Vol. 40, No. 1, January–February 2010, pp. 5–16issn 0092-2102 �eissn 1526-551X �10 �4001 �0005

informs ®

doi 10.1287/inte.1090.0465©2010 INFORMS

THE FRANZ EDELMAN AWARDAchievement in Operations Research

CSX Railway Uses OR to Cash In onOptimized Equipment Distribution

Michael F. GormanDepartment of Management Information Systems, Operations Management, and Decision Sciences, School of Business,

University of Dayton, Dayton, Ohio 45419, michael.gorman@udayton.edu

Dharma Acharya, David SellersCSX Transportation, Jacksonville, Florida 32202

{dharma_acharya@CSX.com, david_sellers@CSX.com}

Each day, CSX Railway allocates hundreds of empty railcars among hundreds of customer car orders. In 1997,it implemented the US rail industry’s first real-time, fully integrated equipment-distribution optimization sys-tem, the dynamic car-planning system (DCP). DCP seamlessly integrates operations research modeling intoCSX’s process that assigns empty cars to customer car orders. CSX estimates that the DCP system saves thecompany more than $51 million annually and has saved $561 million since its implementation. DCP has alsoprovided $1.4 billion in capital-expenditure avoidance because of more efficient car allocation. Fewer railcarsyield improved return on assets and reduced congestion on the CSX rail network. Customer satisfaction has alsoincreased because of improved empty-car delivery. Public benefits include improved highway safety; reductionsin congestion, pollution, and greenhouse gases; and reduced tax-supported road maintenance, thus saving anestimated $600 million.

Key words : freight; transportation; rail; equipment; rolling stock; railcar; CSX; distribution; allocation.

CSX Transportation, Inc., one of the major freightrailroads in the United States, employs 35,000

people and earns $11 billion in annual revenue. It pro-vides a crucial link to the transportation supply chainthrough its 21,000-mile rail network, which servesover two-thirds of the US population in 23 stateseast of the Mississippi River and in parts of Canada(Figure 1). CSX serves 70 ports along the Atlanticand Gulf Coasts, the Mississippi River, the GreatLakes, and the St. Lawrence Seaway, and thou-sands of production and distribution facilities throughtrack connections to more than 230 short-line andregional railroads that provide service over all ofNorth America.To serve its general merchandise customers (e.g.,

those using boxcars, gondolas, hoppers, tank cars,etc.), CSX first delivers an empty car from its rail-

car fleet to the customer’s required loading location.After loading, the car moves to the customer’s loaddestination and is emptied. It then becomes availablefor a subsequent customer order, and the cycle beginsagain.The implications of improved empty-car distribu-

tion decisions are significant for CSX. Repositioningempty railcars to customer order locations createshundreds of thousands of empty-car miles each day.Empty-car miles generate wear and tear on the rail-cars and tracks, can require that additional trains berun, and create rail-yard congestion. Finally, emptycars represent lost revenue opportunities.

The Equipment-Distribution ProblemCSX and its predecessor, the B&O Railroad, havedelivered empty cars to customers for more than

5

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Figure 1: CSX’s railway’s 21,000-mile network spans most of the easternUnited States and connects to other railroads to provide service over allof North America.

180 years. Historically, decisions on which emptycars to deliver to which customers have been madelocally because timely and accurate centralized datadid not exist. Local decision makers had fewer carsand car orders to manage; however, local controlrequired many decision makers, led to inefficiencies(e.g., regional surpluses and shortages), and hinderedthe railroad’s car-distribution visibility and its con-trol over service levels. A long history of mergershas created today’s large CSX rail network. Driven bycompetitive forces brought about by rail deregulation,and enabled by computer use, equipment-distributiondecisions have been centralized, thus providing net-work efficiencies and improved service.Empty-car distribution is complex. Each day, hun-

dreds of CSX customers order hundreds of railcars.The source of the cars is a 90,000-car fleet (e.g., box-cars, gondolas, and hoppers) in a network with thou-sands of geographic locations. The car distributor

tries to find the best empty car for a customer order,with “best” established by multiple cost and customercar preferences and service requirements. The prob-lem has hundreds of thousands of possible solutions,and the constantly changing conditions exacerbate it.Throughout the day, equipment availability changesas customers return empty cars and send new andupdated car orders, and CSX takes cars offline forcleaning or maintenance.Confronted with the steady flow of information

updates on customer car orders and empty-car avail-ability, CSX had used several methods, including ship-per pools, single-car allocation rules, and the Sentinelweekly optimization model, to assist the car distributorand reduce empty-car miles. We describe each methodbelow.

