Mixed-time mixed-integer linear programming for optimal detailed scheduling of a crude oil port depot

Abstract

Crude oil port depots are key hubs in oil trade and thus play a vital role in the crude oil supply chain. Based on the characteristics of the scheduling system in an oil port depot, the development of a precise schedule is essential for handling asynchronous upstream supply and downstream market demand, thereby reducing operation cost as well as the impact of market fluctuations. Most previous studies focused only on the depot system, and few have taken the export transport scheduling into consideration. In this study, the diversity of crude oil storage modes, complexity of operations, and variety of transportation modes are considered. To minimize the total operation cost, a mixed-time representation based on a mixed-integer linear programming model is established. Finally, three real cases in a crude oil port depot in China are studied, and the monthly schedules of the depot are developed. Compared with previous algorithms, this method yields more accurate results in a considerably shorter time with improved computational stability.