Estimating HighwayConstruction ProductionRates during Design:Elements of a UsefulEstimation ToolWAI KIONG CHONG, M.ASCE; SANG-HOON LEE, M.ASCE; AND

JAMES T. O’CONNOR, M.ASCE

ABSTRACT: Construction scheduling for highway projects is an important process dur-ing the design stage. Numerous research studies have attempted to apply new techniquesto improve the accuracy of construction scheduling. Many of these studies, however,failed to address the practicality of the scheduling methods and the needs of highwaydesigners. The authors conducted literature reviews, surveys, and interviews to study thechallenges designers face in estimating production rates for highway construction. Wefound that estimation tools for production rates should be flexible, user friendly, andefficient yet comprehensive. Data should be collected from reliable sources and analyzedappropriately and efficiently before being applied to a production rate tool. This studysuggested that combining designers’ experience and reliable tools is the most effectiveway to develop realistic production rates for highway construction scheduling.

Construction schedule development isa critical process during the designphase of a highway constructionproject. Under- or overestimation ofa project schedule can cripple thesmooth progress of a project; a tight

schedule may increase the chances of excessive claimsand delays, whereas a loose schedule may causeproject idling during construction, increasing thechances of material and equipment damage bybad weather and safety hazards for pedestrians anddrivers.

Designers do not have control over many productiv-ity variables, such as the means and methods of con-struction and the productivity of equipment andlabor. Instead, they take the “big picture” approach,seeking an ideal schedule, not an exact one. Theyaim to develop a logical and reliable schedule that isbased on the limitations normally faced in highwayconstruction projects. The contractor’s schedule is nor-mally more thorough and detailed than the designer’s,as contractors need total control in every constructionprocess. As a result, the designer’s approach to schedul-ing can be very different from the contractor’s approach.

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The scheduling process in the design phase involvesthree key activities: (1) A production rate is calculatedfor individual work items, (2) the production ratesfrom these work items are consolidated and lead–lag relationships are developed among them, and(3) a final schedule is developed using the Gantt chartor critical path method (CPM). Many departments oftransportation (DOTs) do not have sufficient datato apply computerized and complicated statisticalmodels to improve the reliability of their estimates,and designers typically develop estimates within theboundaries of their knowledge of construction andthe information they readily possess.

The purpose of the research described in this paperwas to improve the accuracy of production rate esti-mation for critical highway work items (i.e., the firstactivity mentioned above). This paper examines howdesigners can improve their production rate estimatesgiven the boundaries they face. It examines statistical,computerized, and factor analysis models and thequality of the data sources and its impact on the reli-ability of the scheduling estimates. Interviews and sur-veys were conducted to study the requirements for agood scheduling tool at the highway design phase.This research can help schedulers develop a betterunderstanding of the parameters that affect work itemproductivity and that should be considered in calcu-lating the production rates.

SCHEDULING PRACTICES FOR HIGHWAYCONSTRUCTION DESIGN

Productivity factors occurring on “normal” and “ab-normal” production days may be different (AbdulMajid and McCaffer 1998). Designers seek to estimatea normal production day, as an abnormal productionday includes variables that cannot be controlled, suchas rain heavy enough to prevent excavation work, aroad accident, or unforeseen utility conflicts or soilconditions. Excusable delays occur only on abnormaldays, and contractors are normally allowed to claimtime extensions for these delays. As such, productivityfactors on normal and abnormal days are separated,and designers base their estimates on a normal produc-tion day.

One of the most common scheduling practices dur-ing the design phase is to break down constructionwork into constituent operations and estimate each op-eration individually. There is no standard way ofbreaking down the operations; it depends on howdetailed the designers wish to be. Breaking down each

work operation into smaller pieces for estimatingpurposes may increase schedule reliability; however,limited knowledge of and control over operationsmay prevent designers from doing so. Designersmay not have the luxury of time and information toestimate their schedule in such detail. Most designersbreak operations down into a size that is manageableenough to increase the schedule’s reliability withoutinvolving too many variables. Hancher et al.(1992) found that the common practice among design-ers was to break down their production rates intowork items, each of which has its own productivityvariables.

Schedule estimates are then based on critical workitems on a project. CPM involves identifying the criti-cal path, which consists of work items that take thelongest overall time to complete on a project and thusnormally extend over the project completion time. Ac-tivities that fall on the critical path are what designersneed to schedule for. These activities normally includework items with the highest quantities or those thatrequire a lot of lead time. A typical highway projectincludes more than 100 work items. Given the tightschedule in most projects, designers do not have theluxury of estimating every work item. They need toidentify and concentrate only on the critical workitems in their projects.

