Show simple item record

dc.contributor.authorGarcia-Flores, Rodolfoen
dc.contributor.authorBanerjee, Soumyaen
dc.contributor.authorMathews, Georgeen
dc.contributor.editorFerreira Tiryaki, Gen
dc.contributor.editorMota dos Santos, ALen
dc.date.accessioned2020-10-06T11:34:26Z
dc.date.available2020-10-06T11:34:26Z
dc.date.issued2017-03en
dc.identifier.isbn978-1-53610-792-0en
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/311135
dc.description.abstractWhen stakeholders commit to building infrastructure as part of strategic, long-term planning, the final facilities are not normally amenable to modification after completion. A consequence of this is that users are forced to operate within the original specifications for, at least, as long as it takes to carry out major refurbishments or retrofitting, and even then, the constraints imposed by the original layout may be inescapable. On one hand, the original infrastructure plans enhance (or limit) the users' ability to operate efficiently for years to come. As time passes and the payback period approaches, changing operating conditions and unforeseen bottlenecks in the original blueprint can, at best, affect the economic returns and, at worst, defeat the purpose of the whole project (see, for example, Castellon airport in Spain, which was built but is grossly underutilised), producing unanticipated economical, social and political repercussions. On the other hand, managers and operators (that is, those living with the consequences of the strategic planning) have some leeway to compensate for miscalculations by means of their tactical and operational planning. In this chapter, we explore the use of quantitative techniques to, first, amend bottlenecks and uncertain market and operating conditions that affect the performance of infrastructure investments (the tactic and operational levels), and second, validate the effectiveness of the original infrastructure design (the strategic level) under these changing conditions. More specifically, we present a rail scheduling case study where we combine demand forecasting using Machine Learning techniques and formal Operations Research methods to assess and maximise the value of already-existing infrastructure. Rail scheduling is a typical optimisation problem popular in the literature, but its potential value is bounded not only by its technical properties and specifications (how good the algorithm is) but also by the accuracy of data feeding the algorithm. Such data is critical in specifying the demand that a facility will experience in the future, and the costs that will be incurred to operate it. The use of intensive data analytics and appropriate Machine Learning techniques can resolve this and provide a substantial competitive edge for investors and operators of rail inter-modal terminals. We anticipate that Machine Learning algorithms that predict future demand, coupled with optimisation techniques that streamline operations of facilities, can be integrated to create tools that help policy makers and terminal operators maximise the value of their current infrastructure, while meeting ever-changing demand.en
dc.publisherNova Publishersen
dc.rightsAll rights reserved
dc.rights.uri
dc.titleUsing Optimisation and Machine Learning to Validate the Value of Infrastructure Investmentsen
dc.typeBook chapter
prism.number7en
prism.publicationDate2017en
prism.publicationNameInfrastructure Investments: Politics, Barriers and Economic Consequencesen
dc.identifier.doi10.17863/CAM.58223
rioxxterms.versionAM
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserveden
rioxxterms.licenseref.startdate2017-03en
dc.contributor.orcidBanerjee, Soumya [0000-0001-7748-9885]
rioxxterms.typeBook chapteren
cam.issuedOnline2017-04-03en
dc.identifier.urlhttps://novapublishers.com/shop/infrastructure-investments-politics-barriers-and-economic-consequences/en
rioxxterms.freetoread.startdate2018-04-03


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record