The warehouse automation market has experienced significant growth due to rising demand and the necessity for quick responses to customer needs. The adoption of Automated Storage and Retrieval System (AS/RS) aims to enhance operational efficiency and expedite order fulfilment, although environmental considerations are frequently overlooked. This study introduces the implementation of a regenerative braking system (RBS) on AS/RS to minimize the carbon emission impact of the system. Various storage configurations, including storage classification, slot selection, retrieval selection, and multiple I/O points, are examined to identify the best solution in the face of supply-demand uncertainties, under travel time, response time, and carbon emission as performance indicators. This study employs a discrete-event simulation approach, revealing that the implementation of RBS can result in an average energy saving of 13.21% or equal to additional travel range of 28,800 meters. This indicates that RBS is suitable for adoption in the AS/RS system to reduce carbon emissions. Furthermore, statistical tests demonstrate that main effect and interaction between storage assignment and I/O point significantly impact performance indicators, with the best solution is identified by utilizing desirability function analysis, involving the application of AS/RS configuration with a single-side I/O point, non-class storage classification, closest open location with column-order slot selection, and closest open location with row-order retrieval selection.

[1] S. Tiwari, “Smart warehouse: A bibliometric analysis and future research direction,” Sustain. Manuf. Serv. Econ., vol. 2, no. March, p. 100014, 2023, doi: 10.1016/j.smse.2023.100014.

[2] R. B. M. De Koster, A. L. Johnson, and D. Roy, “Warehouse design and management,” Int. J. Prod. Res., vol. 55, no. 21, pp. 6327–6330, 2017, doi: 10.1080/00207543.2017.1371856.

[3] MMH Staff, “Interact Analysis: warehouse automation market to expand to over $69 billion by 2025,” Supply Chain Management Review, 2021. https://www.scmr.com/article/interact_analysis_warehouse_automation_market_to_expand_to_over_69_billion.

[4] J. M. Ries, E. H. Grosse, and J. Fichtinger, “Environmental impact of warehousing: a scenario analysis for the United States,” Int. J. Prod. Res., vol. 55, no. 21, pp. 6485–6499, 2017, doi: 10.1080/00207543.2016.1211342.

[5] T. Lerher, M. Edl, and B. Rosi, “Energy efficiency model for the mini-load automated storage and retrieval systems,” Int. J. Adv. Manuf. Technol., vol. 70, no. 1–4, pp. 97–115, 2014, doi: 10.1007/s00170-013-5253-x.

[6] A. Meneghetti, E. Dal Borgo, and L. Monti, “Decision support optimisation models for design of sustainable automated warehouses,” Int. J. Shipp. Transp. Logist., vol. 7, no. 3, pp. 266–294, 2015, doi: 10.1504/IJSTL.2015.069127.

[7] A. Meneghetti, E. Dal Borgo, and L. Monti, “Rack shape and energy efficient operations in automated storage and retrieval systems,” Int. J. Prod. Res., vol. 53, no. 23, pp. 7090–7103, 2015, doi: 10.1080/00207543.2015.1008107.

[8] A. Meneghetti and L. Monti, “Multiple-weight unit load storage assignment strategies for energy-efficient automated warehouses,” Int. J. Logist. Res. Appl., vol. 17, no. 4, pp. 304–322, 2014, doi: 10.1080/13675567.2013.861896.

[9] E. Tappia, G. Marchet, M. Melacini, and S. Perotti, “Incorporating the environmental dimension in the assessment of automated warehouses,” Prod. Plan. Control, vol. 26, no. 10, pp. 824–838, 2015, doi: 10.1080/09537287.2014.990945.

[10] J. Dhooma and P. Baker, “An exploratory framework for energy conservation in existing warehouses,” Int. J. Logist. Res. Appl., vol. 15, no. 1, pp. 37–51, 2012, doi: 10.1080/13675567.2012.668877.

