Particle swarm optimization for the truck scheduling in container terminals

In this paper we focus on the dispatching problem for trucks at a container terminal, considering a set of transportation requests with different ready times and sequence-dependent processing times. Since the scheduling problem is proved to be NP-hard, exact solution approaches cannot solve it within reasonable time. We proposed a new approach based on particle swarm optimization (PSO) to obtain the optimal solution. Smallest Position Value (SPV) rule is applied as a mapping mechanism to determine the scheduling permutation. Furthermore, a novel algorithm used to convert particle position value into job permutation solution and truck dispatching solution is designed. In the experiment study, two kinds of PSO algorithm are used, i.e. Standard PSO (SPSO) and Local PSO (LPSO). The results obtained by PSOs are also compared with that obtained by genetic algorithm (GA). Experimental results demonstrate that the PSO based approach is efficient to solve the truck scheduling problem than GA in terms of convergence rate, solution quality and CPU time.