North American freight rail industry real-time optimized equipment distribution systems: State of the practice

Gorman, Michael F.; Crook, Kevin A.; Sellers, David

doi:10.1016/j.trc.2010.03.012

Cited by 23 publications

(21 citation statements)

References 15 publications

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“…Because the two formulations are similar, only the transportation problem is shown below. A detailed comparison of the two formulations can be found in Gorman et al (2011).…”

Section: Model Formulationmentioning

confidence: 99%

“…A critical insight into the trip plan helps drive empty assignment flexibility, improved dynamic decision making, and reduce costs: At yards where cars are sorted between trains and reblocked, there is little or no incremental costs of changing to which block a car goes (Gorman et al 2011). The car can easily be reassigned at any yard where it is reblocked.…”

Section: Optimization Model: Operational Systems: Decision Making Promentioning

confidence: 99%

“…Fourth, the train space required for moving empties is reduced, effectively expanding capacity for loaded movements, and saving locomotive fuel. Railroads have reported saving tens of millions of dollars per year and billions of capital avoidance from the implementation of railcar distribution systems (see Gorman et al 2010Gorman et al , 2011Narisetty et al 2008).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Handbook of Operations Research Applications at Railroads

Patty¹

2015

International Series in Operations Research &Amp; Management Science

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“…Because the two formulations are similar, only the transportation problem is shown below. A detailed comparison of the two formulations can be found in Gorman et al (2011).…”

Section: Model Formulationmentioning

confidence: 99%

Section: Optimization Model: Operational Systems: Decision Making Promentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Handbook of Operations Research Applications at Railroads

Patty¹

2015

International Series in Operations Research &Amp; Management Science

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“…Reviews of this problem class can be found in Crainic & Gendreau (1993), Crainic & Laporte (1997) and Powell et al (1995). The most common models used in industry are myopic (see Gorman et al (2011) for a summary of car distribution models at two major railroads) or they use a deterministic lookahead (rolling horizon) policy (Joborn et al (2004)). A number of authors have experimented with various approximations of stochastic models (Jordan & Turnquist (1983), List et al (2003), , Lam et al (2007)).…”

Section: Managing Fleets Of Vehiclesmentioning

confidence: 99%

Approximate dynamic programming in transportation and logistics: a unified framework

Powell

Simão

Bouzaiene-Ayari

2012

EURO Journal on Transportation and Logistics

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“…Another related problem area concerns equipment distribution (G), where empty rail cars are to be repositioned in the service network as to compensate for regional imbalances in freight flows. A recent review of the current state of the art in empty rail car distribution using optimization-based decision support systems is provided by Gorman et al [31]. Optimization-based equipment distribution models intended for railway systems with pre-defined operating plans, as in Sweden, have been proposed by e.g.…”

Section: Related Workmentioning

confidence: 99%

Trip plan generation using optimization: A benchmark of freight routing and scheduling policies within the carload service segment

Backåker¹,

Krasemann²

2012

Journal of Rail Transport Planning & Management

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The rail freight carload service segment enables the distribution of freight volumes down to the unit of single rail cars, and stand as an important alternative to road transportation. However, this service segment is often associated with significant uncertainties and variations in daily freight volumes. Such uncertainties are challenging to manage since operating plans generally are established long in advance of operations. Flexibility can instead be found in the way trip plans are generated. Previous research has shown that a commonly used trip plan generation policy does not exploit the available flexibility to the full extent. In this paper, we therefore suggest an optimization-based freight routing and scheduling (OFRS) policy to address the rail freight trip plan generation problem. This OFRSpolicy generates trip plans for rail cars while still restricted by the customer commitments. The policy involves a MIP formulation with a continuous time representation and is solved by commercial software. We apply the OFRS-policy on a case built on real data provided by the Swedish rail freight operator, Green Cargo, and assess the performance of the policy comparing the current industry practice. The results show that by using the OFRS policy, we can achieve a reduction in the total transportation times, number of shunting activities and potentially also a reduction in the service frequency given the considered transport demand. *Corresponding author (lars.backaker@liu.se, +46 (0) 11363481) Keywords: Rail freight planning, trip plan generation, routing and scheduling, dynamic assignmentIn this paper, we therefore suggest an optimization-based freight routing and scheduling (OFRS) policy to deal with the rail freight trip plan generation problem. The OFRS-policy routes and schedules rail cars onto available train services freely while still restricted by the customer commitments (e.g. agreed delivery time frames) and service characteristics (e.g. departure times and capacity limits). The policy involves a Mixed Integer Linear Programming (MILP) formulation which is solved by commercial optimization software. In contrast to previous models, our MILP-formulation has a continuous time representation which enables a more detailed representation of the service network and reduces the number of required binary variables. We apply the OFRS-policy on a case built on real data provided by the Swedish rail freight operator, Green Cargo. We also assess the performance of the OFRS-policy in a benchmark with the industry practice currently used by Green Cargo for the trip plan generation.The structure of the paper is as follows; in Section 2 we provide an introduction to the rail freight planning and distribution process, and existing rail car priority principles. In Section 3, we provide an overview and discussion of related work followed by Section 4, which presents our proposed optimization-based dynamic trip plan generation policy (OFRS). In Section 5, we outline our experiments and the results are presented and discussed i...

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North American freight rail industry real-time optimized equipment distribution systems: State of the practice

Cited by 23 publications

References 15 publications

Handbook of Operations Research Applications at Railroads

Handbook of Operations Research Applications at Railroads

Approximate dynamic programming in transportation and logistics: a unified framework

Trip plan generation using optimization: A benchmark of freight routing and scheduling policies within the carload service segment

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