Shipper PoolsA shipper pool is a set of railcars that is dedicated to asingle customer and is based on the customer’s aver-age demand and shipment cycle time. It eliminatesdaily allocation decisions, gives customers the com-fort of a guaranteed car supply, and simplifies fleetmanagement. Unfortunately, dedicating equipment toone customer hurts car utilization, and any variabil-ity in order patterns and cycle times hampers timelydeliveries. For example, Corona might ship beer fromMexico to New York in its dedicated pool, whereasInternational Paper ships paper products from thesoutheast to the southwest United States and Mex-ico in its dedicated pool. The transcontinental move-ments of empty cars in opposite directions are clearlyinefficient.

Single-Car Allocation SystemsA single-car allocation system, an expert-system ap-proach that is common in the North American railindustry, enables car distributors to use rules toautomate the large volume of car assignments. Forexample, based on experience and repeated trafficpatterns, a distributor might decide that “send box-cars in Birmingham to Smurfit Stone” or “gondo-las released in Buffalo go to Newark” are goodheuristic rules for decisions that occur regularly.As each empty car becomes available, the appro-priate rule is applied and the car is allocated toan order. This approach codifies and standardizesthe car distributors’ knowledge of empty-car flows.

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More importantly, it automates the execution of thou-sands of car assignment decisions and allows forcost-effective, continuous attention to assignmentsand immediate disposition of each empty car as itbecomes available.However, a single-car system has drawbacks. Effec-

tive rules are hard to design and must change asdemand patterns shift, making them cumbersome tomanage. Moreover, the approach provides subopti-mal solutions because myopic rules apply to cars ona first-come, first-served basis. Each car gets the bestassignment available, given the set of rules and theremaining unfilled car orders. The resulting heuris-tic assignments are influenced by the sequence ofevents. However, the timing of car orders and empty-car availability is unpredictable, making the perfor-mance of the rules difficult to predict.Figure 2 illustrates the sequence-dependence prob-

lem. Four cars become available in sequences 1through 4. In Figure 2(a), the sequence of car releasesrequires that the last car must move from Bostonto New Orleans, because New Orleans is the onlycar order remaining when the fourth car becomesavailable. If the cars had arrived in the order shownin Figure 2(b), Boston would have received a bet-ter assignment and fewer miles would have beenrequired to service all orders.

Emptyavailable

locationsCustomer order

locations Miles(1) Chicago (1) Detroit 300

(2) Newark (2) Philadelphia 100

(3) Atlanta (3) Cincinnati 500

(4) Boston (4) New Orleans 1,600Total miles 2,500

Empty

availablelocations

Customer orderlocations Miles

(4) Boston (1) Detroit 300(1) Chicago (2) Philadelphia 300(2) Newark (3) Cincinnati 850(3) Atlanta (4) New Orleans 650

Total miles 2,100

(a)

(b)

Average empty travel distance: 625 miles/empty car

Average empty travel distance: 525 miles/empty car

Figure 2: Sequential single-car heuristic assignments depend on thesequence of car-order and car-release events; the result is poor and unpre-dictable results.

When possible, car distributors handle exceptionsto the rule-based assignments by overriding theseassignments; however, distributors cannot evaluateeach rule-based assignment because of the volume ofassignments. Therefore, opportunities for improvingempty-car distributions are lost.

The Sentinel SystemIn 1990, CSX implemented the Sentinel system, thefirst known optimization-based equipment-distribu-tion system in US freight railroads (Turnquist 1986,1994; Markowicz and Turnquist 1990) to remedythe single-car system problems. Sentinel performsa weekly, fixed-horizon optimization for forecastedorders and car supply to establish optimization-basedallocation that the railroad uses to derive rules thatare manually entered into a single-car system.The weekly plan that Sentinel produces provides

a more global network view for the car distribu-tor; however, because it is a seven-day, fixed-horizonmodel, it becomes outdated soon after being gener-ated because of rapidly changing supply and demandconditions. It relies heavily on week-ahead forecastsof expected car supply and demand; however, whenforecasts are incorrect, the results are no longer usefulbecause Sentinel cannot take advantage of unfore-casted shifts in car supply and demand. It does notintegrate with a single-car system, and implementingits solutions is labor intensive and costly. Moreover,its recommendations must still be executed via thesequence-dependent, single-car system.

The Dynamic Car-Planning SystemCSX needed to combine the automation of the single-car system with Sentinel’s holistic network view ofsupply and demand. In 1997, it implemented its state-of-the-art, real-time dynamic car-planning (DCP) sys-tem to more effectively distribute empty railcars tomeet customer orders. The system cost $5 million andtook two years to develop. DCP is written in C++on a UNIX/RISC midtier computer linked to a main-frame computer. It uses a proprietary solver and aSybase database for data management. In the follow-ing sections, we describe DCP’s operations research(OR) modeling philosophy and the system, process,and organizational and commercial considerationscrucial to its success.

