Abstract
This paper studies a novel rack scheduling problem with multiple types of multiple storage locations (RS-MTMS), which can decide the retrieval sequence of racks and assign each rack a storage location after visiting a picking station. A major challenge in RS-MTMS is that the storage assignment problem and the retrieval sequence decision are closely coupled. If the RS-MTMS is solved directly, the storage assignment scheme and the retrieval sequence of racks are generally generated separately, thus resulting in poor performance. To overcome this difficulty, we propose a bi-level optimization approach for jointly optimizing the storage assignment and retrieval sequence (BiJSR). In BiJSR, the storage assignment problem is solved by variable neighborhood search (VNS) in the upper-level optimization. Effective candidate modes are incorporated into VNS to improve solution quality and computational efficiency. The sequencing optimization is obtained in the lower-level according to the given storage location set. A transformation strategy with sufficient problem-specific knowledge is developed to identify the lower-level optimization as the traveling salesman problem and its variants. Then these identified problems are solved using the loop-based strategy. Experimental results show that the proposed BiJSR is more effective and efficient than the representative algorithms in solving the RS-MTMS problem.
References
Boysen N, de Koster R, Weidinger F. Warehousing in the e-commerce era: a survey. Eur J Oper Res, 2019, 277: 396–411
Azadeh K, de Koster R, Roy D. Robotized and automated warehouse systems: review and recent developments. Transpation Sci, 2019, 53: 917–945
da Costa Barros Í R, Nascimento T P. Robotic mobile fulfillment systems: a survey on recent developments and research opportunities. Robot Autonom Syst, 2021, 137: 103729
Rimélé A, Gamache M, Gendreau M, et al. Robotic mobile fulfillment systems: a mathematical modelling framework for e-commerce applications. Int J Production Res, 2022, 60: 3589–3605
Shi X, Deng F, Fan Y, et al. A two-stage hybrid heuristic algorithm for simultaneous order and rack assignment problems. IEEE Trans Automat Sci Eng, 2022, 19: 2955–2967
Kim H J, Pais C, Shen Z J M. Item assignment problem in a robotic mobile fulfillment system. IEEE Trans Automat Sci Eng, 2020, 17: 1854–1867
Valle C A, Beasley J E. Order allocation, rack allocation and rack sequencing for pickers in a mobile rack environment. Comput Oper Res, 2021, 125: 105090
Zou B, Gong Y Y, Xu X, et al. Assignment rules in robotic mobile fulfilment systems for online retailers. Int J Production Res, 2017, 55: 6175–6192
Yue L, Fan H. Dynamic scheduling and path planning of automated guided vehicles in automatic container terminal. IEEE CAA J Autom Sin, 2022, 9: 2005–2019
Merschformann M, Lamballais T, de Koster M B M, et al. Decision rules for robotic mobile fulfillment systems. Oper Res Perspect, 2019, 6: 100128
Gharehgozli A, Zaerpour N. Robot scheduling for pod retrieval in a robotic mobile fulfillment system. Transpation Res Part E-logistics Transpation Rev, 2020, 142: 102087
Weidinger F, Boysen N, Briskorn D. Storage assignment with rack-moving mobile robots in KIVA warehouses. Transpation Sci, 2018, 52: 1479–1495
Boysen N, Briskorn D, Emde S. Parts-to-picker based order processing in a rack-moving mobile robots environment. Eur J Oper Res, 2017, 262: 550–562
Yang X, Hua G, Hu L, et al. Joint optimization of order sequencing and rack scheduling in the robotic mobile fulfilment system. Comput Oper Res, 2021, 135: 105467
Xie J, Mei Y, Ernst A T, et al. A bi-level optimization model for grouping constrained storage location assignment problems. IEEE Trans Cybern, 2016, 48: 385–398
Hanson R, Medbo L, Johansson M I. Performance characteristics of robotic mobile fulfilment systems in order picking applications. IFAC-PapersOnLine, 2018, 51: 1493–1498
de Koster R, Le-Duc T, Roodbergen K J. Design and control of warehouse order picking: a literature review. Eur J Oper Res, 2007, 182: 481–501
Martinez-Carranza J, Rojas-Perez L O. Warehouse inspection with an autonomous micro air vehicle. Unmanned Sys, 2022, 10: 329–342
Chu W J, Zhang W, Zhao H Y, et al. Massive self-organized shape formation in grid environments. Sci China Inf Sci, 2022, 65: 164101
Lamballais T, Roy D, de Koster M B M. Estimating performance in a robotic mobile fulfillment system. Eur J Oper Res, 2017, 256: 976–990
Duan G, Zhang C, Gonzalez P, et al. Performance evaluation for robotic mobile fulfillment systems with time-varying arrivals. Comput Industrial Eng, 2021, 158: 107365
Jaghbeer Y, Hanson R, Johansson M I. Automated order picking systems and the links between design and performance: a systematic literature review. Int J Production Res, 2020, 58: 4489–4505
Lienert T, Staab T, Ludwig C F, et al. Simulation-based performance analysis in robotic mobile fulfilment systems. In: Proceedings of the 8th International Conference on Simulation and Modeling Methodologies, Technologies and Applications, 2018
