The picking efficiency of warehouses assisted by KIVA robots benefit from exploiting synergy effect between order assignment and picking station scheduling. We treat an integrated optimization which contains both allocating orders and racks to multiple stations and concurrently sequencing their interlinked processing flows at each individual one. The various decisions included in our problem, which are closely associated and must be solved in real time, are often tackled separately for ease of treatment in past. We, however, develop a comprehensive mathematical model under the consideration of the minimum total rack visits. The problem can be proven NP-hard. Consequently, an efficient algorithm based on simulated annealing and dynamic programming is developed. The experimental results show that the proposed approach has more advantage in the light of solution quality as compared with actual rule-based policies. Moreover, the results reveal that ignoring order assignment policy leads to considerable optimality gaps under realistically sized settings.

翻译：由KIVA机器人协助的仓库效率的挑选得益于利用订单分配和选择站时间安排之间的协同效应。我们处理综合优化,包括向多个站点分配订单和架子,并同时对每个站点的相互关联的处理流程进行顺序排列。我们的问题所包含的各种决定与过去密切相关,必须实时解决,过去往往分开处理,以便于治疗。然而,我们在考虑最低总架子访问时开发了一个综合数学模型。问题可以证明是NP-硬的。因此,根据模拟排泄和动态程序开发了一个高效的算法。实验结果显示,与实际基于规则的政策相比,拟议的方法在解决方案质量方面更有优势。此外,结果还表明,无视订单分配政策会导致现实规模环境下的巨大最佳性差距。