Data from: Congestion reduction through efficient container movement under stochastic demand

In today’s world, there is a significant amount of investigation regarding how to efficiently distribute loaded containers from the ports to the consignees. However, to fully maximize the process and become more environmentally friendly, one should also study how to allocate the empty containers created by these consignees. This is an essential part in the study of container movement since it balances out the load flow at each location.

The problem of coordinating the container movement to reuse empty containers and lower truck miles is called the “Empty Container Problem”. In this work, we develop a scheduling assignment for loaded and empty containers that builds on earlier models but incorporates stochastic (random) future demand. It is worth mentioning that in the previous research [5], the empty container problem was divided into two subproblems, including an assignment problem and a vehicle routing problem (VRP).

The previous research only considered the problem as a one-day horizon. But in reality, the container movements are not only to fulfill today’s demand at each location but also prepare for the next day’s delivery. Thus, incorporating future demand is an essential aspect of the problem.

By considering the future demand, a better solution can be constructed compared to solving the problem as a one-day horizon problem. This report shows that the truck miles needed to satisfy the demand at all locations is reduced by about 4-7% when considering future stochastic demand as opposed to only considering today’s demand, thus, leading to a cleaner and greener solution, creating less congestion and lowering the impact of freight movement on the environment.

The data is mostly randomly generated data sets and demand data for containers near the San Pedro Ports.