Tactical locomotive fleet sizing for freight train operations

Godwin, T.; Gopalan, Ram; Narendran, T.T.

doi:10.1016/j.tre.2006.11.004

Cited by 15 publications

(6 citation statements)

References 21 publications

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“…A natural extension is the recoverability of the network in case of disruptions by modifying schedules and assignments. Godwin et al [ 26 ] developed a simulation-based approach to calculate the maximum fleet size of locomotives on a rail network. The results showed that with an increase in locomotive fleet size, the network throughput would increase.…”

Section: Literature Reviewmentioning

confidence: 99%

Decisions on train rescheduling and locomotive assignment during the COVID-19 outbreak: A case of the Beijing-Tianjin intercity railway

Kang

Xiao

Sun

et al. 2022

Decision Support Systems

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Travel restriction measures have been widely implemented to curb the continued spread of COVID-19 during the Chinese Lunar New Year celebrations. Many operation lines and train schedules of China's railway were either heavily adjusted or canceled. In this study, a mixed-integer linear programming model and a two-step solution algorithm were developed to handle such large-scale adjustments. The formulation considers a flexible time window for each operation line and locomotive traction operations, and minimizes the number of locomotives utilized with their total idle time for train rescheduling and locomotive assignment, respectively. The solution algorithm determines the minimum locomotive fleet size based on the optimal train rescheduling results; it then reduces the traction idle time of locomotives. In response to the uncertainty of COVID-19, two tailored approaches were also designed to recover and remove operation lines, which can insert and cut operation lines based on the results of locomotive assignment. Finally, we conducted a case study of the Beijing-Tianjin intercity railway from the start of the COVID-19 outbreak in December 2019 to the recovery of operations.

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Section: Literature Reviewmentioning

confidence: 99%

Decisions on train rescheduling and locomotive assignment during the COVID-19 outbreak: A case of the Beijing-Tianjin intercity railway

Kang

Xiao

Sun

et al. 2022

Decision Support Systems

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“…Gertsbach and Gurevich [6] present locomotive fleet sizing problem to minimize the fleet size assuming uniform locomotives and constant travel time. Godwin et al [7] provide the use of simulation for locomotive fleet sizing in freight transportation. The rail system studied has no predefined time schedule.…”

Section: Literature Reviewmentioning

confidence: 99%

A hybrid metaheuristic algorithm for dynamic rail car fleet sizing problem

Yaghini

Khandaghabadi

2013

Applied Mathematical Modelling

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a b s t r a c tThe aim of this paper is to present a model and a solution method for rail freight car fleet sizing problem. The mathematical model is dynamic and multi-periodic and car demands and travel times are assumed deterministic, and the proposed solution method is hybridization of genetic algorithms and simulated annealing algorithms. Experimental analysis is conducted using several test problems. The results of the proposed algorithm and CPLEX software are compared. The results show high efficiency and effectiveness of the proposed algorithm. The solution method is applied to solve fleet sizing problem in the Iran Railways as a case study.

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“…In military applications, a simulation model was used to determine the fleet size applied to air mobility tasks (Wesolkowski and Billyard, 2008), and a combination of military judgement and integer programming to determine the size of the US destroyer fleet (Crary et al, 2002). Discrete-event simulation methods have been used in fleet sizing for train locomotives (Godwin et al, 2008) and anchor handling operations (Shyshou et al, 2010).…”

Section: Problem Illustrationmentioning

confidence: 99%

Using discrete-event simulation to predict the size of a fleet of naval combat helicopters

2013

Piantadosi, J., Anderssen, R.S. And Boland J. (Eds) MODSIM2013, 20th International Congress on Modelling and Simulation

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This paper describes the methodology developed to determine the size of a new fleet of combat helicopters for the Royal Australian Navy. The Australian Government's 2009 Defence White Paper described "as a matter of urgency" a requirement for a fleet of helicopters "to provide eight or more aircraft concurrently embarked on ships at sea". The objective therefore is to find the minimum fleet size that enables the fleet to meet both this minimum daily embarked requirement, as well as annual requirements for a specified number of flying hours for both embarked helicopters and the remaining ashore-based fleet. The fleet sizing problem incorporates the helicopter fleet, ships, personnel and a home base with maintenance facilities. Individual helicopters (or tails) pass through various states throughout their life. These include the serviceable state when they are able to fly, or a range of maintenance states which will make them unable to fly. Maintenance types include regular inspections, phased maintenance and deep maintenance. Many of these states can occur when a tail is either embarked or ashore. Unscheduled maintenance adds a random element to the problem and influences which aircraft may be serviceable on a day-today basis. A discreteevent simulation approach was chosen to address this problem, due to the requirements to track tails in various states, test for state transitions and incorporate random effects.

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Tactical locomotive fleet sizing for freight train operations

Cited by 15 publications

References 21 publications

Decisions on train rescheduling and locomotive assignment during the COVID-19 outbreak: A case of the Beijing-Tianjin intercity railway

Decisions on train rescheduling and locomotive assignment during the COVID-19 outbreak: A case of the Beijing-Tianjin intercity railway

A hybrid metaheuristic algorithm for dynamic rail car fleet sizing problem

Using discrete-event simulation to predict the size of a fleet of naval combat helicopters

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