1 research outputs found
Bi-objective Recoverable Berth Allocation and Quay Crane Assignment Planning under Environmental Uncertainty
This study discusses the development of tactical-level
integrated planning at seaport container terminals in an uncertain environment. The suggested approach seeks to strike a
balance between the cost-effectiveness of a robust baseline schedule and
recovery plan and the required quality of customer service in order to enhance
the competitive edge of container ports. Integrated planning for a tactical level at the container
terminal synchronizes the decisions of berth allocation and quay crane
assignment planning by taking into account the unpredictability of the vessel's
arrival time and handling time caused by a variety of unforeseen factors such
as unfavorable weather conditions, instability in the productivity rate of the
quay cranes, the uncertainty of the quantity of loading and discharging containers,
and other unpredictable events. The proposed optimization model produces a robust and proactive
baseline schedule with a recoverable reactive plan for each scenario that
occurs by utilizing buffer times and quay cranes that anticipate fluctuations
in uncertain parameters. The proposed bi-objective recoverable robustness
optimization model is solved by applying a hybrid method, namely the Rolling
Horizon-based Optimization Algorithm (RHOA) and the Preemptive Goal Programming
approach, using Gurobi-Python Optimization. The proposed bi-objective recoverable robust optimization
model demonstrates superior solution quality in terms of service level and
total costs, as well as a more efficient computational time when compared to an
optimization model that minimizes total costs for tactical level planning
decisions in seaside container terminals