基于Block Gibbs的航空公司外航服务人员排班算法

航空公司外航服务人员排班旨在优化员工排班方案以满足外航航班的人员资质需求，并最小化员工总工作时长和兼顾工作时间均衡，其本质是一个面向多任务类型、员工层次资质、白夜班轮换等约束的人员排班问题。现有算法未考虑白夜班轮换强制性约束，制约了它们的应用。为此，该文提出基于Block Gibbs的航空公司外航服务人员排班算法。算法首先设计了数据拷贝技巧以快速建模具有白夜晚班约束的排班问题，然后提出基于Block Gibbs的多员工有放回抽样优化策略。理论分析表明该文算法与基准算法具有同规模的计算复杂度，但却具有更高的抽样效率以加大可行解生成规模和求解速度。与此同时，在国内某大型航空公司外航服务部排班数据集上的实验表明：相比于基准算法，算法在工作总时长、有效工作时长、有效工作时长比例等指标上提升至少0.62%。

Scheduling Service Staffs for Alien Airlines Using Block Gibbs Sampling

Scheduling staffs servicing alien airlines aims to yield task-person assignments by covering the required skills and minimizing employee total working hours as well as balancing staffs’ workload. Its essence is a personnel scheduling problem constrained by multiple task types, hierarchical skills as well as day and night alternation. The existing algorithms do not consider the constraint of day and night alternation. An algorithm is proposed to address that issue. The proposed algorithm firstly designs a data copy trick to quickly model the issue of staff scheduling constrained by day and night alternation. A novel Block Gibbs sampling technique with replacement is designed to efficiently optimize the formulated problem. Theoretical analysis indicates that the computational complexity of the proposed algorithm is the same scale to that of the baselines, whereas the proposed algorithm gains high sampling efficiency. Experimental results on a real dataset shows the improvement of the proposed algorithm over the existing methods is at least 0.62% in terms of evaluation measures.