Open Access
| Issue |
RAIRO-Oper. Res.
Volume 59, Number 5, September-October 2025
|
|
|---|---|---|
| Page(s) | 3285 - 3307 | |
| DOI | https://doi.org/10.1051/ro/2025132 | |
| Published online | 29 October 2025 | |
- M. Sadeghi, M. Nikfar and F Rad, Optimizing warehouse operations for environmental sustainability: a simulation study for reducing carbon emissions and maximizing space utilization. J. Future Sustain. 4 (2024) 35–44. [Google Scholar]
- A. Shrivastava, A. Gatherer, T. Sun, S. Wokhlu and A. Chandra, Slap: a split latency adaptive VLIW pipeline architecture which enables on-the-fly variable SIMD vector-length, in ICASSP 2021–2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE (2021) 7868–7872. [Google Scholar]
- P. Wu and Y. Chen, Establishing a novel algorithm for highly responsive storage space allocation based on NAR and improved NSGA-III. Complexity 2022 (2022) 4247290. [Google Scholar]
- G. Dunn, H. Charkhgard, A. Eshragh and E. Stojanovski, A modified algorithm for optimal picker routing in a single block warehouse. Preprint arXiv:2409.13219 (2024). [Google Scholar]
- V. Choudhary, K. Patel, M. Niaz, M. Panwala, A. Mehta and K. Choudhary, Implementation of next-gen IoT to facilitate strategic inventory management system and achieve logistics excellence, in 2024 International Conference on Trends in Quantum Computing and Emerging Business Technologies. IEEE (2024) 1–6. [Google Scholar]
- A. Chiaraviglio, S. Grimaldi, G. Zenezini and C. Rafele, Overall warehouse effectiveness (OWE): a new integrated performance indicator for warehouse operations. Logistics 9 (2025) 7. [Google Scholar]
- T. Khai Jen, R. Wang and P. Yang, Determining the I/O point policy in a robotic live-cube compact storage system. Int. J. Prod. Res. 62 (2024) 7056–7072. [Google Scholar]
- C. Lorenz, A. Otto and M. Gendreau, On picking operations in e-commerce warehouses: insights from the complete-information counterpart. Preprint arXiv:2410.14316 (2024). [Google Scholar]
- Z.U. Rizqi and S.-Y. Chou, Dynamic crane scheduling for green automated warehousing: learning-based simulation-optimization approach. Flexible Serv. Manuf. J. (2025) 1–28. [Google Scholar]
- G.J. Viano and S. Sudaryanto, Perancangan sistem automatic storage and retrieval system untuk automasi persediaan sistem pergudangan. Jurnal Syntax Admiration 5 (2024) 3240–3253. [Google Scholar]
- N. Shetty, B. Sah and S.H. Chung, Route optimization for warehouse order picking operations via vehicle routing and simulation. SN Appl. Sci. 2 (2020) 1–18. [Google Scholar]
- Z.U. Rizqi, S.-Y. Chou and A. Khairunisa, Energy harvesting for automated storage and retrieval system with sustainable configuration of storage assignment and input/output point. Transp. Res. Part E: Logistics Transp. Rev. 192 (2024) 103781. [Google Scholar]
- Z.U. Rizqi, S.-Y. Chou, N.-Z. Zheng and A. Dewabharata, On selecting and configuring automated material handling systems: two-stage approach using simulation and data-envelopment analysis. J. Ind. Prod. Eng. (2025) 1–18. DOI: 10.1080/21681015.2025.2495949. [Google Scholar]
- C. Waubert de Puiseau, D.T. Nanfack, H. Tercan, J. Löbbert-Plattfaut and T. Meisen, Dynamic storage location assignment in warehouses using deep reinforcement learning. Technologies 10 (2022) 129. [Google Scholar]
- A. Troch, E. Mannens and S. Mercelis, Solving the storage location assignment problem using reinforcement learning, in Proceedings of the 2023 8th International Conference on Mathematics and Artificial Intelligence (2023) 89–95. [Google Scholar]
- M. Gabellini, F. Calabrese, A. Regattieri, D. Loske and M. Klumpp, A hybrid approach integrating genetic algorithm and machine learning to solve the order picking batch assignment problem considering learning and fatigue of pickers. Comput. Ind. Eng. 191 (2024) 110175. [Google Scholar]
- S. Mahmoudinazlou, A. Sobhanan, H. Charkhgard, A. Eshragh and G. Dunn, Deep reinforcement learning for dynamic order picking in warehouse operations. Comput. Oper. Res. 182 (2025) 107112. [Google Scholar]
