  Due to the rapid increase of air cargo and postal transport volume, an
efficient automated multi-dimensional warehouse with elevating transfer
vehicles (ETVs) should be established and an effective scheduling strategy
should be designed for improving the cargo handling efficiency. In this paper,
artificial bee colony algorithm, which possesses strong global optimization
ability and fewer parameters, is firstly introduced to simultaneously optimize
the route of ETV and the assignment of entrances and exits. Moreover, for
further improve the optimization performance of ABC, novel full-dimensional
search strategy with parallelization, and random multi-dimensional search
strategy are incorporated in the framework of ABC to improve the diversity of
the population and the convergence speed respectively. Our proposed algorithms
are evaluated on several benchmark functions, and then applied to solve the
combinatorial optimization problem with multitask, multiple entrances and exits
in air cargo terminal. The simulations show that the proposed algorithms can
achieve much more desired performance than the traditional artificial bee
colony algorithm at balancing the exploitation and exploration abilities.
