Port yard storage optimization

The port yard storage optimization problem (PYSOP) originates from space allocation needs at the Port of Singapore. Space allocated to cargo is to be minimized in a designated yard within a time interval. The problem is akin to a packing problem in space and time, but where shapes packed and constraints are particular to port operations. Further, space requests can change within the time interval in which it is requested. This basic problem is generic to port operations and may find applications elsewhere. The PYSOP is NP-hard, but we propose a number of metaheuristics. Extensive experiments were conducted and good results obtained. Note to Practitioners-The Port of Singapore is one of the busiest ports in the world where competing pressures for land use and competition from other regional and international ports force port planners to make best use of available land. Factors that impact storage capacity include stacking heights, net storage area available, storage density (containers per acre), dwell times for empty containers and breakbulk cargo. In studying its operations to find better ways to utilize storage space within the dynamic environment of the port, we narrowed storage problems down and focused on the central allocation process in storage-operations improvement which would allow for better utilization of space. In this process, requests are made from an operations unit which coordinates ship berthing and ship-to-apron loading as well as apron-to-yard transportation. Each request is for a set of spaces within a yard required in a single time interval. If any space is allocated to the request, this space cannot be freed (released) until the request is completed, that is, until the end time point of the time interval. The problem is akin to a packing problem in space and time, but where shapes packed and constraints are particular to port operations. Further, space requests can change within the time interval in which it is requested. This basic problem is generic to port operations and may find applications elsewhere. The PYSOP is NP-hard for which we propose a number of metaheuristics. Extensive experiments were conducted and good results obtained.