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|Title:||Research on optimising chemical / petro - chemical logistics||Authors:||Ten, Zhi Yong||Keywords:||DRNTU::Engineering::Maritime studies::Maritime management and business||Issue Date:||2015||Abstract:||The petrochemical supply chain is a series of capital intensive stages which spans from refineries to end consumers. Due to the substantial investments involved, much research has been conducted in an attempt to optimise the supply chain. Maritime transportation is one of the key transportation mode adopted within the supply chain and thus, optimising this particular segment of the chain will have far reaching effects on downstream activities. In lieu with the importance of the maritime sector, this paper presents two optimisation models that specifically targets shipowning entities. The models aim to allow shipowners to make informed decisions regarding where to deploy their vessels in order to maximise their revenue and minimise costs. Solutions generated by the revenue model filters the top income earning port pairings and a balance is struck between freight rate and cargo quantity available for transport. For the cost model, it focuses on vessel size optimisation where the model seeks to find the most cost effective vessel size for a specific trade pairing. It is then contextualised in the form of a company’s fleet where the solution will provide results which shows how many vessels of which size has to be deployed on which trade pairing in order to minimise total cost per tonne-mile of a company’s fleet. In addition to the two models, a qualitative analysis is used to supplement both models by evaluating other factors which are not quantifiable. In this case, the multi-berth problem which is a pertinent issue in the parcel tanker industry is discussed. The paper then provides two suggestions namely vertical integration and the formation of shipping alliances as proposed methods of alleviating the situation. The models were solved using two linear programming software. The revenue model is solved using the Microsoft Excel Solver add-in while the cost model is solved using the Premium Solver Platform due to the inability of Microsoft Excel Solver in handling the sheer number of variables involved. The results from the revenue model shows that it is capable of filtering from the initial 49 port pairings to just 31. At the same time, it provides solutions to show the amount of cargoes the company shall aim to carry in order to achieve the maximum revenue. Prior to solving for the cost model, it was found that the analysis of the cost per tonne-mile data affirms the current trend of having larger vessels due to economies of scale. Results from the cost model also shows that the model is capable of further filtering the port pairings from 31 in the revenue model results to just 10. Therefore, in combination, the models screened off about 80% of the initial 49 port pairings. This will allow shipowners to focus on more profitable trade routes.||URI:||http://hdl.handle.net/10356/64099||Rights:||Nanyang Technological University||Fulltext Permission:||restricted||Fulltext Availability:||With Fulltext|
|Appears in Collections:||CEE Student Reports (FYP/IA/PA/PI)|
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