Quantum behaved particle swarm optimization of inbound process in an automated warehouse

References

• Ababneh, J. (2015). Greedy particle swarm and biogeography-based optimization algorithm. International Journal of Intelligent Computing and Cybernetics, 8(1), 28–49. https://doi.org/10.1108/IJICC-01-2014-0003
   Google Scholar
• Basturk, B. (2006). An artificial bee colony (ABC) algorithm for numeric function optimization. The IEEE Swarm Intelligence Symposium, Indianapolis, IN, USA.
   Google Scholar
• Beltran, B., Carrese, S., Cipriani, E., & Petrelli, M. (2009). Transit network design with allocation of green vehicles: A genetic algorithm approach. Transportation Research Part C: Emerging Technologies, 17(5), 475–483. https://doi.org/10.1016/j.trc.2009.04.008
   Web of Science ®Google Scholar
• Cheng, J. W., Zhang, F., & Li, X. Y. (2022). Nonlinear amplitude inversion using a Hybrid Quantum Genetic algorithm and the exact Zoeppritz equation. Petroleum Science, in Press, 19(3), 1048–1064. https://doi.org/10.1016/j.petsci.2021.12.014
   Web of Science ®Google Scholar
• D’Antonio, G., & Chiabert, P. (2019). Analytical models for cycle time and throughput evaluation of multi-shuttle deep-lane AVS/RS. The International Journal of Advanced Manufacturing Technology, 104(5–8), 1919–1936. https://doi.org/10.1007/s00170-019-03985-8
   Web of Science ®Google Scholar
• Ekren, B. Y., & Akpunar, A. (2021). An open queuing network-based tool for performance estimations in a shuttle-based storage and retrieval system. Applied Mathematical Modelling, 89(2), 1678–1695. https://doi.org/10.1016/j.apm.2020.07.055
   Web of Science ®Google Scholar
• Emde, S., Polten, L., & Gendreau, M. (2020). Logic-based benders decomposition for scheduling a batching machine. Computers & Operations Research, 113, 104777. https://doi.org/10.1016/j.cor.2019.104777
   Web of Science ®Google Scholar
• Feng, B., & Ye, Q. (2021). Operations management of smart logistics: A literature review and future research. Frontiers of Engineering Management, 8(3), 344–355.
   Google Scholar
• Gao, X. H. (2021). A location-driven approach for warehouse location problem. Journal of the Operational Research Society, 72(12), 2735–2754. https://doi.org/10.1080/01605682.2020.1811790
   Web of Science ®Google Scholar
• Guedria, N. B. (2016). Improved accelerated PSO algorithm for mechanical engineering optimization problems. Applied Soft Computing, 40, 455–467.
   Google Scholar
• Jerman, B., Ekren, B. Y., Küçükyaşar, M., & Lerher, T. (2021). Simulation-based performance analysis for a novel AVS/RS technology with movable lifts. Applied Sciences, 11(5), 2283. https://doi.org/10.3390/app11052283
   Google Scholar
• Jiang, M., Leung, K. H., Lyu, Z. Y., & Huang, G. Q. (2020). Picking-replenishment synchronization for robotic forward-reserve warehouses. Transportation Research Part E: Logistics and Transportation Review, 144, 102138. https://doi.org/10.1016/j.tre.2020.102138
   Web of Science ®Google Scholar
• Kennedy, J., & Eberhart, R. C. (1995). Particle swarm optimization. Proceedings of IEEE International Conference on Neural Networks, Piscataway, NJ, USA, 1942–1948.
   Google Scholar
• Kumawat, G. L., & Roy, D. (2021). A new solution approach for multi-stage semi-open queuing networks: An application in shuttle-based compact storage systems. Computers & Operations Research, 125, 105086. https://doi.org/10.1016/j.cor.2020.105086
   Web of Science ®Google Scholar
• Küçükyasara, M., Ekren, B. Y., & Lerher, T. (2020). Cost and performance comparison for tier-captive and tier-to-tier SBS/RS warehouse configurations. International Transactions in Operational Research, 28(2), 1–17.