[11] A. Kolinski and B. Sliwczynski, “Evaluation Problem and Assessment Method of Warehouse Process Efficiency,” Proc. 15th Int. Sci. Conf. Bus. Logist. Mod. Manag., no. Proc. 15th Int. Sci. Conf. Bus. Logist. Mod. Manag., pp. 175–188, 2015, [Online]. Available: https://hrcak.srce.hr/ojs/index.php/plusm/article/view/3880.

[12] P. Baker and Z. Halim, “An exploration of warehouse automation implementations: Cost, service and flexibility issues,” Supply Chain Manag., vol. 12, no. 2, pp. 129–138, 2007, doi: 10.1108/13598540710737316.

[13] J. Rowley, I. Thomson, and P. Chroucher, The Principles of Warehouse Design, 2nd ed. UK: Institute of Logistics & Transport, 2000.

[14] R. B. M. de Koster, “Automated and Robotic Warehouses: Developments and Research Opportunities,” Logist. Transp., vol. 38, no. 2, p. 33, 2018, doi: 10.26411/83-1734-2015-2-38-4-18.

[15] V. Bansal, D. Roy, and J. A. Pazour, “Performance analysis of batching decisions in waveless order release environments for e-commerce stock-to-picker order fulfillment,” Int. Trans. Oper. Res., vol. 28, no. 4, pp. 1787–1820, 2021, doi: 10.1111/itor.12921.

[16] Y. A. Bozer and J. A. White, “Travel-time models for automated storage/retrieval systems,” no. November 2012, pp. 37–41, 2007.

[17] R. Manzini, Warehousing in the global supply chain: Advanced models, tools and applications for storage systems, vol. 9781447122. 2012.

[18] H. M., K. A., and A. T., “The Automatic Storage and Retrieval System: An Overview,” Int. J. Comput. Appl., vol. 177, no. 16, pp. 36–43, 2019, doi: 10.5120/ijca2019919603.

[19] K. Y. Hu and T. S. Chang, “An innovative automated storage and retrieval system for B2C e-commerce logistics,” Int. J. Adv. Manuf. Technol., vol. 48, no. 1–4, pp. 297–305, 2010, doi: 10.1007/s00170-009-2292-4.

[20] K. Lewczuk, “The study on the automated storage and retrieval system dependability,” Eksploat. i Niezawodn., vol. 23, no. 4, pp. 709–718, 2021, doi: 10.17531/ein.2021.4.13.

[21] A. Hoshimov, A. C. Cagliano, G. Mangano, M. Schenone, and S. Grimaldi, “AS/R system travel time in class-based storage with different input-output point levels: a proposed formula,” J. Facil. Manag., 2022, doi: 10.1108/JFM-11-2021-0145.

[22] J. S. Morgan, S. Howick, and V. Belton, “A toolkit of designs for mixing Discrete Event Simulation and System Dynamics,” Eur. J. Oper. Res., vol. 257, no. 3, pp. 907–918, 2017, doi: 10.1016/j.ejor.2016.08.016.

[23] J. Karnon and H. Haji Ali Afzali, “When to use Discrete Event Simulation (DES) for the economic evaluation of health technologies? A review and critique of the costs and benefits of DES,” Pharmacoeconomics, vol. 32, no. 6, pp. 547–558, 2014, doi: 10.1007/s40273-014-0147-9.

[24] F. Fontanili, A. Vincent, and R. Ponsonnet, “Flow simulation and genetic algorithm as optimization tools,” Int. J. Prod. Econ., vol. 64, no. 1, pp. 91–100, 2000, doi: 10.1016/S0925-5273(99)00047-X.

[25] T. Lehmann and J. Hußmann, “Travel time model for multi-deep automated storage and retrieval systems with different storage strategies,” Int. J. Prod. Res., vol. 61, no. 16, pp. 5676–5691, 2023, doi: 10.1080/00207543.2022.2110536.

[26] H. P. Hsu, C. N. Wang, and T. T. Dang, “Simulation-based optimization approaches for dealing with dual-command crane scheduling problem in Unit-load double-deep AS/RS considering energy Consumption,” Mathematics, vol. 10, no. 21, 2022, doi: 10.3390/math10214018.