Highway construction time estimation methodsadopted by different states, agencies, and districtscan be significantly different from one another.The Texas DOT, for example, adopted the Construc-tion Time Determination System (CTDS; Hancheret al. 1992), and the New Jersey DOT adopted theCapital Program Construction Scheduling Codingand Procedures for Designers and ContractorsManual. These scheduling methods are quite different.For the New Jersey DOT, the production rate for acast-in-place (CIP) retaining wall for Type 1 bridgeconstruction is 20 days/30 meters, given the followingassumptions: (1) 8-hour working day per crew,(2) 50% added for one bridge and 25% added fortwo, (3) separate estimates for bridges built in differ-ent stages, (4) additional 30 days required for bridgesbuilt over water, and (5) inclusion of concrete curingtime. In contrast, the Texas CTDS production rate forretaining wall construction is 9.3 to 18:5 m2 per day,and soil condition is the only adjustment needed.Thus, the two methods differ in units (area versuslength), productivity factors, and design issues. Inboth systems, however, designers’ experiences arecritical in their decisions regarding the ideal produc-tion rate based on the range given.

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The Transportation Research Board conducted aseries of studies between 1981 and 1995 to investigateand develop systems to improve the reliability of con-tract time estimation of highway construction projects(National Cooperative Highway Research Program1981; Herbsman and Ellis 1995). In addition,Hancher et al. (1992) found in a survey of employeesin 36 DOTs that 44% of the respondents relied onpersonal experience to estimate production rates,30% used standard production rates that were usuallyprovided by the DOTs, and 22% used productionrates from historical records of previously completedprojects from other sources. Data used to develop thesesystems were normally collected either from survey re-turns or documentation from projects. Some DOTsand several contractors had simplified their productionrate estimates into three values—mean, optimistic,and pessimistic values for each work item—anddesigners determined a realistic rate from these valuesbased on their experience.

PRODUCTIVITY FACTORSIn production scheduling, productivity factors areused to establish relationships between fluctuatingproduction rates and production work items. Design-ers must identify relevant productivity variables theycan rely on to improve their estimates.

Differing geological locations, site conditions, andother location variables affect work productivity. Con-structing a drilled shaft on dry soil may be faster thanconstructing one nearer a riverbank, assuming the samecrew is used in both operations. Constructing a road ona mountain may be much slower than constructing oneon flat land. A sudden change in the weather can dis-rupt or delay construction operation. Constructionwork has to stop during snowstorms in the warmer re-gions (e.g., Florida and Texas) but can continue in thecolder regions (e.g., Maine and Washington). Ruralhighway projects face fewer traffic problems than high-way projects in metropolitan areas, which face frequenttraffic congestion, strict environmental regulations,risky traffic safety, and right-of-way issues that reduceconstruction productivity. Rural highway construc-tion, however, may face difficulties in acquiring skilledworkers and highly productive equipment, drivingdown productivity (Koehn and Ahmed 2001). Thus,the location of the project is not a dependable produc-tivity factor; in a work zone located in a metropolitanarea that has ample work space, highway congestionmay affect only material delivery and may have mini-mal impact on work operations.

Various productivity factors have different impactson materials and components. For example, off-sitefabricated precast concrete has less productivity vari-ability during installation but a higher chance ofbeing delayed during transport, whereas CIP concretehas a higher chance of being delayed by poor weatherconditions. Other productivity factors include sizeof equipment (Bhurisith and Touran 2002; Sawhneyand Mund 2002), controllability of soil condition(Allouche et al. 2001), weather conditions, and lossesfrom learning (Thomas et al. 1999; El-Rayes &Moselhi 2001). For example, it makes sense to linkthe productivity of foundation, pipeline, and retainingwall construction to soil conditions. Alternatively, thelearning curve and poor weather conditions have agreater effect on highly repetitive work items suchas pavement, multiple drilled shafts, and hot-mix as-phalt pour. Thomas et al. (1999) found that changingweather, erroneous work, poorly coordinated materialdelivery, and frequent equipment relocation weremore disruptive to long and continuous productionprocesses than to less repetitive work items. Stoppageto a process slows down work momentum and leads toproductivity losses of other work, which Thomas et al.(1999) described as the “ripple effect.” However, be-cause a highway designer cannot accurately predict thechances of having disruptions during construction,such disruptions are normally excluded from theschedule at the design phase.