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OR Modeling ConsiderationsCSX could have deployed other modeling alterna-tives for DCP. Gorman et al. (2009), Cordeau et al.(1998), Powell et al. (1995), Crainic et al. (1993), andDejax and Crainic (1987) provide detailed discussionsof some alternatives.Shipped cars follow a trip plan (Ahuja et al. 2007,

Ireland et al. 2004) that determines a series of trainsand intermediate connection yards from origin to des-tination and the estimated time in transit. The CSXnetwork includes both cars and orders of varyingtypes at various locations, at various dates, and con-nected by trip plans; thus, modeling the problem as amuticommodity, flow-time-space network of sources,sinks, and connecting trains or trip plans might seemintuitive, as Joborn (1995) and Holmberg et al. (1998)describe for Swedish National Rail. Such a specifica-tion, however, has substantial data requirements andlong solution times, and it is subject to the uncertain-ties of train travel times and supply and demand.To address these uncertainties, Topaloglu andPowell

(2006) and Powell and Topaloglu (2005) employ sto-chastic, multicommodity flow modeling to empty-car distribution at Norfolk Southern Railroad. WhenCSX was developing DCP, neither railroad had fullyresearched or deployed these projects. Moreover, CSXdid not feel the need to introduce such modelingcomplexity given the near-term focus of most allo-cation decisions and the sphere of influence of thecar distributors within CSX’s organizational structure.It adopted a simpler minimum cost flow formula-tion. This modeling approach enables quick formula-tion and solution and a modicum of information tobe communicated from DCP to operations. The BNSFRailway (Gorman et al. 2009) and Union Pacific Rail-way (Narisetty et al. 2008) also subsequently imple-mented similar basic designs.

System Design and IntegrationFigure 3 shows a diagram of DCP’s input sources,modules, and integration into production systems.Table 1 shows its key input data. It has fiveinput sources: three external and two internal tothe equipment-distribution organization. Key externalinputs to the system are the customer car orders,the available cars that might meet these needs,

and the transit-time standards. A customer order spec-ifies the car type, any required and preferred fea-tures (e.g., capacity, door height), and the requireddate and location. Each car has a set of attributes,a location, and a date of availability. The illustrationin Table 1 shows a 50-foot rigid boxcar available onday 1; in some cases, empty cars are anticipated asavailable on future dates. Transit-time standards andyard handlings are established weekly for all origin-destination pairs based on current train schedules andcar-trip plans for each origin and destination on theCSX network.The equipment-distribution organization manages

two key input files: customer profiles that specify cus-tomer priority, car preferences, and allowable sub-stitute cars for each customer and an acceptableearliness and lateness window for car deliveries, andDCP cost parameters that define the hard and softcosts that DCP uses to establish cost parameters.The feasibility engine (Figure 3) checks the attri-

butes of each empty car and the customer-preferenceprofile to assure the permissibility of each match. Car-order pairs that the feasibility engine deems infea-sible for either service or car-preference reasons arescreened from the DCP optimization engine. Based ontransit-time standards and customer-profile specifica-tions, the feasibility engine identifies permissible carsthat can meet a customer-specified time window andexcludes those that cannot. For example, the last tworows of Table 1 show examples of infeasible car assign-ments. The car is not an acceptable type to customer6 and, by CSX transit standards, cannot be deliveredto customer 7 within the lateness tolerance constraint.The infeasibility is shown as a high cost; the optimiza-tion model excludes these pairings from considerationbecause they are deemed infeasible. Simply put, is thisthe car the customer wants, and can it get to the cus-tomer on time?The costing engine (Figure 3) includes numerous

complex hard and soft cost components of eachassignment. As Table 1 illustrates, the primary hard-dollar costs in empty-car movements are car travel dis-tance (fuel, depreciation), car handling costs (the costassociated with car movements at yards between twotrains), and car travel time (opportunity cost and carhire costs). CSX also includes soft-service costs toassure quality service. It applies a customer priority

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Customerorders

Caravailability

Operationalinformation

Feasibility engine: Preprocessing for candidate assignments

Costing engine: All feasible candidate assignments

Dynamic car-planningoptimization engine

Car-to-orderassignments

Trip-planning engine: Routes car from origin to customer order

Individualcar trip plans

Car-distribution

organization

Costparameters

Customerprofiles

Figure 3: DCP is tightly integrated with operational and marketing data sources as well as production systems. Itchecks the customer preference and operational feasibility of each assignment, assigns a cost to it via the costingengine, optimizes the network, and communicates car-order assignments to operations on an as-needed basis.Operational systems constantly update DCP on an empty car’s next available location for possible replanning.

