# Cargo Terminal Intelligent-Scheduling Strategies Based on Improved Bee Colony Algorithms
## 1 Introduction  
Nowadays, transport activities around the world are developing rapidly, and the cooperative management of cargo terminals, which are the major gateway for cargo services, has become crucial. Thus, an effective and efficient scheduling and cooperative management strategy considering the route of ETVs and the assignment of entrances and exits should be designed to minimize the time cost for handling all inbound and outbound cargoes [1; 2; 3]. Actually, if the numbers of ETVs and tasks are small, the scheduling problem can be regarded as a zero-one integer linear programming problem and can be solved using the simple method, such as the Hungarian algorithm [4], the mixed-integer linear model [5], the enumeration technique [6], the priority rule-based procedure [7], and so on. But for large-scale problems that are difficult to be solved by traditional optimization algorithms, the evolutionary algorithms with high robustness, become more effective. [PERSON] [8] andQiu [9] studied the inbound and outbound cargo scheduling problem with a single ETV and solved it with the genetic algorithm and the particle swarm optimization (PSO) algorithm, respectively. PSO was also applied to assign two ETVs to different cargo areas with an improved shared fitness strategy in ref. [10]. The works mentioned above focus on deciding the cargo transportation sequence by considering the picking sequence and ETV routing, but very few studies have discussed the problem of the assignment of entrances and exits if there are several gates in the freight station. In our research, a swarm intelligent algorithm named the artificial bee colony algorithm (ABC), which possesses a strong global optimization ability and fewer parameters [11; 12; 13; 14], is firstly proposed to solve the problem of assigning the entrances and exits as well as autonomous ETVs for several outbound and inbound tasks simultaneously. Actually, as an artificial intelligence algorithm, the ABC has been well adapted for various complex optimization and scheduling problems [15; 16; 17; 18; 19; 20; 21]; however, it often suffers from the problem of a slow convergence rate because of its single-dimensional random search strategy in the bee updating phases. To accelerate the convergence speed without reducing the accuracy, other metaheuristic algorithms were introduced and combined with the traditional ABC. [PERSON] and [PERSON] [22] combined the mutation and the crossover operators in differential evolution algorithm with the onlooker bee phases to improve the accuracy and speed up the convergence. Aiming at improving the optimization accuracy, combined with the learning characteristics of the Q-learning algorithm, the update dimension in each iteration could be dynamically adjusted in ref. [23]. [PERSON] et al. [24] introduced a differential evolution strategy in the employed bee phase to accelerate its convergence and adopted the global best position to guide the updating processes in the onlooker bee phase, which could enhance the local search ability. The firework explosion search mechanism was introduced to explore the potential food sources of ABC in ref. [25]. A modified search operator was employed in ref. [26] to exploit useful information of the current best solution in the onlooker phase, and the ability of exploitation could be improved. Obviously, most of the improvements are based on the introduced search strategies or the combinations with other algorithms, and there are few systematic analyses and improvements from the perspective of operation mechanisms. Therefore, for balancing the abilities of exploration and exploitation, after modeling the actions of the ETV in a cargo terminal with multiple entrances and exits, improved ABC algorithms with paralleled full-dimensional search strategy and random multidimensional search strategy are proposed in this paper. The rest of paper is organized as follows: Section 2 introduces how to establish the scheduling model for a cargo terminal. Then, two improved strategies based on the ABC are proposed in Section 3.