Design can significantly affect work productivity(Poh and Chen 1998). Constructability has beenshown to increase site productivity, and site conges-tion can be avoided with designs that use smallerequipment.

Site condition would normally be considered aproductivity factor. Construction on mountainousregions, in tight work spaces, in extreme cold and heat,on rough terrain, on congested work zones, and in closeproximity to adjacent structures would normally slowdown workers’ productivity (Koehn and Brown 1985).

Wideman (1994) showed that the productivity ofworkers varied during different phases of construction.His study found that workers’ productivity was slowduring the early phase of construction and slowly spedup as construction progressed. Productivity continuedto rise and plateau between 25% and 75% of projectcompletion, and then fell until project completion. Heattributed the initial growth to workers’ learningeffect and the fall nearing project completion to re-duced work amount. Thus, worker productivityshould not be treated as uniform throughout theconstruction phase.

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Most research has found that work productivity isaffected by more than one productivity factor. For ex-ample, our literature review indicated that foundationconstruction is affected by many factors, including soiltype, drill type (size, type, and construction method),angle of swing, methods of spoil soil removal, pile axisadjustment, depth and size of holes, equipmentpower, operator efficiency, weather conditions, spoilsoil removal and space availability, rebar cage instal-lation procedures, concrete pouring methods, machineavailability, job and management conditions, drillingtime activity, other time activities, change orders, andweather (Zayed and Halpin 2004; Hanna et al. 2002;El-Rayes and Moselhi 2001). Furthermore, Thomaset al. (1989) emphasized that productivity factorsshould be divided into within-project, between-project, and regional drivers. Such divisions allowdesigners to better identify and allocate significantproductivity drivers for different work zone scenarios.

PRODUCTION RATE DOCUMENTATION ANDANALYSIS

Developing reliable productivity data documentationand analysis processes is critical to the development ofa dependable scheduling tool. Data accuracy andcorrect analysis are the two critical elements of anysuccessful and reliable information system. Collectedproductivity data should support the following func-tions: (1) identify significant productivity factors,(2) examine and establish the relationships betweenfactors and productivity, (3) develop production ratemodels for scheduling, and (4) study productivityimprovement methods. Efficiency of data collectionis also critical, though there is a need to balance effi-ciency and reliability. Survey forms and existing pro-ductivity databases are the two most common datacollection methods. Hancher et al.’s (1992) surveyof experienced designers and site personnel in 36DOTs indicated that many contractors kept and de-veloped their own production factor and rate informa-tion. They gathered most of their productivityinformation from daily log books, payment and sched-ule documentation, record books, and informationstored on computers. Some contractors required theirstaffs to input information systematically and keptcomprehensive records of all their projects. No proce-dures or standards have been developed for suchrecording processes; thus, the reliability of the infor-mation may vary greatly across organizations. Like anyresearch using the survey approach, the reliability ofHancher et al.’s findings cannot be verified, and most

of the data are based on personal opinion. Althoughcontractors may collect huge amounts of data fromexisting records, we question the reliability and use-fulness of these data if they are not being recorded forproduction rate estimation purposes.

Regression analysis, factor analysis models,Monte Carlo simulation, fuzzy logic, schedule algo-rithms, and neural networks have been applied toscheduling (AbouRizk and Wales 1997; Adeli andKarim 1997; Ben-Haim and Laufer 1998; Jiangand Shi 2005; Lee 2005; Lee and Arditi 2006). Severalattempts have been made to integrate some of thesetechniques with standard scheduling software, suchas Primavera, Microsoft Project, and SureTrak. Alter-natively, many designers continue to develop projectschedules using their own experience. Advancedscheduling techniques may improve a schedule esti-mate’s accuracy, but many departments of transporta-tion lack the required infrastructure and informationto use these techniques. In addition, scheduling toolsdeveloped in the past for specific purposes became out-dated very quickly. Texas DOT’s CTDS (Hancher et al.1992), for example, became obsolete simply becausethere was no way to update the information in the sys-tem. Thus, any new scheduling tool should supportpopular scheduling software, such as Primavera andMicrosoft Project.

