variables: the former include operating and maintenance costs and bus depreciation; the latter include market imponderables such as the inflation and real discount rates, as well as energy costs, particularly fuel. The authors have created models for the withdrawal/replacement of buses using endogenous economic variables. The models include standard econometric models reflecting the influence of maintenance policies, especially Condition Monitoring (CM) or predictive maintenance, and the size of the reserve fleet. The paper deals with exogenous economic variables, specifically the influence of the cost of money, the inflation and real discount rates rate and the cost of fuel. Both variables fluctuate over time. The paper proposes analytical models for determining the influence of those variables on the withdrawal time and the size of the reserve fleet. It then comprehensively summarizes the variables in a global model, showing its relevance to the dimensioning of the reserve fleet and the withdrawal time.

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“…Using neural network models, they model the behavior of an historic data series without requiring a priori information about the series, by generating a synthetic time series adaptable to time series. Some cases of use of neural networks and stochastic models are reported in [1,4,15,24,26,27,33,35,37,50,54]. Araújo and Bezerra [4] demonstrate the feasibility of a component that implements a stochastic decision support model to integrate with corporate information systems, thus contributing to the efficiency and effectiveness of the decision-making process.…”

Section: State Of the Artmentioning

confidence: 99%

“…Other authors suggest the coordination of maintenance scheduling for the transportation fleets of many branches of a logistic service provider [27], the identification techniques of linear and nonlinear time series [33], the evaluation of vehicle fleet maintenance management indicators [50], and a chaotic time series prediction based on neural networks [54]. For work on neural networks, [15] and [26] are good references.…”

Section: State Of the Artmentioning

confidence: 99%

An integrated econometric model for bus replacement and determination of reserve fleet size based on predictive maintenance

Raposo¹,

Farinha²,

Ferreira³

et al. 2017

EiN

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On the coordination of maintenance scheduling for transportation fleets of many branches of a logistic service provider

Cited by 12 publications

References 17 publications

Understanding short‐ and long‐term implications of “myopic” fleet maintenance policies: a system dynamics application to a city bus company

Understanding short‐ and long‐term implications of “myopic” fleet maintenance policies: a system dynamics application to a city bus company

Reserve Fleet Indexed to Exogenous Cost Variables

An integrated econometric model for bus replacement and determination of reserve fleet size based on predictive maintenance

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