We consider single-crane scheduling at rail transshipment yards, in which gantry cranes move containers between trains, trucks and a storage area. The single-crane scheduling problem arises at single-crane transshipment terminals and as a subproblem of the multiple-crane scheduling problem. We consider a makespan objective function, which is equivalent to minimizing the train dwell time in the yard, and introduce time windows for container moves, for example, as a customer service promise. Our proposed decomposition algorithm with integrated dynamic branch-and-cut or dynamic programming solves practically relevant instances within short time limits.
The train-to-yard assignment problem (TYAP) pertains to freight consolidation in a large rail transshipment yard—also called a multiple yard—that consists of two sub-yards. Inbound and outbound trains need to be assigned to one or the other sub-yard in a way that minimizes the total railcar switching costs. Each inbound and outbound train is processed in one of the two sub-yards, and time-consuming maneuvers may be necessary for railcars that are supposed to be part of an outbound train leaving from the other sub-yard. A lower number of railcar reassignments between the sub-yards reduce train dwell times and avoid train delays that affect the whole rail network. We develop a matheuristic algorithm with a learning mechanism, which we call MuSt, as well as a branch-and-bound procedure that incorporates elements of constraint propagation. We examine the performance of the developed algorithms through extensive computational experiments. Effective optimization approaches for the TYAP have high practical significance since they may reduce the number of avoidable railcar reassignments, which are resource-blocking, traffic-generating, and expensive, by about 20% compared to current practice, as we illustrate in our computational experiments. Our branch-and-bound algorithm solves problem instances for small or medium railyards in less than a minute or within several hours run time, respectively. The heuristic procedure MuSt finds optimal or nearly optimal solutions within just a couple of minutes, even for large railyards.
Optimization approaches for civil applications of unmanned aerial vehicles (UAVs) or aerial drones
(2018)
Unmanned aerial vehicles (UAVs), or aerial drones, are an emerging technology with significant market potential. UAVs may lead to substantial cost savings in, for instance, monitoring of difficult‐to‐access infrastructure, spraying fields and performing surveillance in precision agriculture, as well as in deliveries of packages. In some applications, like disaster management, transport of medical supplies, or eironmental monitoring, aerial drones may even help save lives. In this article, we provide a literature survey on optimization approaches to civil applications of UAVs. Our goal is to provide a fast point of entry into the topic for interested researchers and operations planning specialists. We describe the most promising aerial drone applications and outline characteristics of aerial drones relevant to operations planning. In this review of more than 200 articles, we provide insights into widespread and emerging modeling approaches. We conclude by suggesting promising directions for future research.