- P. Yang, L. Miao, Z. Xue and L. Qin, An integrated optimization of location assignment and storage/retrieval scheduling in multi-shuttle automated storage/retrieval systems. J. Intell. Manuf. 26 (2015) 1145–1159. [Google Scholar]
- J. Cai, X. Li, Y. Liang and S. Ouyang, Collaborative optimization of storage location assignment and path planning in robotic mobile fulfillment systems. Sustainability 13 (2021) 5644. [Google Scholar]
- L. Chen, A. Langevin and D. Riopel, The storage location assignment and interleaving problem in an automated storage/retrieval system with shared storage. Int. J. Prod. Res. 48 (2010) 991–1011. [Google Scholar]
- Y. Leilin, L. Zhiqiang and X. Lifeng, Storage assignment and optimization research of AS/RS based on a hybrid tabu search algorithm, in 2009 International Conference on Computers & Industrial Engineering. IEEE (2009) 501–505. [Google Scholar]
- H. Habibi Tostani, H. Haleh, S.M. Hadji Molana and F.M. Sobhani, An integrated model for storage location assignment and storage/retrieval scheduling in AS/RS system. J. Q. Eng. Prod. Optim. 4 (2019) 149–170. [Google Scholar]
- S.S. Rao and G.K. Adil, Class-based storage assignment in a unit-load warehouse employing AS/RS with inventory space allocation considering product specific setup to holding cost ratio. Asia-Pac. J. Oper. Res. 31 (2014) 1450034. [Google Scholar]
- E. Atmaca and A. Ozturk, Defining order picking policy: a storage assignment model and a simulated annealing solution in AS/RS systems. Appl. Math. Modell. 37 (2013) 5069–5079. [Google Scholar]
- M.E. Fontana, J. Carlos Leyva López, C. Alexandre Virgínio Cavalcante and J. Jaime Solano Noriega, Multi-criteria assignment model to solve the storage location assignment problem. Invest. Oper. 41 (2020) 1019–1029. [Google Scholar]
- S.-M. Guo and T.-P. Liu, An evaluation of storage assignment policies for twin shuttle AS/RS, in 2010 IEEE International Conference on Management of Innovation & Technology. IEEE (2010) 197–202. [Google Scholar]
- J. Bola˜nos-Zu˜niga, M.A. Salazar-Aguilar and J.A. Saucedo-Martínez, Solving location assignment and order picker-routing problems in warehouse management. Axioms 12 (2023) 711. [Google Scholar]
- Z.U. Rizqi, S.-Y. Chou and A. Khairunisa, Multi-objective simulation-optimization for integrated automated storage and retrieval systems planning considering energy consumption. Comput. Ind. Eng. 189 (2024) 109979. [Google Scholar]
- Y.A. Bozer and J.A. White, Travel-time models for automated storage/retrieval systems. IIE Trans. 16 (1984) 329–338. [Google Scholar]
- P.J. Egbelu, Framework for dynamic positioning of storage/retrieval machines in an automated storage/retrieval system. Int. J. Prod. Res. 29 (1991) 17–37. [Google Scholar]
- H. Hwang and J.-M. Lim, Deriving an optimal dwell point of the storage/retrieval machine in an automated storage/retrieval system. Int. J. Prod. Res. 31 (1993) 2591–2602. [Google Scholar]
- B.A. Peters, J.S. Smith and T.S. Hale, Closed form models for determining the optimal dwell point location in automated storage and retrieval systems. Int. J. Prod. Res. 34 (1996) 1757–1771. [Google Scholar]
- B.C. Park, An optimal dwell point policy for automated storage/retrieval systems with uniformly distributed, rectangular racks. Int. J. Prod. Res. 39 (2001) 1469–1480. [Google Scholar]
- R.D. Meller and A. Mungwattana, AS/RS dwell-point strategy selection at high system utilization: a simulation study to investigate the magnitude of the benefit. Int. J. Prod. Res. 43 (2005) 5217–5227. [Google Scholar]
- A. Regattieri, G. Santarelli, R. Manzini and A. Pareschi, The impact of dwell point policy in an automated storage/retrieval system. Int. J. Prod. Res. 51 (2013) 4336–4348. [Google Scholar]
- H. Yu and Y. Yu, Optimising two dwell point policies for AS/RSS with input and output point at opposite ends of the aisle. Int. J. Prod. Res. 57 (2019) 6615–6633. [Google Scholar]
- Z.U. Rizqi, S.-Y. Chou and T. Hui-Kuang Yu, Data-driven approach for dwell point positioning in automated storage and retrieval system: a metaheuristic-optimized ensemble learning. Ann. Oper. Res. (2024) 1–24. [Google Scholar]