Lessons learned from the past have shown thatschedulers tend to resist a complicated informationtechnology system and prefer a flexible system. Be-cause most designers use the critical path and Ganttchart methods to schedule their projects, Primaveraand Microsoft Projects are the most frequently usedscheduling programs. Therefore, production rate esti-mation tools should not deviate too much from thesetechniques and software. Production rate informationthat is easily integrated with the software and tech-niques will allow designers to spend less time learningnew techniques and software and more time improv-ing the reliability of their estimates. Consequently,any production rate system should be either com-pletely independent from or well integrated intoexisting software and estimation techniques.

Researchers have successfully applied many tech-niques to improve the reliability of productionrate estimates. Some of these techniques include re-gression analysis and models, factor analysis models,Monte Carlo simulation, and neural networks. Other,more simplified methods include summing up col-lected production rates and averaging them into mean,optimistic, and pessimistic values (Hancher et al.1992). The techniques an organization adopts depend

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heavily on the types of obtainable and available infor-mation within the organization, the predictability ofthe productivity factors, and the details and accuracyrequirements of the production rate estimates. Neuralnetwork application to estimation requires an exten-sive amount of information to develop the network tothe point where the network would self-learn and cor-rect itself.

Lu and AbouRizk (2000) supplemented PERT(Program Evaluation and Review Technique) withstatistical and simulation techniques to develop asix-value estimation technique: minimum duration(optimistic), maximum duration (pessimistic), mean,standard deviation, confidence interval, and probabil-ity. This technique allows estimators to better under-stand the risks and confidence of their estimates andeliminates the need to guess. Regression analysis is oneof the most common methods applied to examine andquantify the relationships between productivity fac-tors and rates (Koehn and Brown 1985; Sandersand Thomas 1993; Hanna et al. 2002). Regressionanalysis is useful to illustrate the continuous relation-ships between numeric productivity factors andrates. However, it cannot be used to develop relation-ships between nonnumeric (categorical) productivityfactors and rates. For such factors, Hancher et al.’s(1992) method is the most common approach; thecontractor determines a productivity rate under nor-mal conditions (normally, a mean or median) andtwo rates for extreme conditions (normally, optimisticand pessimistic). Other statistical techniques are avail-able to handle nonnumeric factors (categorical factors),such as box plots and longitudinal data analysis tech-niques (commonly used in social science research). Themore advanced techniques require special treatment ofdata, like creating artificial variables and splitting datainto different components.

Peña-Mora and Li (2001) proposed using theGERT (Graphical Evaluation and Review Technique)diagramming scheme to calculate the probability ofproject duration by measuring the variability of differ-ent branches and loops of each construction activity,their relationships, and overlaps with other nonrele-vant activities. They claimed that such a scheme couldbetter control or even eliminate variability within aschedule. Park and Peña-Mora (2004) proposed usingreliability control to refine activity buffers and appliedsimulation to measure and reduce buffer variabilitybetween activities. However, designers may not havesufficient control and may lack the ability to predictbuffer variability between activities during design,and they cannot feasibly apply both techniques at

the same time. Also, these techniques are applicableonly for measuring relationships between activitiesand cannot be used to improve the reliability ofproduction rates.

Many researchers have applied dynamic and sto-chastic approaches and developed simulation modelsthat integrate the construction process network usingactivities at the project level to develop productionrate models that can improve the reliability of dura-tion variability estimates of and between activities.AbouRizk and Wales (1997) illustrated that such aproject simulation model should consist of three com-ponents: (1) a project network that maintains schedulelogic, (2) a stochastic and random particles model thatgenerates uncertain factors, and (3) a productivitymodel that relates uncertainty of productivity factorsto generated project conditions. Each activity could besimulated individually and combined at a later stage(discrete-event continuous simulation). Discreteevents could be combined for simultaneous and con-tinuous simulation. Their models relied on historicaldata such as weather data from meteorological agen-cies. Construction processes are broken down intoindividual activities based on their relationships withvarious productivity factors. The effect of a productiv-ity factor on an individual activity is measured by thesimulation process and is later combined into aschedule.

Dzeng and Tommelein (1997) suggested breakingdown construction projects into “cases” and automat-ing the duration estimating process for each case, andthey found that their schedule estimates were more ac-curate. The proposed application of neural networks toconstruction scheduling (Adeli and Karim 1997),although seeming to help improve the reliability ofconstruction scheduling through continuous knowl-edge learning, has limited application at the designstage, especially on improving the reliability ofproduction rate estimates. Adeli and Karim (1997)stated that neural dynamic models require breakdownof work into tasks, crews, and segments, and logics andconstraints between repetitive tasks have to be devel-oped while each task is simulated individually. Exces-sive amounts of data are required to ascertain theusefulness of neural dynamic models since the accuracyof the final estimates heavily depends on inputs to thesystem. Such requirements of massive and continuousinformation inputs make the neural network ineffi-cient and impractical to be applied at the design phase.

