In the construction of tank farms there is a requirement for the tanks to be hydro-tested in order to verify that they are leak proof as well as proving the lack of differential settlement in the foundations. The tanks will be required to be filled to a predetermined level and then to maintain this loaded state for a certain period of time before being drained. In areas such as the Middle East water for hydro-testing is not freely available as sea water is often not suitable for this purpose, so fresh water needs to be produced or transported to the construction site for this purpose. It is therefore of major benefit to the project to schedule the hydro-testing of the tanks in such a manner as to minimize the utilization of hydro-test water. 

This problem is a special case of the Resource Constrained Project Scheduling Problem (RCPSP) and in this research we have modified our previously developed Fitness differential adaptive genetic algorithm [4, 6 & 7] to the solution of this real world problem.

The Algorithm has been ported from the original MATLAB code into Microsoft Project using VBA in order to provide a more user friendly, practical interface

Cheng, K

Lancaster, J

Proceedings of the Institution of Mechanical Engineers Part B Journal of Engineering Manufacture

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Brunel University Research Archive

Optimization of the hydrotesting sequence intank farm construction using an adaptive geneticalgorithm with stochastic preferential logicJ Lancaster1,2* and K Cheng11Advanced Manufacturing and Enterprise Engineering, Brunel University, Uxbridge, UK2Hill International, Abu Dhabi, United Arab EmiratesThe manuscript was received on 11 October 2007 and was accepted after revision for publication on 30 October 2007.DOI: 10.1243/09544054JEM1022Abstract: In the construction of tank farms there is a requirement for the tanks to be hydro-tested in order to verify that they are leak proof as well as proving the lack of differential settle-ment in the foundations. The tanks will be required to be filled to a predetermined level andthen to maintain this loaded state for a certain period of time before being drained. In areassuch as the Middle East, water for hydrotesting is not freely available as sea water is often notsuitable for this purpose, so fresh water needs to be produced or transported to the construc-tion site for this purpose. It is therefore of major benefit to the project to schedule the hydro-testing of the tanks in such a manner as to minimize the utilization of hydrotest water.This problem is a special case of the resource-constrained project scheduling problem (RCPSP)and in this research the fitness differential adaptive genetic algorithm previously developed bythe authors has been modified to enable the solution of this real world problem. The algorithmhas been ported from the original MATLAB code into Microsoft Project using Microsoft VisualBasic for Applications in order to provide a more user friendly, practical interface.Keywords: genetic algorithm, resource-constrained project scheduling problem (RCPSP),project scheduling1 INTRODUCTIONThis paper aims to provide a solution to a special caseof the well-studied resource-constrained project sche-duling problem (RCPSP) utilizing a genetic algorithmapproach. Overviews of previous research in this areacan be found in both Lancaster and Ozbayrak [1]and Kolisch and Hartmann [2]. The unique caseconsidered in this paper is that the determination ofcertain preferential logic links will be considered aspart of the optimization process. Certain activitiesare identified that may be considered for the applica-tion of preferential logic and a stochastic process isused to apply this logic. This additional logic is thenstored as an extension to the normal genetic algorithmchromosome and is therefore refined as part of theoptimization process. The problem of hydrotesting anumber of tanks in the construction of a storage tankfarm is considered for application of this technique,as this type of preferential logic is required in orderto ascertain the sequence in which the hydrotest waterwill be transferred from tank to tank.Oil refineries and import terminals normallyrequire the construction of large tank storage farms.These farms comprise a large number of tanks ofdiffering volumes and designs. One normal require-ment on the construction of all of the tanks is thatthey be tested by partial filling with water in orderto test for leakage and differential settlement in thefoundations. Large volumes of water are requiredfor the testing of these tanks and in order to reducethe total quantity employed, water is transferred fromone tank to another, but withmany differing capacitiesplanning the sequence is complex, as to maintain acertain quantity of water within the system the totalvolume of tankage under hydrotesting at any one timewill need to be approximately consistent. The object-ive of this optimization problem is to identify the*Corresponding author: Claims and Construction Management,Hill International, PO Box 5201, Abu Dhabi, United ArabEmirates. email: JohnLancaster@Hillintl.comSHORT COMMUNICATION 367JEM1022SC  IMechE 2008 Proc. IMechE Vol. 222 Part B: J. Engineering Manufacturesequence with the minimum duration that will utilizeno more than the total available volume of water.This optimization becomes particularly importantin arid areas such as the Middle East where wateris at a premium. Sea water is often not suitable forthis testing purpose and therefore fresh water willoften need to be produced or transported to the con-struction site for this purpose at a large expense.The current paper is structured as follows: section 2looks at the additional requirements for