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Open AccessFeature PaperArticle

Adjustable Robust Optimization for Planning Logistics Operations in Downstream Oil Networks

CEG—IST, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, 1049-001 Lisboa, Portugal
Department of Applied Mathematics, University of Málaga, 29071 Málaga, Spain
Author to whom correspondence should be addressed.
Processes 2019, 7(8), 507;
Received: 27 April 2019 / Revised: 15 July 2019 / Accepted: 19 July 2019 / Published: 2 August 2019
The oil industry operates in a very uncertain marketplace, where uncertain conditions can engender oil production fluctuations, order cancellation, transportation delays, etc. Uncertainty may arise from several sources and inexorably affect its management by interfering in the associated decision-making, increasing costs and decreasing margins. In this context, companies often must make fast and precise decisions based on inaccurate information about their operations. The development of mathematical programming techniques in order to manage oil networks under uncertainty is thus a very relevant and timely issue. This paper proposes an adjustable robust optimization approach for the optimization of the refined products distribution in a downstream oil network under uncertainty in market demands. Alternative optimization techniques are studied and employed to tackle this planning problem under uncertainty, which is also cast as a non-adjustable robust optimization problem and a stochastic programing problem. The proposed models are then employed to solve a real case study based on the Portuguese oil industry. The results show minor discrepancies in terms of network profitability and material flows between the three approaches, while the major differences are related to problem sizes and computational effort. Also, the adjustable model shows to be the most adequate one to handle the uncertain distribution problem, because it balances more satisfactorily solution quality, feasibility and computational performance. View Full-Text
Keywords: distribution; planning; oil supply chain; robust optimization; uncertainty distribution; planning; oil supply chain; robust optimization; uncertainty
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Lima, C.; Relvas, S.; Barbosa-Póvoa, A.; Morales, J.M. Adjustable Robust Optimization for Planning Logistics Operations in Downstream Oil Networks. Processes 2019, 7, 507.

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