Bonnal et al. (2004) believed that fuzzy logic hadbecome sufficiently mature to be applied to projectscheduling in real life. They claimed that fuzzy logic

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could eliminate calculation imprecision, narrow theprobability of estimates, and improve the plausibilityand credibility of calculated values. In most produc-tion rate estimates at the design phase, uncertaintiesduring production cannot be predicted, and thusdesigners cannot include them in the schedule. Fuzzyand probabilistic statistical techniques can beextremely complicated. The need to create new vari-ables (including dummy ones) to simulate uncertain-ties of individual activities and between them(Vanhoucke 2006; Fan and Tserng 2006) requires de-signers to look at different ways of treating their data.In real life, these variables do not exist, so it may bedifficult for designers to understand and appreciatetheir meanings and purposes. These techniques alsorequire extensive algorithms to estimate other resourceconstraints (Jiang and Shi 2005). Algorithms areneeded to clarify relationships between activitiesand allow schedulers to better control variability be-tween activities. Refinement and reliability of theschedules could be enhanced with algorithms.

Construction schedules are dynamic and thus needto be updated frequently to reflect changes duringconstruction. El-Shahhat et al. (1995) found that be-tween 29% and 67% of all construction errors oc-curred at the design phase, while only 12% to 59%of errors occurred at the construction phase. Errorscommitted at the design phase may be magnifiedat the construction phase and cause problems duringconstruction operations. Thus, designers cannot relytoo much on their personal experience.

A REVIEW OF SCHEDULE ESTIMATIONPRACTICES AND NEEDS

A survey conducted by the Texas Department ofTransportation found that scheduling practices variedacross districts and regions (O’Connor et al. 2005).Most of the practices were driven by the needs ofthe districts, the top management, and the demandsof the engineers in those districts. Some districts evendeveloped their own system to handle their uniqueneeds and situations. Each district had authority overits own scheduling methods, and employees weregiven the option to adopt anything that suited them.Scheduling software used in the Texas DOT includedin-house systems like Primavera, Microsoft Project,and SureTrak. The type of software used dependedheavily on the preferences of the employees. Severaldistricts did not use the Texas DOT’s standard system(i.e., CTDS), as they found that it was inflexible andthat the information was not accurate. They preferred

the flexibility of using any software they chose. Manydistricts preferred user-friendly and flexible systemsthat could easily integrate into Primavera, MicrosoftProject, and SureTrak. Indeed, most highlighted thetendencies to use CPM and/or Gantt charts for sched-uling. They preferred to carry out production ratecalculation manually rather than using the software.

Many respondents suggested they would prefer asystem that would integrate with Microsoft Excel.They highlighted that because some of the projectswere small, manual calculation helped speed up theestimation process. The feedback also highlighted thataccurate production rate information was needed thatreflected and represented the situations and conditionsin different districts and regions, like calendar day andworking day contracts, rural areas, soil conditions, andregional profiles. Many designers highlighted that theunits adopted should be in line with the pay units, asthis would reduce their work to convert the unitsbetween schedule and payment.

We selected eight Texas DOT designers for inter-view with regard to their needs for a better schedulingtool. Their feedback included the following sevenpoints:

1. They hoped to separate the production rates andscheduling tools from their scheduling softwareto make the system more flexible and adaptiveto many situations.

2. They wanted more realistic rates that reflectedactual site conditions. Many of their rates wereunrealistic and were not collected from the site.

3. They wanted a system that was user friendlyand did not hamper the contributions of thedesigners. The designers needed to have morecontrol over the rates and to be able to adjustthe rates accordingly so that they would notbe forced to accept unrealistic rates.

4. They cited the need to know productivity fac-tors at the design stage and to exclude factorsthat cannot be predicted at the design stage.

5. Most agreed that statistical tools can help im-prove the accuracy of their schedules, but theypreferred these tools to be relatively simple andpractical. They noted that complicated statisti-cal and simulation techniques make any toolunfriendly and yield information that may bedifficult to interpret.