value to customer orders when making equipmentassignment decisions during periods of short supplyand applies an early and late penalty to encourage on-time delivery. The cost of being late is lost customergoodwill. The cost of being early is based on ship-per dock congestion and equipment idle time. The carpreference mismatch cost is applied when equipmentattributes do not match a customer’s first choice butcan suffice; for example, the car is not a perfect matchbecause of an incorrect door height, length, or cubiccapacity but is still usable. In Table 1, we see that cus-tomers with a desire for a 50-foot rigid box car haveno mismatch penalty, but customers desiring otherboxcar types are assessed a penalty to discourage theDCP assignment of a 50-foot rigid boxcar.The costing engine can handle any level of complex-

ity (including convexity of lateness, demand priorities,fixed costs of car handling, customer priorities, and

preferences). It converts the nonlinearities and discon-tinuities in the cost function to a single-cost coefficientof assigning a specific car to a specific order, as thefar right column in Table 1 shows.

DCP Model FormulationDCP’s optimization engine finds the best set ofcar-to-order assignments and provides a car and adestination to operations to create a trip plan for eachcar. The optimization engine is based on a straightfor-ward minimum cost flow problem, made possible bypreprocessing the feasibility and costing engines. Wesummarize the model below; the appendix shows themathematical formulation.

Minimize total hard car costs and soft penalty costs= (car mile cost+ car time cost+ car handling cost)

+ (early penalty+ late penalty+priority penalty+ car mismatch penalty)�

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Cartoallocate:5

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Samplecostparameters:

Carm

ilecost($)

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($)

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Latepenalty

($)

Early

penalty

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reference($)

123

5089

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76

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subject to• All car orders are met,• Supply of each car type is not exceeded,• Service constraint: Car is delivered within the

allowable time window,• Car-type constraint: Car delivered meets the

allowable car-type constraint. This formulation fitswith the organizational, operational, and commercialconsiderations, and CSX’s rapidly changing condi-tions, as described below.

Organizational ConsiderationsThe car distributor is concerned with the feasibilityand cost of the car-order pairing but cannot affectthe operational details of the car’s trip plan, whichare the responsibility of CSX’s operations depart-ment. The model formulation focuses solely on thecar assignment, using the information of the under-lying trip plan for determining feasibility but treat-ing it as exogenous. The time-space network conceptis preserved through differing supply and demandnodes for each car-availability date and customerorder and is taken into account by the feasibility andcosting engines. Thus, the time-space modeling thatSwedish National Railway uses (Joborn 1995, Holm-berg et al. 1998) is less appropriate and unnecessarygiven CSX’s organizational structure, utilization of thetrip-planning engine, and decision sequence.

Operational ConsiderationsTo handle the inherent future uncertainty of the prob-lem, CSX solves the deterministic DCP model fre-quently (i.e., every 15 minutes throughout the day)to capture changing supply-and-demand conditions.The model takes 1 minute to load and 10 secondsto solve. The result is a best-deterministic solutionthat responds quickly to changing conditions, such assupply-and-demand shifts.The OR modeling in DCP is simple. The approach

enables CSX to reoptimize quickly and adjust plansas new information becomes available. From its Sen-tinel experience, CSX realized that solving the modelfrequently and continually refreshing solutions basedon the current best information produced the mostuseful, effective, and implementable solutions. UnlikeSentinel, DCP bases its solutions on a rolling hori-zon; at any given time, DCP looks two weeks ahead

to avoid the short-sightedness of single-car systems.Although DCP might generate multiple plans fora car over time, CSX defers decisions until theyare required, i.e., the time at which the operationsdepartment needs disposition on the cars. Thus, itavoids thrashing (i.e., frequent reversals of previousdecisions). This is a fundamental difference betweenDCP and the single-car system, in which a car isgiven a single and final assignment immediately afterthe event that makes the car available (Figure 3).Other modeling approaches that endogenize uncer-tainty (e.g., Topaloglu and Powell 2006) are more dataintensive, take longer to run, and are thus run lessoften. CSX has been successful with this simpler, intu-itive, and real-time approach that focuses on immedi-ate executable decisions while keeping a placeholderfor future supply-and-demand parings.DCP goes beyond deferring a decision until the trip

plan starts. It can revisit and revise the decision evenafter an empty car has begun its trip plan, and theoperations group can seamlessly execute according tothe change in plan. Figure 4 shows the integrationof DCP with operations. The DCP car assignment isavailable when a trip begins. The empty car typicallyhas multiple trains and intermediate handling yardsin its trip plan. Each handling yard removes the carfrom the inbound train and switches it to the out-bound train. The yard can easily handle a reassign-ment if it receives notice prior to the car’s arrival at theyard. After an empty car has started its trip, it appearsto DCP as supply at the car’s future intermediate han-dling yard and date of availability. If in subsequent