Bozer Y A, Aldarondo F J. A simulation-based comparison of two goods-to-person order picking systems in an online retail setting. Int J Production Res, 2018, 56: 3838–3858
Yang X, Liu X, Feng L, et al. Non-traditional layout design for robotic mobile fulfillment system with multiple workstations. Algorithms, 2021, 14: 203
Yuan R, Cezik T, Graves S C. Stowage decisions in multi-zone storage systems. Int J Production Res, 2018, 56: 333–343
Gong Y, Jin M, Yuan Z. Robotic mobile fulfilment systems considering customer classes. Int J Production Res, 2021, 59: 5032–5049
Zhuang Y, Zhou Y, Yuan Y, et al. Order picking optimization with rack-moving mobile robots and multiple workstations. Eur J Opera Res, 2022, 300: 527–544
Xie L, Thieme N, Krenzler R, et al. Introducing split orders and optimizing operational policies in robotic mobile fulfillment systems. Eur J Opera Res, 2021, 288: 80–97
Shahriari M, Biglarbegian M. A new conflict resolution method for multiple mobile robots in cluttered environments with motion-liveness. IEEE Trans Cybern, 2018, 48: 300–311
Cai J, Li X, Liang Y, et al. Collaborative optimization of storage location assignment and path planning in robotic mobile fulfillment systems. Sustainability, 2021, 13: 5644
Li X P, Pan D Y, Wang Y D, et al. Scheduling multi-tenant cloud workflow tasks with resource reliability. Sci China Inf Sci, 2022, 65: 192106
Cao Z C, Lin C R, Zhou M C, et al. Scheduling semiconductor testing facility by using Cuckoo search algorithm with reinforcement learning and surrogate modeling. IEEE Trans Automat Sci Eng, 2018, 16: 825–837
Li S L, Zhai D S, Du P F, et al. Energy-efficient task offloading, load balancing, and resource allocation in mobile edge computing enabled IoT networks. Sci China Inf Sci, 2019, 62: 029307
Yuan H, Zhou M C, Liu Q, et al. Fine-grained and arbitrary task scheduling for heterogeneous applications in distributed green clouds. IEEE CAA J Autom Sin, 2020, 7: 1380–1393
Merschformann M. Active repositioning of storage units in robotic mobile fulfillment systems. In: Proceedings of Operations Research Proceedings 2017, 2018. 379–385
Yuan R, Graves S C, Cezik T. Velocity-based storage assignment in semi-automated storage systems. Prod Oper Manag, 2019, 28: 354–373
Ji T, Zhang K, Dong Y. Model-based optimization of pod point matching decision in robotic mobile fulfillment system. In: Proceedings of IEEE 7th International Conference on Industrial Engineering and Applications (ICIEA), 2020. 216–223
Ouzidan A, Pardo E G, Sevaux M, et al. BVNS approach for the order processing in parallel picking workstations. In: Proceedings of the 8th International Conference on Variable Neighborhood Search, 2021. 176–190
Wang Z, Sheu J-B, Teo C-P, et al. Robot scheduling for mobile-rack warehouses: human-robot coordinated order picking systems. Production Oper Manag, 2022, 31: 98–116
Yuan W, Sun H. A task scheduling problem in mobile robot fulfillment systems. In: Proceedings of the 12th International Conference on Advanced Computational Intelligence (ICACI), 2020. 391–396
Lu S, Xin B, Zhang H, et al. Agent-based self-organized constructive heuristics for travelling salesman problem. In: Proceedings of the 59th IEEE Conference on Decision and Control (CDC), 2020. 1164–1169
Osaba E, Ser J D, Sadollah A, et al. A discrete water cycle algorithm for solving the symmetric and asymmetric traveling salesman problem. Appl Soft Computing, 2018, 71: 277–290
Kuhn H W. The Hungarian method for the assignment problem. Naval Res Logistics, 1955, 2: 83–97
Zhou Z, Liu Z T, Su H Y, et al. Multi-objective optimization for 10-kW rated power dynamic wireless charging systems of electric vehicles. Sci China Inf Sci, 2022, 65: 202201
Chen Y X, Shi Z K, Xu B, et al. Optimal design of a scaled-up PRO system using swarm intelligence approach. Sci China Inf Sci, 2021, 64: 222203
Acknowledgements This work was supported in part by National Natural Science Foundation of China (Grant No. 61933002), National Science Fund for Distinguished Young Scholars (Grant No. 62025301), and National Natural Science Foundation of China Basic Science Center Program (Grant No. 62088101).
Author information
Authors and Affiliations
Corresponding author
Additional information
Supporting information Appendixes A–C. The supporting information is available online at info.scichina.com and link. springer.com. The supporting materials are published as submitted, without typesetting or editing. The responsibility for scientific accuracy and content remains entirely with the authors.
Electronic supplementary material
Rights and permissions
About this article
Cite this article
Shi, X., Deng, F., Lu, S. et al. A bi-level optimization approach for joint rack sequencing and storage assignment in robotic mobile fulfillment systems. Sci. China Inf. Sci. 66, 212202 (2023). https://doi.org/10.1007/s11432-022-3714-4
Received:
Revised:
Accepted:
Published:
DOI: https://doi.org/10.1007/s11432-022-3714-4