optimiz-ing this problem over those of the standard RCPSP;section 3 discusses the structure of the current algo-rithm; section 4 outlines the test problem utilized totest the effectiveness of the algorithm; section 5 dis-cusses the results and finally section 6 presents con-clusions and suggestions for future research.2 THE HYDROTESTING PROBLEMOn first consideration it would seem that the pro-blem described would be a simple implementationof the RCPSP; the water used in hydrotesting can bemodelled as a renewable or non-renewable resourcedepending on the approach adopted, and perform-ing a levelling of this resource within certain limits,while minimizing the duration, would be a typicalRCPSP problem and would indeed to a certain pointprovide optimization of this problem. However, thevital component that would be missing when opti-mizing this problem in the manner just describedis that it would not provide, as output, a routing, orlogical sequence in which water is transferred fromone tank to another.In order to include this additional requirementinto the model, stochastic logic has been adoptedinto the problem. Items in the schedule to be con-sidered for stochastic logic are marked prior to theoptimization run. As part of the processing of eachpotential schedule, logical relationships are formedrandomly between the marked activities. This sto-chastic logic is then considered along with the exist-ing deterministic logic during the normal processingof the population of schedules.3 THE GENETIC ALGORITHMThe main construction of this present algorithm isbased on that presented in Lancaster and Cheng [3],which uses adaptation based on the fitness differ-ential between successive generations to modify themutation factor. The stochastic logic has been mod-elled as an extension to the existing chromosome,providing a position in the chromosome for each ofthe activities identified for stochastic logic.Figure 1 shows the extension of the chromosome.In the test problem a 180-activity schedule is utilizedwith 8 activities selected for stochastic logic relation-ships. The random permutation portion of the chro-mosome shown in Fig. 1 holds the sequencing of all180 activities. The extension portion shown to theright holds 8 additional alleles (the term for a singleposition within the chromosome) to map the ran-domly generated logic. The actual values containedin these 8 positions refer to the stochastic successorof that particular activity. This randomly generatedlogic is used during the schedule generation alongwith existing deterministic logic to produce the feasi-ble schedule.One consideration that needs to be made is that bypurely applying random logic generation it is neces-sary to avoid the creation of logic loops that wouldprevent the formation of a feasible schedule. Thepresent algorithm performs loop checking utilizing adepth first recursive cycle check in order to first iden-tify loops where they exist and then to select a logiclink to remove in order to break the loop.The utilization of an extension of this nature tothe normal permutation portion requires specializedcross-over and mutation operators that are capableof allowing for this functional division of the chromo-some into two portions, performing their operationson the two portions separately. In order to cater forthis a composite cross-over operator was developed.This cross-over operator consists of two compon-ents; the first component is a standard independentcross-over operator (IPX [4]), which is applied to thetraditional part of the chromosome, i.e. the allelescontaining the permutation of the activities or the‘activity list’. The second component, a two-pointcross-over is utilized on the chromosome extension.This cross-over operator is illustrated in Fig. 2.In addition to the specialized cross-over operatora complimentary mutation operator has also beendesigned. This mutation operator also comprisestwo components. The first component applied tothe main chromosome is a two-point adjacent swapmutation operator (Murata and Ishibuchi [4]) andthe second component is simply a single-bit randomFig. 1 Chromosome extension368 J Lancaster and K ChengProc. IMechE Vol. 222 Part B: J. Engineering Manufacture JEM1022SC  IMechE 2008change operator which is applied to the chromosomeextension. This second mutation operator selectsan allele from the chromosome extension at randomand then changes its value to a randomly selectedmember of the set of activities identified for stochas-tic logic application.Once the cross-over and mutation have been per-formed, a cycle checking algorithm is employed toensure that loops have not been introduced to thenetwork via the function of the genetic operators.This algorithm will also break any detected loops toensure a feasible network remains. The loops areonly broken by removing links that have been sto-chastically assigned; hence the integrity of the origi-nal network is always maintained.In order to communicate the logic into the algo-rithm an adjacency matrix is utilized. The logic linkscontained in the adjacency matrix are considered intwo sets: the first set is the deterministic logic linkswhich remain constant for the entire optimization;the second set is the stochastic logic which willchange for each chromosome considered. To managethis within the algorithm a copy of the deterministicadjacency matrix is made prior to applying the serialschedule generation scheme. The stochastic logicfor the chromosome being considered is added tothe deterministic logic and the schedule generationalgorithm is then run. Figure 3 shows a sample ofthe adjacency matrix with the addition of stochasticlogic carried out at the processing of each chromo-some; the example only shows activities from the firsttank