6. Because it is impossible to establish good rela-tionships among the many productivity factorsfor any work item, they suggested that a systemshould not be restrictive in establishing theserelationships.

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7. The most popular scheduling programs werePrimavera and Microsoft Project, and the mostpopular techniques were Gantt chart and CPM.They did not believe new software was needed.

RECOMMENDATIONS FOR A PRACTICALSCHEDULING TOOL

We identified the following six key components of aproduction rate estimation system:

1. Data collection methodology,2. Data arrangement,3. Data analysis methodology,4. Critical work items analysis,5. Data recall and information output system, and6. Factor and driver analysis.

Integrating these components into current scheduleestimation practices is critical; they are consistent withexisting good schedule estimating practices. In addi-tion, several useful schedule estimation techniquesare available to estimate and analyze productionrates, such as statistical analysis (normally regression),neural networking, Monte Carlo simulation, and lin-ear programming.

Most designers surveyed for this research conductedtheir scheduling using the CPM and/or Gantt charts.They often relied on Primavera, MS Project, or Sure-Trak, so any scheduling tools should be either inte-grated into or separate from this software. Thedesigners wanted to retain the functions of CPMand Gantt charts but needed more reliable and usefulinformation on production rates and production vari-ability to develop better estimates. Thus, they werelooking for reliable and useful production rate andvariability estimation tools that could easily be inte-grated into or applied to existing CPM or Ganttcharts. In addition, because it is impossible to collectsufficient data to establish good statistical models forevery factor, designers need some freedom to use theirexperience to adjust the production rates provided bythese tools in order to estimate more realistic rates.Relying solely on experience can make productionrates less reliable and unrealistic, as research has shownthat human memory distorts information easily; like-wise, scheduling tools alone cannot replace experience.Combining experience with reliable scheduling toolsis the solution to more reliable construction projectscheduling.

Scheduling tools should apply only the less compli-cated statistical and simulation techniques. It is impos-sible to collect sufficient and reliable information to

comply with the requirements of the more advancedtechniques. The designers found that many valuesgenerated by these techniques did not mean anythingto them and noted that schedules do not need to beaccurate, just realistic and reliable. The more advancedstatistical and simulation techniques help improve theaccuracy of models; however, most project schedules donot require this kind of accuracy.

It is also important to ensure that data are collectedefficiently and reliably. The sources of data are critical,and sources need to be properly selected and checkedbefore the data are used to develop scheduling tools.Although breaking down work processes into greaterdetail can help improve the accuracy of estimates, it isimpractical to expect designers to do this. Designersdo not have control over the actual work processes, andscheduling is one of the many duties they have to do.Separating the production rates by work items is amore efficient way for designers to break down workprocesses. In addition, factors have to be properly de-scribed, meaningful to the designers, and foreseeableat the design stage. The literature reviews also foundthat productivity estimation tools may present therates in many ways. The designers preferred to havea tool that presents flexible values or even ranges ofvalues that they could choose from and would allowthem to adjust according to their own logic. In short,they demanded controllability, applicability, and flex-ibility in the estimation tool.

Finally, productivity factors should be categorizedappropriately and integrated when several factors haveto be considered. Historical information withoutproper appraisal may not reflect the actual conditionswhen they were collected. Historical information onpipeline production, for example, can document ratesas low as 10 meters per day or as high as 400 metersper day. Users of such information should have accessto reasons, factors, or models to use in decidingwhether their rates should be 10 or 400. Relyingon personal experience to decide a rate will naturallyincorporate some psychological effects.

CONCLUSION

Literature reviews, surveys, and interviews have high-lighted important factors to be considered in design-ing scheduling tools. The results also highlightedseveral important requirements by practitioners thatpast researchers failed to appreciate. The industryneeds a practical tool to support designers and inte-grate their experiences, and this research can be usedto inform future work to develop such a tool.

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Wai Kiong Chong is assistant professor, De-partment of Civil, Environmental and Architec-tural Engineering, University of Kansas, 2150Learned Hall, 1530 West 15th St., Rm. 2134-C,Lawrence, KS 66049. He can be reached atoswald@ku.edu.

Sang-Hoon Lee is assistant professor, Depart-ment of Engineering Technology, University ofHouston, Houston, TX.

James T. O’Connor is C. T. Wells Professor,Department of Civil, Architectural and Environ-mental Engineering, University of Texas–Austin,Austin, TX. LME

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