The integrated optimization of container terminal scheduling with uncertain factors

• Model of integrated scheduling at terminals with uncertain factors are formulated.
• A PSO (particle swarm optimization) algorithm is stated for solving the problem.
• Numerical studies are conduced to test the model and the PSO algorithm.
• Operation of parallel discharging and loading is our further study direction.

This paper proposes a PSO (particle swarm optimization)-based integrated optimization of container terminal scheduling with uncertain factors. It explores uncertain factors of yard truck travel speed, yard crane speed and unit time of yard crane hoisting/lowing operation, which are not considered in classical literature. On the basis of it, an integrated scheduling optimization model is suggested. The objective of the model is to minimize the operation time of the yard crane with the coordination of the quay crane and yard truck. To solve this difficult combinatorial problem, the PSO algorithm is developed. PSO is evaluated for combinatorial problems with uncertain factors, which represents a new application of PSO. Different from published literatures, our study findings can be presented in two aspects. One is in the modeling. This includes (i) formalizing the description of the purpose of the model; (ii) a real-world coordination of three equipments that incorporate in uncertain circumstances. The other is that the PSO algorithm is applied to deal with integrated scheduling with uncertain factors whose results can be gotten in permitted time with stability and satisfactory. Numerical experiments show that the model gives systemic simulation for the whole scheduling process with uncertain factors. And the results are stable and acceptable in allowable CPU time. Furthermore, compared with the results in determined context, the model with uncertain factors can get a more stable solution and guarantee the objective function to achieve sufficiently optimal results.