Robust scheduling for multi-product pipelines under demand uncertainty

Data uncertainty is an inherent component of many real-world problems, and ignoring the uncertainty makes the proposed programs impractical or inefficient. Robust optimization, regarding the data uncertainty, provides a program that remains feasible for almost all situations. This paper studies the...

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Bibliographic Details
Published in:International journal of advanced manufacturing technology Vol. 87; no. 9-12; pp. 2541 - 2549
Main Authors: Moradi, S., MirHassani, S. A.
Format: Journal Article
Language:English
Published: London Springer London 01.12.2016
Springer Nature B.V
Subjects:
CAE) and Design
Computer-Aided Engineering (CAD
Cost analysis
Demand
Economic models
Engineering
Feasibility studies
Industrial and Production Engineering
Integer programming
Inventory
Inventory management
Linear programming
Mechanical Engineering
Media Management
Mixed integer
Optimization
Original Article
Petroleum pipelines
Pipelines
Refineries
Robustness
Scheduling
Uncertainty
Pipeline scheduling
Robust optimization
Conservatism level
Demand uncertainty
ISSN:0268-3768, 1433-3015
Online Access:Get full text
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Summary:Data uncertainty is an inherent component of many real-world problems, and ignoring the uncertainty makes the proposed programs impractical or inefficient. Robust optimization, regarding the data uncertainty, provides a program that remains feasible for almost all situations. This paper studies the problem of scheduling of an oil pipeline that connects a single refinery to a distribution center (DC) under demand uncertainty. In the first phase, a deterministic mixed integer linear programming (MILP) model is presented for pipeline scheduling and inventory management at a DC. Then, by means of the Γ-robustness approach, the deterministic model is extended to a robust formulation. The robust model is implemented on a real-world multi-product pipeline and, by varying the level of conservatism, trade-off between robustness and cost is analyzed. Finally, by generating the random samples for product demand, the average of surplus or shortage of inventory for each level of conservatism is estimated.
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ISSN:0268-3768
1433-3015
DOI:10.1007/s00170-016-8561-0