DCP assignment isavailable

Car moves to nexthandling yard

Intermediatehandling yard

Empty car is considered supplyby DCPat the next yard

A trip plan includes multiplehandling yards

Final handlingyard

Assignment is locked at the finalhandling yard, 72 hours before delivery

Customer delivery

Yardrequests anassignmentfor anempty car

Figure 4: DCP integrates tightly with the CSX operational process. A carmight receive many assignments before it begins moving and is a candi-date for assignment at each intermediate handling yard. The car assign-ment is held by DCP until needed by operations, effectively deferring finaldecisions until necessary.

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Empty

availablelocations

Customer orderlocations Miles

(1) Chicago (1) Detroit 300

(2) Newark (2) Philadelphia 100(3) Atlanta (3) Cincinnati 500

(4) Boston (4) New Orleans 150

(5) Mobile (5) Springfield 50

Total miles 1,100Average empty travel distance: 220 miles/empty car

Figure 5: OR allows DCP to take a network view, making the sequenceof events immaterial. Deferring and revisiting decisions can save manymiles. In this example, empty miles are reduced by more than 50 percent,and an additional customer is served.

DCP optimization runs the original assignment is stilloptimal, DCP will “reassign” the car to the same cus-tomer; however, DCP might also revise the final des-tination at that time because new supply or demandinformation has changed the car’s optimal assign-ment. In either case, the car assignment executed bythe intermediate handling yard is finalized when oper-ations pulls the current car assignment from DCPbefore the car arrives at the handling yard. This capa-bility gives DCP opportunities to improve the assign-ment of each empty car after its trip has begun.Figure 5 shows the effect of one more car and

order on the original empty-car distribution net-work (Figure 2). The last order and available carmight become available after the other cars have beenassigned and have begun their trips. By dynamicallyreassigning previously assigned cars, total empty-carmiles decrease even as the number of customer ordersserved increases.

Commercial ConsiderationsSome CSX customers were accustomed to pools ofcars dedicated to their individual use and were notcomfortable with relinquishing that control. Otherswere familiar with the single-car allocation systemsand could track “their” empty car as it traversed theCSX network toward their facility. This is akin torequiring Hertz to provide a customer with the licenseplate number of a car reserved for the following week.To improve operational efficiency and customer deliv-ery, CSX had to remove the artificial constraints, andits customers had to trust that CSX would deliveron time a car that matched their specifications. CSX“locks” the final assignment at the last intermediate

handling yard, 72 hours prior to delivery. DCP is notlikely to change the car’s destination, and providingthis insurance relieves any customer concerns, thusimproving customer service.DCP relies on the accuracy of customer orders and

forecasts. CSX requires a one-week lead time on allcar orders and requests its customers to provide a six-week forecast of car orders. Although not binding,this forecast is vastly superior to any statistical fore-cast that CSX might develop. CSX and its customersboth benefit from this collaborative relationship.

Challenges and Best Practices

ChallengesCSX was the first railroad to implement real-timedecision support systems for empty-car distribution.Understandably, its customers had concerns abouthow the system would affect their service. Moreover,CSX field-operating personnel were used to havingmore autonomy in making car allocation decisions;some did not understand or did not want to followDCP recommendations. CSX had to provide consider-able training and communications to ensure that theseconstituencies would embrace the dramatic change.The communications efforts were made more difficultbecause CSX chose a simultaneous rollout of DCP onthe entire 90,000 DCP-managed car fleet, an approachthat accelerates benefits but increases cost and risk.Because of the difficulties of trading off hard and

soft costs, model cost-parameter tuning is critical toobtaining good results. Car distributors and fleet man-agers work with the DCP system, adjusting parame-ters by fleet, geographic region, and customer. The cardistributors’ job description changed with the incep-tion of DCP; they became costing technicians ratherthan car allocators. Car distributors manage DCPexceptions rather than each car assignment. CSX wasputting this innovative system in the hands of savvycar distributors; however, they did not know OR.Training on how the model works and why it differedfrom previous methods was essential for user accep-tance. Illustrations such as Figures 1 and 4 helpedusers to understand the new system.