[27] J. Valladolid, M. Calle, and A. Guiracocha, “Analysis of regenerative braking efficiency in an electric vehicle through experimental tests,” Ingenius, vol. 2023, no. 29, pp. 24–31, 2023, doi: 10.17163/ings.n29.2023.02.

[28] S. Heydari and P. Fajri, “Maximizing Energy Harvesting in Electric Vehicles through Optimal Regenerative Braking Utilization Doctor of Philosophy,” no. December, p. 97, 2020.

[29] S. Vasiljević, B. Aleksandrović, J. Glišović, and M. Maslać, “Regenerative braking on electric vehicles: working principles and benefits of application,” IOP Conf. Ser. Mater. Sci. Eng., vol. 1271, no. 1, p. 012025, 2022, doi: 10.1088/1757-899x/1271/1/012025.

[30] T. Grigoratos and G. Martini, “Brake wear particle emissions: a review,” Environ. Sci. Pollut. Res., vol. 22, no. 4, pp. 2491–2504, 2015, doi: 10.1007/s11356-014-3696-8.

[31] L. N. Deepika, R. Sandhyaa, K. D. Pooja, and P. S. Lakshmi, “Energy efficient electric vehicle using eegenerative braking system,” Int. J. Adv. Res. Idea Innov. Technol., vol. 3, no. 3, pp. 55–58, 2017.

[32] A. K. Namdeo, G. Mitchell, and R. Dixon, “White Rose Research Online: A Review of Regenerative Braking System,” Bilingualism, vol. 110, pp. 115–122, 2009.

[33] B. Güney and H. Kiliç, “Research on Regenerative Braking Systems: A Review,” Int. J. Sci. Res. , vol. 9, no. 9, pp. 160–166, 2020, doi: 10.21275/SR20902143703.

[34] E. Guerrazzi, V. Mininno, D. Aloini, R. Dulmin, C. Scarpelli, and M. Sabatini, “Energy evaluation of deep-lane autonomous vehicle storage and retrieval system,” Sustain., vol. 11, no. 14, pp. 1–15, 2019, doi: 10.3390/su11143817.

[35] B. Jerman, N. Zrnić, M. Jenko, and T. Lerher, “Energy regeneration in automated high bay warehouse with stacker cranes,” Teh. Vjesn. - Tech. Gaz., vol. 24, no. 5, pp. 1411–1416, 2017, doi: 10.17559/tv-20161219112306.

[36] G. Zhou and L. Mao, “Design and simulation of storage location optimization module in AS / RS based on FLEXSIM,” I.J. Intelligent Syst. Appl., vol. 2, no. December, pp. 33–40, 2010.

[37] S. Brezovnik, J. Gotlih, J. Balič, K. Gotlih, and M. Brezočnik, “Optimization of an automated storage and retrieval systems by swarm intelligence,” Procedia Eng., vol. 100, no. January, pp. 1309–1318, 2015, doi: 10.1016/j.proeng.2015.01.498.

[38] M. Bortolini, M. Faccio, E. Ferrari, M. Gamberi, and F. Pilati, “Time and energy optimal unit-load assignment for automatic S/R warehouses,” Int. J. Prod. Econ., vol. 190, pp. 133–145, 2017, doi: 10.1016/j.ijpe.2016.07.024.

[39] M. Rajković, N. Zrnić, N. Kosanić, M. Borovinšek, and T. Lerher, “A Multi-objective optimization model for minimizing cost, travel time and CO2 emission in an AS/RS,” FME Trans., vol. 45, no. 4, pp. 620–629, 2017, doi: 10.5937/fmet1704620R.

[40] J. P. Gagliardi, J. Renaud, and A. Ruiz, “On storage assignment policies for unit-load automated storage and retrieval systems,” Int. J. Prod. Res., vol. 50, no. 3, pp. 879–892, 2012, doi: 10.1080/00207543.2010.543939.

