Browsing by Author "Perera, H. N."

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item: Conference-Full-text
Integrated quay crane assignment and scheduling problem under uncertainty: a reactive approach
(Sri Lanka Society of Transport and Logistics, 2024) Wijesooriya, D; Weerasinghe, B. A.; Perera, H. N.; Gunaruwan, T. L.
This study examines the integrated quay crane assignment and scheduling problem (QCASP) under uncertainty at container terminals. First, a mixed integer linear programming model is formulated to optimally assign and schedule quay cranes to multiple vessels simultaneously. The model derives a baseline schedule that minimises the cost of waiting and departure delays of vessels. However, different disruptions; delays in vessel arrivals and quay crane breakdowns may occur when implementing the baseline schedule. Therefore, a reactive strategy is formulated to generate a reactive reschedule that minimises delays from the baseline schedule when a disruption occurs. The reactive strategy takes the baseline schedule as a reference and derives the reactive schedule by rescheduling only quay cranes. Finally, the accuracy of the model is tested by running small-size problem instances.
item: Conference-Full-text
Optimizing quayside truck allocation: expert system for automating discharging operations planning
(Sri Lanka Society of Transport and Logistics, 2024) De Silva, V.; Weerasinghe, B. A.; Perera, H. N.; Gunaruwan, T. L.
This research investigates optimizing discharging truck allocation at container terminals, crucial hubs in global maritime logistics, by using a fuzzy logic approach to enhance container movements from ship to shore. Traditionally managed manually by ground handling staff, the truck allocation process is automated in this model to address the complexities of quayside operations. This study proposes a model that adapts to operational variables, reducing bottlenecks and increasing terminal throughput. By employing fuzzy logic for its adaptability and interpretability, the research provides a computational methodology suitable for complex quayside operations, involving fuzzification, inference, and defuzzification to transform raw data into actionable insights. Data were collected from two container terminals at a leading South Asian port, ranked among the top 30 global ports. The study used the Fuzzy Logic Toolbox in MATLAB and Python to effectively integrate a rule-based structure. The findings highlight the critical role of discharging truck allocation in enhancing terminal efficiency and operational integration, with the model demonstrating compatibility with the Terminal Operating System (TOS). Future research should focus on more dynamic and integrated operational planning systems to further improve efficiency in container terminal operations.