Yard crane scheduling in container terminal using Genetic Algorithm

The increase in world trade has spurred the tremendous growth in cargo containerization through container ports, which has put pressure on the port to make facilities more efficient to cope with this dynamic challenge. Without an effective operation planning of yard cranes, trucks may have to wait in the yard, and consequently QCs (quay cranes) will be idle in waiting for trucks. Therefore, to achieve high productivity, the use of yard cranes should be well planned. In this paper, the yard crane scheduling problem seeks an optimal schedule for a given collection of container flows, each of which requires a known sequence of handling operations in storage blocks, where only one container can be mounted by each yard crane at a time. Considering yard crane problem's constraints, for example, the precedence constraints among the operations and the prevention of conflicts between container flows, we formulate a yard crane scheduling problem mathematical model. To solve this problem, we propose a Genetic Algorithm (GA) approach, which uses operation-based representation, partial schedule exchange crossover and job-pair exchange mutation. The proposed Genetic Algorithm method is compared with heuristics: Shortest Processing Time (SPT) and Longest Processing Time (LPT) in the different instances of yard crane scheduling problem. Comparative analyses show the proposed GA provides optimal solutions in all the cases.