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|Title:||Decision support for integrated refinery supply chains. Part 1. Dynamic simulation||Authors:||Pitty, S.S.
|Issue Date:||24-Nov-2008||Citation:||Pitty, S.S., Li, W., Adhitya, A., Srinivasan, R., Karimi, I.A. (2008-11-24). Decision support for integrated refinery supply chains. Part 1. Dynamic simulation. Computers and Chemical Engineering 32 (11) : 2767-2786. ScholarBank@NUS Repository. https://doi.org/10.1016/j.compchemeng.2007.11.006||Abstract:||Supply chain studies are increasingly given top priority in enterprise-wide management. Present-day supply chains involve numerous, heterogeneous, geographically distributed entities with varying dynamics, uncertainties, and complexity. The performance of a supply chain relies on the quality of a multitude of design and operational decisions made by the various entities. In this two-part paper, we demonstrate that a dynamic model of an integrated supply chain can serve as a valuable quantitative tool that aids in such decision-making. In this Part 1, we present a dynamic model of an integrated refinery supply chain. The model explicitly considers the various supply chain activities such as crude oil supply and transportation, along with intra-refinery supply chain activities such as procurement planning, scheduling, and operations management. Discrete supply chain activities are integrated along with continuous production through bridging procurement, production, and demand management activities. Stochastic variations in transportation, yields, prices, and operational problems are considered in the proposed model. The economics of the refinery supply chain includes consideration of different crude slates, product prices, operation costs, transportation, etc. The proposed model has been implemented as a dynamic simulator, called Integrated Refinery In-Silico (IRIS). IRIS allows the user the flexibility to modify not only parameters, but also replace different policies and decision-making algorithms in a plug-and-play manner. It thus allows the user to simulate and analyze different policies, configurations, uncertainties, etc., through an easy-to-use graphical interface. The capabilities of IRIS for strategic and tactical decision support are illustrated using several case studies. © 2007 Elsevier Ltd. All rights reserved.||Source Title:||Computers and Chemical Engineering||URI:||http://scholarbank.nus.edu.sg/handle/10635/51769||ISSN:||00981354||DOI:||10.1016/j.compchemeng.2007.11.006|
|Appears in Collections:||Staff Publications|
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