Practical approaches to chemical tanker scheduling in ports: a case study on the Port of Houston

Abstract

The objective of this study was to develop practical scheduling solutions for chemical tankers visiting the Port of Houston (PoH). Chemical tanker movements represent approximately 42% of the Houston Ship Channel traffic. Historically, chemical tanker scheduling has been problematic and has resulted in long waiting times for tankers. Scheduling is difficult because chemical tankers carry several liquid cargoes and must visit multiple terminals for loading and unloading. Physical constraints (layout of the port and draft) and commercial constrains (such as terminal and personnel readiness for cargo handling operations, tank cleaning processes, and inspection requirements) create a complex scheduling problem, long waiting times, and unnecessary tanker movements in the port. These problems cause an increase in the business costs for shipowners, risk of collisions and allisions, production of additional air emissions, and decreases in the operating capacity of terminals. The recent expansion decisions for chemical and petrochemical plants in Houston, Texas, will exacerbate the problem. Significant benefits could thus be gained even for small scheduling improvements. Currently, the scheduling practice of loading/unloading activities in the PoH involves primarily the manual and de-centralized use of the "first come, first served" (FCFS) rule, which results in inefficiencies such as long waiting times and poor resource utilization. We propose two mathematical methods to address the tanker scheduling problem in the port: a mixed-integer programming (MIP) method, and a constraint programming (CP) method. The two methods are formulated as open-shop scheduling problems with sequence-dependent post-setup times. MIP yields optimum results that minimize makespan. However, computation time increases significantly as the number of tankers, or the number of terminals, increases. CP achieves better makespan results in a shorter run time, compared to MIP, for medium to large-scale problems including the problem considered in this case study. Overall, the results show that MIP is more suitable for real-time scheduling tools (hourly and daily), whereas CP is the better option for longer-horizon scheduling problems (weekly or monthly). Our models gave good alternative schedules under short optimization run times. Hence, they can afford decision makers sufficient time to complete multiple optimization scenarios and implementation setups.

Development of a machine-learning-based decision support mechanism for predicting chemical tanker cleaning activity

“…The AIS-based data analysis enables the creation of realistic simulation models and artificial intelligence algorithms to help port authorities in safe navigation especially in congested port areas and waterways (Huang et al, 2020). In addition, ports need to stay competitive in terms of port operations efficiencies in order not to lose businesses to any neighboring ports (Cankaya et al, 2019a). Operational inefficiencies may cause losing money and competition to other ports.…”

“…These terminals handle different types of ships such as chemical products, crude oil, general cargo and containerships (Port of Houston, 2019). Approximately, 42% of the HSC traffic belongs to chemical ships (Cankaya et al, 2019a). Some inefficiencies exist in chemical ship scheduling in the PoH, resulting in long waiting to load/unload chemical cargo (Scully, 2015).…”

“…Some research concluded that the lack of automatic labeling for vessel movement prevents efficient vessel scheduling (Cankaya et al, 2019a;Kaiser and Narra, 2014). The chemical tanker AIS data labeling process can be helpful in gaining business insights needed for efficient transportation at ports.…”

“…Such data can also reveal whether the tank-cleaning operations add any risk to port resiliency due to the additional traffic. While some researchers have attempted to label the vessel activities, they typically relied on private data sets and did not use efficient classification algorithms for decision-making (Cankaya et al, 2019a).…”

“…Moreover, when presented with multiple vessels and a large data set, the machine-learning methodology can be used for predictions that surpass human expert judgment in terms of commercial liability by the speed constraint. When the resultant user scenario of the study thought as a chemical tanker company or a terminal operator updates its scheduling software automatically with the chemical cleaning time labeled data, it significantly changes operational efficiencies (Cankaya et al, 2019a), therefore even though the expert human judgment is used as a benchmark operationally it is almost impossible to use human judgment efficiently in these labeling and scheduling operations.…”