Optimization of multi-station pick scheduling in goods-to-person systems with shuttle-based storage and retrieval systems for pharmaceutical logistics

In pharmaceutical logistics, strict compliance with first-in-first-out and batch segregation requirements limits bin reuse and induces bin coupling in goods-to-person picking systems with multiple-level shuttle-based storage and retrieval. Competition for shared bins across picking stations causes frequent bin replacements, longer waiting times, and reduced picking efficiency. To address these challenges, this study develops a mixed-integer programming model for multiple picking station scheduling that explicitly characterizes bin coupling and jointly optimizes order assignment, order sequencing, and bin sequencing. The model jointly minimizes the bin replacements and parallel coupling conflicts. Two solution approaches are proposed: an exact algorithm that integrates dynamic programming with a backtracking search, and a beam search-based heuristic algorithm. Computational results show that the exact algorithm solves small-scale instances within milliseconds, whereas the heuristic algorithm provides high-quality solutions for large-scale instances within seconds. A real-world case study indicates that compared with current random or similarity-based strategies, the proposed method reduces the total cost by up to 22 percent and substantially reduces coupling conflicts. These findings provide actionable guidance for optimizing multiple picking station scheduling in pharmaceutical logistics.