[41] J. P. Van Den Berg and A. J. R. M. Gademann, “Simulation study of an automated storage/retrieval system,” Int. J. Prod. Res., vol. 38, no. 6, pp. 1339–1356, 2000, doi: 10.1080/002075400188889.

[42] X. Xu, X. Zhao, B. Zou, Y. Gong, and H. Wang, “Travel time models for a three-dimensional compact AS/RS considering different I/O point policies,” Int. J. Prod. Res., vol. 58, no. 18, pp. 5432–5455, 2020, doi: 10.1080/00207543.2019.1659519.

[43] Y. B. Song and H. B. Mu, “Large-scale storage/retrieval requests sorting algorithm for multi-I/O depots automated storage/retrieval systems,” Discret. Dyn. Nat. Soc., vol. 2021, p. 16, 2021, doi: 10.1155/2021/6646180.

[44] L. Systems, “AISLE : analytical integrated software for a logistics environment Gary P Nandagopal Padmanabhan,” pp. 78–95, 2006.

[45] S. U. Randhawa and R. Shroff, “Simulation-based design evaluation of unit load automated storage/retrieval systems,” Comput. Ind. Eng., vol. 28, no. 1, pp. 71–79, 1995, doi: 10.1016/0360-8352(94)00027-K.

[46] S. Hsieh and K. C. Tsai, “A BOM oriented class-based storage assignment in an automated storage/retrieval system,” Int. J. Adv. Manuf. Technol., vol. 17, no. 9, pp. 683–691, 2001, doi: 10.1007/s001700170134.

[47] Y. Song, “Integrated optimization of input / output point assignment and twin stackers scheduling in multi-input / output points automated storage and retrieval system by ant colony algorithm,” Math. Probl. Eng., vol. 2022, p. 18, 2022, doi: doi.org/10.1155/2022/5997095.

[48] R. Accorsi, M. Bortolini, M. Gamberi, R. Manzini, and F. Pilati, “Multi-objective warehouse building design to optimize the cycle time , total cost , and carbon footprint,” pp. 839–854, 2017, doi: 10.1007/s00170-017-0157-9.

[49] M. Schenone, G. Mangano, S. Grimaldi, and A. C. Cagliano, “An approach for computing AS/R systems travel times in a class-based storage configuration,” Prod. Manuf. Res., vol. 8, no. 1, pp. 273–290, 2020, doi: 10.1080/21693277.2020.1781703.

[50] P. Yang, K. Yang, M. Qi, L. Miao, and B. Ye, “Designing the optimal multi-deep AS/RS storage rack under full turnover-based storage policy based on non-approximate speed model of S/R machine,” Transp. Res. Part E Logist. Transp. Rev., vol. 104, pp. 113–130, 2017, doi: 10.1016/j.tre.2017.05.010.

[51] M. Choy and M. L. F. Cheong, “Identification of demand through statistical distribution modeling for improved demand forecasting,” pp. 1–14, 2011, [Online]. Available: https://arxiv.org/ftp/arxiv/papers/1110/1110.0062.pdf.

[52] S. Robinson, Simulation : the practice of model development and use / Stewart Robinson. 2004.

[53] R. G. Sargent, “Verification and validation of simulation models,” Proc. - Winter Simul. Conf., pp. 166–183, 2010, doi: 10.1109/WSC.2010.5679166.

[54] E. C. Harrington, “The desirability function,” Ind. Qual. Control, vol. 21, no. 10, pp. 494–498, 1965, [Online]. Available: https://www.scirp.org/reference/ReferencesPapers?ReferenceID=1542744.

[55] H. W. Chen, W. K. Wong, and H. Xu, “An augmented approach to the desirability function,” J. Appl. Stat., vol. 39, no. 3, pp. 599–613, 2012, doi: 10.1080/02664763.2011.605437.

[56] Ministry of Economic Fair of Taiwan, “Electricity carbon emission factor,” 2022. https://www.moeaea.gov.tw/ecw/english/content/Content.aspx?menu_id=24200 (accessed Oct. 10, 2023).