Integrated scheduling of vessel dispatching and port operations in the closed-loop shipping system for transporting petrochemicals. (12th July 2019)

• Record Type: Journal Article
• Authors: An, Heungjo; Choi, Sangdo (Sam); Lee, Jay H.
• Is Part Of: Computers & chemical engineering. Volume 126(2019)
• Page Start: 485
• Page End: 498
• Publication Date: 2019-07-12
• Subjects: Closed-loop shipping system -- Scheduling -- Petrochemicals transportation -- Mathematical modeling -- Constructive heuristics; Chemical engineering -- Data processing -- Periodicals; 660.0285
• DOI: 10.1016/j.compchemeng.2019.05.003
• Languages: English
• ISSNs: 0098-1354

Abstract

Abstract: Countries in the Middle East are requiring larger and more complex shipping operations for the growing downstream sectors of the hydrocarbon industry, such as refining and petrochemistry. These shipping operations generally entail a closed-loop system, in which the vessels travel back and forth from a manufacturing plant to various international ports for exporting the hydrocarbon products. The current shipping systems only focus on feasible, smooth, and repeatable operations with a first-available-first-use (FAFU) policy, owing to the lack of a rigorous and systematic optimal scheduling method for maritime transportation systems. This study presents an integrated short-term scheduling model formulated as a mixed integer linear programming (MILP) model, for dispatching vessels from a petrochemical plant to international ports and economically operating a plant-side port in the closed-loop shipping system. Further, the MILP model considers varying vessel capacities and other related complex restrictions. A constructive heuristic algorithm combined with an optimization solver is adopted to solve the MILP model with sufficient speed for practical use. Numerical studies using a real data set demonstrate the effectiveness of the developed model compared to the current scheduling policy, FAFU. Moreover, we analyze the economic and computational performances of the developed solution method. Our method solves the test scenarios effectively, presenting a cost saving opportunity of 11.3% on average, which is equivalent to 439, 829 USD per year for the studied operation.