Investigation of the bullwhip effect in the LNG supply chain and evaluation of mitigation methods

Βαφειάδης, Ιωάννης; Vafeiadis, Ioannis

dc.contributor.author	Βαφειάδης, Ιωάννης	el
dc.contributor.author	Vafeiadis, Ioannis	en
dc.date.accessioned	2020-11-02T17:48:31Z
dc.date.available	2020-11-02T17:48:31Z
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/51756
dc.identifier.uri	http://dx.doi.org/10.26240/heal.ntua.19454
dc.rights	Default License
dc.subject	Διαταραχή παραγγελιών	el
dc.subject	ΥΦΑ	el
dc.subject	Εφοδιαστική αλυσίδα	el
dc.subject	Νευρωνικά δίκτυα	el
dc.subject	Τεχνητή νοημοσύνη	el
dc.subject	Ρεβυθούσσα	el
dc.subject	Bullwhip effect	en
dc.subject	Supply chain	en
dc.subject	LNG	en
dc.subject	Neural networks
dc.subject	Artificial intelligence	en
dc.subject	Revithoussa	en
dc.title	Investigation of the bullwhip effect in the LNG supply chain and evaluation of mitigation methods	en
dc.title	Μελέτη του φαινομένου διαταραχής παραγγελιών και αξιολόγηση λύσεων για την άμβλυνση του	el
heal.type	bachelorThesis
heal.classification	Επιχειρησιακή Έρευνα	el
heal.language	en
heal.access	free
heal.recordProvider	ntua	el
heal.publicationDate	2020-09-30
heal.abstract	The scope of this thesis was the development of a methodology able to examine the scale of the bullwhip effect in the LNG supply chain, estimate how an LNG Terminal influences the network, and evaluate solutions for the mitigation of the effect. For this purpose, an artificial neural network was designed that can simulate various natural gas demand scenarios and create reliable output. The data used to train the network was daily usage information from the Transmission System Operator, from whose side the optimization was pursued. Specifically, a thorough examination of the LNG market and its prospects for the near future are presented, both at a global and national level. Furthermore, the bullwhip effect is analyzed, initially in the light of operational research and then in particular for the LNG supply chain according to existing research. Afterward, the design of the neural network follows, where its operation and parametrization are explained. The model receives as inputs the total demand of natural gas for a specific day, the demand for electricity production, the loaded capacity of the LNG Terminal of Revithoussa, the LNG imports, the pipeline feed gas imports, and other parameters related to the price of the commodity and returns an estimation for the bullwhip effect for a period of thirty days. This model can be used as a guide for the efficient operation of the natural gas transmission system, as it is able to give a prediction about the behavior of the supply chain according to changes in the import policy or the capacity of the terminal. This way, the mitigation of the demand amplification in the LNG supply chain can be achieved and also, the results can indicate a path for the viability of the system in the upcoming increase in demand for natural gas and electricity production the next years.	en
heal.advisorName	Λυρίδης, Δημήτριος	el
heal.committeeMemberName	Λυρίδης, Δημήτριος	el
heal.committeeMemberName	Βεντίκος, Νικόλαος	el
heal.committeeMemberName	Τόλης, Αθανάσιος	el
heal.academicPublisher	Εθνικό Μετσόβιο Πολυτεχνείο. Σχολή Ναυπηγών Μηχανολόγων Μηχανικών. Τομέας Μελέτης Πλοίου και Θαλάσσιων Μεταφορών	el
heal.academicPublisherID	ntua
heal.numberOfPages	107 σ.	el
heal.fullTextAvailability	false