Best PracticesCSX found that having internal senior-level “champi-ons” helped garner resources for DCP development

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Gorman et al.: CSX Railway Uses OR to Cash In on Optimized Equipment DistributionInterfaces 40(1), pp. 5–16, © 2010 INFORMS 13

and implementation. It identified key car-distributionexperts who supported the more sophisticated ap-proach; they were instrumental to overcoming prob-lems during implementation.CSX’s experience with Sentinel paved the way for

the more advanced DCP system. First, it simplifiedthe financial justification of the new system; Sentinelshowed the value of optimization and the need forreal-time data feeds and system integration. Second,it led to quicker user acceptance by the fleet man-agers. CSX’s evolution from single-car heuristics, toweekly fixed-horizon optimization, to DCP’s dynamicand integrated optimization provided insights intohow to improve empty-car distribution.

DCP BenefitsDCP has provided substantial benefits to all stake-holders, including CSX, its customers, the rail indus-try, and the US public, as we describe below.

Financial Benefits to CSXSince its DCP implementation, CSX has needed10 fewer car distributors, thus saving $1 million peryear. More importantly, empty cars now travel farfewer miles to their next load destination. Each savedempty-car mile reduces fuel, crew, and equipmentdepreciation costs. We show our calculation method-ology below.We developed a savings method based on the

improvement in empty miles for CSX’s biggest fleets,boxcars, and gondolas. Table 2 shows the empty milesand loaded miles in 2006 and estimated savings rel-ative to the 1:1 empty-load ratio that CSX attainedprior to implementing DCP. We projected the emptymiles for 2006 (without DCP) based on the historicalratio prior to the DCP implementation and the actualloaded miles.We used multiple methods to estimate and validate

our savings before and after implementing DCP. Welooked at additional years, and compared DCP withnon-DCP in the same year and CSX with other rail-roads in the same fleet. First, we looked at the years2002 through 2008 for consistency of results withinthe boxcar and gondola fleet over time. Second, wecompared the empty-load ratio for DCP-managedcars with “foreign” (owned by other railroads) and“private” (owned by shippers) railcar ratios in the

Empty movement cost per mile $0.80

Boxcar savingsTotal estimated empty miles without DCP 26�938�800Total empty miles with DCP 17�959�200Mileage savings from DCP 8�979�600Annual DCP boxcar savings (at $0.80 per mile) $7�183�680

52-foot mill gondola savingsTotal estimated empty miles without DCP 47�566�478Total empty miles with DCP 37�050�167Mileage savings from DCP 10�516�311Annual DCP gondola savings (at $0.80 per mile) $8�413�049

Total savings: Gondolas and boxcars $15�596�729

Estimated annual total savings for entire fleet $50�312�028

Table 2: The gondolas and boxcars in these examples are approximately30 percent of the DCP-managed fleet and are representative of the sav-ings achieved in other fleets. Applying these savings across the remainingDCP-managed fleets results in $51 million in annual savings and $561million savings in the years between 1997 and 2007.

same year. Third, we compared the CSX empty-loadratio for the cars in the North American boxcar fleet(a shared railroad pool of cars) with the empty-loadratio achieved by other railroads. In each case, wefound that these comparisons supported our savingsestimate.

Capital-Avoidance SavingsWhen cars spend less time empty, they can spendmore time loaded. One could evaluate DCP’s ben-efits by estimating volume increase and revenueenhancement from the railcar fleet’s more efficientuse. More conservatively, we quantify DCP’s bene-fits using the estimated reduction in the size of rail-car fleets required to serve existing business. CSX hasavoided capital expenditures by making better useof its existing fleets rather than buying more cars. IfCSX had to support its current business with its 1997empty miles per load, the fleet would require an addi-tional 18,000 cars at a replacement cost of $75,000. Therailcar capital investment avoided is approximately$1.4 billion, with a resulting increase in CSX’s returnon assets because of using each car more efficiently.By having fewer cars on its network, CSX reduces

yard congestion; by reducing the number of trainsthat it must run, it reduces track congestion, a majorrail-industry problem (Gorman 2009). Highly utilizedyard, line, and train capacity can result in delayedempty deliveries, or worse, lost orders and revenue.

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Gorman et al.: CSX Railway Uses OR to Cash In on Optimized Equipment Distribution14 Interfaces 40(1), pp. 5–16, © 2010 INFORMS

By reducing car fleets and empty miles, DCP allevi-ates these problems, reducing cost and improving ser-vice to customers. Although we do not quantify themin our estimate, these savings are significant.