   Google Scholar
• Lerher, T. (2018a). Warehousing 4.0 by using shuttle-based storage and retrieval systems. FME Transaction, 46(3), 381–385. https://doi.org/10.5937/fmet1803381L
   Web of Science ®Google Scholar
• Lerher, T. (2018b). Aisle changing shuttle carriers in autonomous vehicle storage and retrieval systems. International Journal of Production Research, 56(11), 3859–3879. https://doi.org/10.1080/00207543.2018.1467060
   Web of Science ®Google Scholar
• Lerher, T., Ficko, M., & Palčič, I. (2021). Throughput performance analysis of automated vehicle storage and retrieval systems with multiple-tier shuttle vehicles. Applied Mathematical Modelling, 91, 1004–1022. https://doi.org/10.1016/j.apm.2020.10.032
   Web of Science ®Google Scholar
• Li, L., Jiao, L., Zhao, J., Shang, R., & Gong, M. (2017). Quantum-behaved discrete multi-objective particle swarm optimization for complex network clustering. Pattern Recognition, 63, 1–14. https://doi.org/10.1016/j.patcog.2016.09.013
   Web of Science ®Google Scholar
• Liu, F., Fang, K., Tang, J., & Yin, Y. (2022). Solving the rotating seru production problem with dynamic multi-objective evolutionary algorithms. Journal of Management Science and Engineering, 7(1), 48–66.
   Google Scholar
• Liu, W., Hou, J., Yan, X., & Tang, O. (2021). Smart logistics transformation collaboration between manufacturers and logistics service providers: A supply chain contracting perspective. Journal of Management Science and Engineering, 6(1), 25–52.
   Google Scholar
• Liu, Y. J., & Zhang, W. G. (2021). Fuzzy multi-period portfolio selection model with time-varying loss aversion. Journal of the Operational Research Society, 72(4), 935–949. https://doi.org/10.1080/01605682.2019.1705191
   Web of Science ®Google Scholar
• Malmborg, C. J. (2002). Conceptualizing tools for autonomous vehicle storage and retrieval systems. International Journal of Production Research, 40(8), 1807–1822. https://doi.org/10.1080/00207540110118668
   Web of Science ®Google Scholar
• Matthews, J., & Visagie, S. E. (2019). SKU arrangement on a unidirectional picking line. International Transactions in Operational Research, 26(1), 100–130. https://doi.org/10.1111/itor.12550
   Web of Science ®Google Scholar
• QuickTron (QT). (2021). http://www.flashhold.com/
   Google Scholar
• Roy, D., Krishnamurthy, A., Heragu, S. S., & Malmborg, C. J. (2014). Blocking effects in warehouse systems with autonomous vehicles. IEEE Transactions on Automation Science and Engineering, 11(2), 439–451. https://doi.org/10.1109/TASE.2013.2243910
   Web of Science ®Google Scholar
• Roy, D., Krishnamurthy, A., Heragu, S., & Malmborg, C. (2015). Queuing models to analyze dwell-point and cross-aisle location in autonomous vehicle-based warehouse systems. European Journal of Operational Research, 242(1), 72–87. https://doi.org/10.1016/j.ejor.2014.09.040
   Web of Science ®Google Scholar
• Roy, D., Krishnamurthy, A., Heragu, S. S., & Malmborg, C. (2017). A multi-tier linking approach to analyze performance of autonomous vehicle-based storage and retrieval systems. Computers & Operations Research, 83, 173–188. https://doi.org/10.1016/j.cor.2017.02.012
   Web of Science ®Google Scholar
• Sarbijan, M. S., & Behnamian, J. (2022). Multi-fleet feeder vehicle routing problem using hybrid metaheuristic. Computers & Operations Research, 141, 105696. https://doi.org/10.1016/j.cor.2022.105696
   Web of Science ®Google Scholar
• Shyur, C. C., & Wen, U. P. (2001). Optimizing the system of virtual paths by tabu search. European Journal of Operational Research, 129(3), 650–662.
   Google Scholar
• Sun, J., Feng, B., Xu, W. (2004). Particle swarm optimization with particles having quantum behavior. Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No. 04TH8753) (Vol. 1, pp. 325–331).