with the hydrotest of activity of the second tank,a stochastic link between these hydrotests has beenindicated.The authors’ original fitness differential adaptivegenetic algorithm [3] has been adapted for thisproblem and has been rewritten from the originalMATLAB code into Visual Basic for Applications(VBA) within Microsoft Project in order to provide abetter platform for practical implementation of thisalgorithm.4 THE TEST PROBLEMThe test problem being utilized as an example forthis problem is a 180-activity schedule representingthe construction of an 8-tank tank farm. From theseactivities the 8 hydrotesting activities (1 for eachtank) have been selected for stochastic logic assign-ment. The application of stochastic logic to this pro-blem can result in between 1 and 8 transfer pathsfor the hydrotest water, 8 separate paths if no logicis applied, and 1 path if a continuous sequence isformed with logic being applied to all of the stochas-tic logic activities.It is the aim of this problem to utilize the app-lication of preferential logic in order to maintainthe desired level of resource utilization while alsoFig. 2 Extended cross-over operatorFig. 3 Adjacency matrix additionOptimization of the hydrotesting sequence in tank farm construction 369JEM1022SC  IMechE 2008 Proc. IMechE Vol. 222 Part B: J. Engineering Manufacturedetermining a suitable logic path from the hydrotestof one tank to that of another. The target maximumresource utilization is 140 000 units of water, for thepurpose of this problem the resource levels of otheractivities are ignored.In the typical RCPSP, activities are scheduled attheir first precedence and resource feasible time. Inthis problem only precedence feasibility is consid-ered and the variation in preferential logic is utilizedto provide the vehicle for optimization. The fitnessfunction of the problem has been altered so that thealgorithm will aim at minimizing the resource utiliza-tion. When a utilization is obtained that falls belowthe desired limit, a reward factor is applied to the fit-ness measure of that solution. Using this philosophya logic sequence will be produced that will maintainthe desired resource level. In this specific problemthis will produce a transfer path for the hydrotestwater from tank to tank that will minimize the totalwater usage.5 RESULTSThe solution provided by the algorithm producesthe Gantt chart and resource histogram that areshown in Fig. 4. The logical path for the hydrotestwater through the 8 tanks can be seen from Fig. 4.Activity 34 which requires 2 353000 units of wateris predecessor to activity 68 also requiring 2 353000units and this sequence continues through activity51 and 17. A similar chain is formed between thefour larger tanks requiring 3 360000 units. Thetwo paths that the algorithm has selected are:34–68–51–17 and 136–119–85–102. Figure 4 also showsthat the prescribed resource limits have been met bythe problem with the peak resource usage being140000 units.6 CONCLUSIONS AND DIRECTIONSFOR FUTURE RESEARCHThis paper has described a genetic algorithm optimi-zation of a project scheduling problem that utilizespreferential logic optimization in order to meetresource requirements. A practical problem hasbeen described in order to provide evaluation of theeffectiveness of the solution and the algorithm hassuccessfully provided the desired results.A specific problem faced by stochastically apply-ing preferential logic was described – the formationof cycles within the network and the solutionemployed within the algorithm for solving this issuewas also discussed. This method of optimization is aparallel of the often used manual process of resourcelevelling. By applying logic to selected activities itensures that resources are levelled in a manner thatFig. 4 Optimization of case study problem370 J Lancaster and K ChengProc. IMechE Vol. 222 Part B: J. Engineering Manufacture JEM1022SC  IMechE 2008will provide an executable schedule. Further workcan be conducted in combining this technique withthe standard RCPSP and also through multi-objectiveoptimization where duration and resource minimiza-tion are considered.REFERENCES1 Lancaster, J. and Ozbayrak, M. Evolutionary algorithmsapplied to project scheduling problems: a survey of thestate-of-the-art. Int. J. Prod. Res., 2007, 45(2), 425–450.2 Kolisch, R. and Hartmann, S. Experimental investigationof heuristics for resource constrained project scheduling:an update. Available online at http://129.187.106.231/psplib/files/KH-18–2-05.pdf, last accessed 12 October2007.3 Lancaster, J. and Cheng, K. A fitness differential adaptiveparameter controlled evolutionary algorithm with appli-cation to the design structure matrix. Int. J. Prod. Res.(in press) 10.1080/00207540701324176.4 Murata, T. and Ishibuchi, H. Performance evaluation ofgenetic algorithms for flowshop scheduling problems.In Proceedings of the First IEEE Conference on Evolu-tionary computation, 1994, 2, pp. 812–817.Optimization of the hydrotesting sequence in tank farm construction 371JEM1022SC  IMechE 2008 Proc. IMechE Vol. 222 Part B: J. Engineering Manufacture

A fitness differential adaptive parameter controlled evolutionary algorithm with application to the design structure matrix.

Evolutionary algorithms applied to project scheduling problems: a survey of the state-of-the-art.

K o l i s c h ,R

Performance evaluation of genetic algorithms for flowshop scheduling problems.

Optimisation of the hydrotesting sequence in tank farm construction using an adaptive genetic algorithm with stochastic preferential logic

Optimisation of the hydrotesting sequence in tank farm construction using an adaptive genetic algorithm with stochastic preferential logic

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