Customer SatisfactionUsing DCP, the car manager can more easily sub-stitute equivalent cars when a shortage exists andcan more actively track fulfillment status, improv-ing customer satisfaction by making it more likelythat the customer will get the right car on the rightday. Largely because of the improved order-fill ratesand timely deliveries made possible by DCP, CSX’scustomer “car-order” score was the highest of allcustomer-satisfaction scores that CSX achieved in its2008 J.D. Power and Associates survey (Figure 6).Thus, CSX has been able to retain and grow businessthat might have otherwise gone to a competing rail-road or to a truck.

Rail-Industry BenefitsCSX has led the North American rail industry in thedevelopment of optimized empty-car distribution sys-tems. Ireland et al. (2004) report that in 2002, CanadianPacific (CP) used the methodology that Turnquistdeveloped (Turnquist 1986, 1994; Markowicz andTurnquist 1990), and CSX had implemented as theSentinel system. CP subsequently purchased DCPfrom CSX. Other railroads have benchmarked DCP,implemented similar systems, and attained improve-ments. BNSF implemented a system of similar design

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2003 2004 2005 2006 2007 2008

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CSX customer car-order satisfaction score

Figure 6: CSX’s customer car-order satisfaction rating has increasedsteadily since the DCP implementation. CSX has gradually increased itsJ.D. Power car satisfaction ratings since 2003, the first year of tracking.Source of data. J.D. Power and Associates.

in 2000 (Gorman et al. 2009); Union Pacific Railwayfollowed suit in 2003 (Narisetty et al. 2008). NorfolkSouthern Railroad is also benchmarking DCP.The use of empty-car distribution systems by other

railroads indirectly benefits CSX because these sys-tems enable the railcar fleet in the US rail networkto be used more efficiently. Notably, the major rail-roads have developed a shared boxcar fleet, effec-tively breaking down the railroad-specific ownershipconstraint in car-fleet management. Systems such asDCP enable the management of such a fleet.

US Public BenefitsThe public benefit derived from DCP resultsfrom rail’s inherent societal advantages over trucks(Gorman 2008) and CSX’s expanded ability to serverail customers. If CSX had not invested in eitherDCP or 18,000 new cars, carrying today’s rail loadwould have required 383,250 trucks, given typicalcycle times, a 3.5:1 railcar-to-truck weight ratio, and a50 percent rate of conversion to truck (Table 3).Rail is a green technology; Gorman (2008) esti-

mates that rail freight incurs only 20 percent of

Societal savings calculations

DCP cars saved (CSX estimate) 18,000Car cycle time (days) 30Cycles per year (loads/car) 12.17Railcars per year (cars ∗ loads/car) 219,000Percent diverted from truck (CSX estimate) 50%Tons/truck (CSX estimate) 20Tons/car (CSX estimate) 70Trucks/car (Ratio: tons per railcar/tons/truck) 4Annual reduction in truckloads 383,250

(diversion ∗ railcars ∗ truck/car)Avg miles/load (CSX estimate) 614Tons/year (cars ∗ cycles ∗ tons/car) 15,330,000Ton miles/year (miles/load*tons/year) 9,412,620,000Truck social costs per ton mile (Gorman 2008) $0.0144Int. rail social cost per ton mile (Gorman 2008) $0.0028Annual social cost truck (cost/ton mile truck ∗ ton miles) $67,770,864Annual social cost rail (cost/ton mile rail ∗ ton miles) $13,177,668Annual social cost savings maximum (truck-rail) $54,593,196

Estimated 11-year social savings $600,525,156

Table 3: Given a CSX average of 614 miles per load and typical weight ofa railcar, we calculate 15 million tons moved, or 9.4 billion ton-miles.Using published values for relative public costs of truck and rail, we cal-culate truck cost at $68 million and rail at $13 million, thus calculating a$55 million annual incremental truck cost (without DCP). Over 11 years,this represents approximately $600 million in public costs avoided.

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Gorman et al.: CSX Railway Uses OR to Cash In on Optimized Equipment DistributionInterfaces 40(1), pp. 5–16, © 2010 INFORMS 15

the public costs of comparable truck freight, result-ing in improved road safety and reduced pollution,greenhouse gases, congestion, and tax burdens fromunfunded road maintenance.Given the number of shipments and their typical

weight and distance, DCP has contributed approxi-mately $600 million in public benefits since its incep-tion. Of course, because of the vagaries of developingsuch an estimate, we intend it only as an indicator ofthe order of magnitude of the public benefit. How-ever, we note that this benefit estimate is based on con-servative savings values and from CSX’s experience,not including road-to-rail diversions made possible bysimilar railroad car-distribution systems. DCP’s annu-alized public and private benefit is approximately $250million per year, not including benefits to and fromother railroads.