   Google Scholar
• Taleizadeh, A. A., Barzinpour, F., & Wee, H. M. (2011). Meta-heuristic algorithms to solve the fuzzy single period problem. Mathematical and Computer Modelling, 54(5–6), 1273–1285. https://doi.org/10.1016/j.mcm.2011.03.038
   Google Scholar
• Tappia, E., Roy, D., De Koster, M. B. M., & Melacini, M. (2017). Modeling, analysis, and design insights for shuttle-based compact storage systems. Social Science Electronic Publishing, 51(1), 269–295.
   Google Scholar
• Tsafarakis, S., Zervoudakis, K., & Andronikidis, A. (2021). Optimal product line design using Tabu Search. Journal of the Operational Research Society, 1–12. https://doi.org/10.1080/01605682.2021.1954486
   Web of Science ®Google Scholar
• Wang, Y. Y., Liu, Z. W., Huang, K., Mou, S. D., & Zhang, R. X. (2020). Model and solution approaches for retrieval operations in a multi-tier shuttle warehouse system. Computers & Industrial Engineering, 141, 106283. https://doi.org/10.1016/j.cie.2020.106283
   Web of Science ®Google Scholar
• Weidinger, F., Nils, B., & Dirk, B. (2018). Storage assignment with rack-moving mobile robots in KIVA warehouses. Transportation Science, 52(6), 1479–1495. https://doi.org/10.1287/trsc.2018.0826
   Web of Science ®Google Scholar
• Wu, Y. Y., Zhou, C., Ma, W. K., & Kong, X. T. R. (2020). Modelling and design for a shuttle-based storage and retrieval system. International Journal of Production Research, 58(16), 4808–4828. https://doi.org/10.1080/00207543.2019.1665202
   Web of Science ®Google Scholar
• Xu, L., Song, B., & Cao, M. (2021). An improved particle swarm optimization algorithm with adaptive weighted delay velocity. Systems Science & Control Engineering, 9(1), 188–197. https://doi.org/10.1080/21642583.2021.1891153
   Web of Science ®Google Scholar
• Yang, D., Wu, Y., & Huo, D. (2021). Research on design of cross-aisles shuttle-based storage/retrieval system based on improved particle swarm optimization. IEEE Access 9, 67786–67796. https://doi.org/10.1109/ACCESS.2021.3077974
   Web of Science ®Google Scholar
• Yen, G. G., & Ivers, B. (2009). Job shop scheduling optimization through multiple independent particle swarms. International Journal of Intelligent Computing and Cybernetics, 2(1), 5–33. https://doi.org/10.1108/17563780910939237
   Google Scholar
• Yuan, X., Wang, P., Yuan, Y., Huang, Y., & Zhang, X. (2015). A new quantum inspired chaotic artificial bee colony algorithm for optimal power flow problem. Energy Conversion and Management, 100, 1–9. https://doi.org/10.1016/j.enconman.2015.04.051
   Web of Science ®Google Scholar
• Zeng, N., Wang, Z., Liu, W., Zhang, H., & Liu, X. (2020). A dynamic neighborhood-based switching particle swarm optimization algorithm. IEEE Transactions on Cybernetics, 99, 1–12.
   Google Scholar
• Zhang, C., & Yi, Z. (2011). Scale-free fully informed particle swarm optimization algorithm. Information Sciences, 181(20), 4550–4568. https://doi.org/10.1016/j.ins.2011.02.026
   Web of Science ®Google Scholar
• Zhao, N., Luo, L., Zhang, S. P., & Gabriel, L. (2016). An efficient simulation model for rack design in multi-elevator shuttle-based storage and retrieval system. Simulation Modelling Practice and Theory, 67, 100–116.
   Web of Science ®Google Scholar
• Zhao, X., Zhang, R., Zhang, N., Wang, Y., Jin, M., & Mou, S. (2020). Analysis of the shuttle-based storage and retrieval system. IEEE Access, 8, 146154–146165.
   Google Scholar
• Zhen, L., & Li, H. (2022). A literature review of smart warehouse operations management. Frontiers of Engineering Management, 1–25.
   Google Scholar
• Zou, B. P., Xu, X. H., Gong, Y., & Koster, R. D. (2016). Modeling parallel movement of lifts and vehicles in tier-captive vehicle-based warehousing systems. European Journal of Operational Research, 254(1), 51–67. https://doi.org/10.1016/j.ejor.2016.03.039
   Web of Science ®Google Scholar