- T.S. Hale, M.E. Hanna and F. Huq, The generalised dwell point location problem. Int. J. Ind. Syst. Eng. 4 (2009) 446–454. [Google Scholar]
- S. Subramanian, N. Bhojane, H. Madhnani, S. Pant, A. Kumar and K. Kotecha, A comprehensive review of nature-inspired optimization techniques and their varied applications. Nat.-Inspired Optim. Algorithms Cyber-Phys. Syst. (2025) 105–174. [Google Scholar]
- B. Meniz and F. Tiryaki, Genetic algorithm optimization with selection operator decider. Arabian J. Sci. Eng. 50 (2025) 6931–6941. [Google Scholar]
- C. Gong, Y. Nan, L.M. Pang, H. Ishibuchi and Q. Zhang, Initial populations with a few heuristic solutions significantly improve evolutionary multi-objective combinatorial optimization, in 2023 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE (2023) 1398–1405. [Google Scholar]
- G. Mateeva, D. Parvanov, I. Dimitrov, I. Iliev and T. Balabanov, Time-consuming fitness functions approximation for genetic algorithms performance improvement, in IXth SWS Conference on Social Sciences (ISCSS) (2022). [Google Scholar]
- P. Sankaran and K. McConky, Rethinking selection in generational genetic algorithms to solve combinatorial optimization problems: an upper bound-based parent selection strategy for recombination. Preprint arXiv:2410.03863 (2024). [Google Scholar]
- P. Ciepliński and S. Golak, Crossover operator inspired by the selection operator for an evolutionary task sequencing algorithm. Appl. Sci. 14 (2024) 11786. [Google Scholar]
- L. Bromham, Mutation rate is central to understanding evolution. Am. J. Botany 111 (2024) e16422. [Google Scholar]
- L. Zhang M. Zhou Q. Deng, Q. Kang and J. An, Objective space-based population generation to accelerate evolutionary algorithms for large-scale many-objective optimization. IEEE Trans. Evol. Comput. 27 (2022) 326–340. [Google Scholar]
- J.D. Kamdem and T. Sayah, Stopping criteria for nonlinear variational problems: iterative approach. Numer. Algorithms (2024) 1–23. [Google Scholar]
- M.M.F. de Oliveira, G.C. Pereira and N.F.F. Ebecken, Genetic optimization of artificial neural networks to forecast virioplankton abundance from cytometric data. J. Intell. Learn. Syst. App. 5 (2013) 57–66. [Google Scholar]
- P. Ayala C.A. Garcia G. Caiza, A. Soto-Rodríguez and M.V. García, Applying deep q-networks to local route optimization, in 2024 IEEE 22nd International Conference on Industrial Informatics (INDIN). IEEE (2024) 1–8. [Google Scholar]
- T. Huang, Research on optimization of intelligent control systems based on deep reinforcement learning, in The International Conference Optoelectronic Information and Optical Engineering (OIOE2024). SPIE (2025) 1085–1089. [Google Scholar]
- R.S. Osei and D. Lopez, Experience replay optimisation via ATSC and TSC for performance stability in deep RL. Appl. Sci. 13 (2023) 2034. [Google Scholar]
- C. Kim, Target-network update linked with learning rate decay based on mutual information and reward in deep reinforcement learning. Symmetry (Basel) 15 (2023) 1840. [Google Scholar]
- C. Deng, B. Xie, X. Tuo, L. Chen, Q. Wang and G. Jiang, Improved double deep q-network algorithm applied to multi-dimensional environment path planning of hexapod robots. Sensors 24 (2024) 2061. [Google Scholar]
- H. Wang, F. Zeng and X. Tu, Deep Q-Learning with phased experience cooperation, in CCF Conference on Computer Supported Cooperative Work and Social Computing. Springer (2019) 752–765. [Google Scholar]
- D. Anggreani, Grid search hyperparameter analysis in optimizing the decision tree method for diabetes prediction. Indonesian J. Data Sci. 5 (2024) 190–197. [Google Scholar]
- N. Suba¸sı, Comprehensive analysis of grid and randomized search on dataset performance. Eur. J. Eng. Appl. Sci. 7 (2024) 77–83. [Google Scholar]
- J. Rui, Research on optimization method for storage allocation of multi aisle warehouses, in 2024 5th International Conference on Mechatronics Technology and Intelligent Manufacturing (ICMTIM). IEEE (2024) 646–650. [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.