DCP Improvements and Future UseOver the last decade, CSX has continued to invest inand enhance DCP. It has improved costing parame-ters, accuracy and completeness of customer profiles,and real-time reporting information on train oper-ations and car status to make DCP more effective.Web-based order-management and visibility tools,known as ShipCSX, have made DCP more customerfriendly and have improved car order-forecast accu-racy. DCP’s use over the previous decade indicatesboth the system’s stability and its fit with CSX’s long-term equipment-distribution needs. Simply stated,CSX could not revert to previous empty-car distribu-tion methods and continue to operate with its existingfleet and network capacity.

SummaryCSX required a real-time empty-car distributionsystem that was nimble and robust enough to(1) adjust to rapidly changing conditions throughoutthe day by constantly refreshing the current best solu-tion, (2) defer decisions until they must be made,(3) revisit previously made decisions when needed,and (4) seamlessly integrate with the car-distributordecision process and communicate decisions to bothcustomers and operations. DCP provides all thesefunctionalities.Without tight real-time systems and process inte-

gration of both inputs and outputs, DCP could not

(1) have the required current information for qualitymodel results, (2) make the information available tothe field on an as-needed basis, and (3) change therouting of a car as new information becomes avail-able and only as operationally feasible. These fea-tures, coupled with fast OR model-solution times,drive DCP’s success.DCP enables CSX’s continued quality customer

service and growth. Its sustained use over the lastdecade gives evidence to its stability and potentialfuture use. DCP is responsible for almost $2 billionin capital and operating savings to CSX and an addi-tional $600 million in public benefits, or $250 millionper year in annualized public and private benefits,not including benefits to other railroads. The subse-quent adoption of such systems by other railroads isfurther evidence of the widespread transferable bene-fits of OR-based empty-car distribution systems. DCPexemplifies the importance of operations research inmanaging the ever-increasing complexity of the USrail network and is perhaps the most successful ORapplication in the rail industry.

AppendixThe optimization model (Equations (1)–(6)) is basedon a minimum cost flow formulation. Let a be a vec-tor of car attributes such as car type, date and loca-tion of availability, etc. Let b be the set of customerpreferences on desired and substitute car types, loca-tion, date, and delivery window. A is the set of allattribute vectors on current car supply, and B is the setof all attribute vectors on current orders. Sa� a ∈ A� isthe number of cars with particular attribute vector a,and Db� b ∈ B is the number of orders with particu-lar attribute vector b. � is the set of allowable car toorder pairings �a� b�, a ∈ A, b ∈ B�

The objective function, Equation (1), charges a costcab for allowable assignments � of supply a to carorders b. Flows from demand locations to the sinknode k earn a bonus (a contra-cost) bd based on cus-tomer priority for providing service. The total costsof assignments are minimized through optimal flowsxab, which is an integer variable (6). The number ofsupply units R at the source node r and the volume ofdemand K at the sink node k are set equal to the sumof all supply over the horizon, R = K = ∑

a∈A Sa. The

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Gorman et al.: CSX Railway Uses OR to Cash In on Optimized Equipment Distribution16 Interfaces 40(1), pp. 5–16, © 2010 INFORMS

flows from the source node r are equal the supply ateach supply node Sa to force equipment into the net-work at the appropriate supply nodes (2). All cars areaccounted for (3), either in an assignment to demandor no assignment (storage). Flow to a demand loca-tion to not to exceed its demand (4), and the flow intoa demand location equals the flow out (5).

Min∑

ab∈�

cabxab + ∑

a∈A

Ckxak −∑

b∈B

bdxbk (1)

subject to xra = Sa ∀a ∈ A� (2)∑

b∈B

xab + xak = Sa ∀a ∈ A��a� b� ∈ �� (3)

∑

a∈A

xab ≤ Db ∀b ∈ B� (4)

∑

a∈A

xab = xbk ∀b ∈ B� (5)

xab ≥ 0� and integer. (6)

AcknowledgementsWe would like to thank the Edelman Committee for its con-sideration of this work as an Edelman Competition final-ist, and our competition coaches, Anthony Brigandi andSudhansu Baksi, for their help in developing the paper andpresentation. We would like to thank the faculty, staff, andadministration of the University of Dayton for their sup-port during the competition. Finally, we would like to thankthe scores of dedicated CSX employees who contributed tothe original and ongoing success of DCP; in particular, wethank Dave Bell, Marty Blue, Alan Blumenfeld, KathleenBrandt, Cheryl Crow, Ellen Dear, Gene Hartley, Ken Hinson,Mary Hollin, Joe McMillan, Bob Muenz, Tim Poineau, andMickie Wittig.

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