Improving aggregate production planning considering maximum inventory area and service level with demand uncertainty: a nearshoring context in Mexican companies

References

• The Logistics Word [Internet]. México: Guerrero, J; [cited 2023 April 28]. Available from: https://thelogisticsworld.com/almacenes-e-inventarios/la-solucion-innovadora-a-la-crisis-de-espacio-para-almacenamiento-en-mexico-y-latinoamerica/
   Google Scholar
• Biblioteca Tec [Internet]. México: Statista; [cited 2023 July 17]. Available from: https://0-www-statista-com.biblioteca-ils.tec.mx/statistics/1380930/industrial-leasing-space-latam-by-city/
   Google Scholar
• Lee J, Ko C, Moon I. E-commerce supply chain network design using on-demand warehousing system under uncertainty. International Int J Prod Res. 2022;1–27. doi: 10.1080/00207543.2022.2128462
   Web of Science ®Google Scholar
• Singha K, Buddhakulsomsiri J, Parthanadee P. Mathematical model of inventory policy under limited storage space for continuous and periodic review policies with backlog and lost sales. Math Prob Eng. 2017;2017:1–9. doi: 10.1155/2017/4391970
   Web of Science ®Google Scholar
• Hariga MA. A single-item continuous review inventory problem with space restriction. Int J Prod Econ. 2010;128(1):153–158. doi: 10.1016/j.ijpe.2010.06.008
   Web of Science ®Google Scholar
• Akbalik A, Penz B, Rapine C. Multi-item uncapacitated lot sizing problem with inventory bounds. Optim Lett. 2015;9(1):143–154. doi: 10.1007/s11590-014-0746-6
   Web of Science ®Google Scholar
• Liu L, Yang X. A multi-objective model and algorithms of aggregate production planning of multi-product with early and late delivery. Algorithms. 2022;15(6):182. doi: 10.3390/a15060182
   Web of Science ®Google Scholar
• Yu VF, Kao HC, Chiang FY, et al. Solving aggregate production planning problems: an extended TOPSIS approach. Appl Sci. 2022;12(14):6945. doi: 10.3390/app12146945
   Google Scholar
• Cheraghalikhani A, Khoshalhan F, Mokhtari H. Aggregate production planning: a literature review and future research directions. Int J Ind Eng Comput. 2019;10(2):309–330. doi: 10.5267/j.ijiec.2018.6.002
   Web of Science ®Google Scholar
• Jamalnia A, Yang JB, Feili A, et al. Aggregate production planning under uncertainty: a comprehensive literature survey and future research directions. Int J Adv Manuf Technol. 2019;102(1–4):159–181. doi: 10.1007/s00170-018-3151-y
   Web of Science ®Google Scholar
• Attia EA, Megahed A, AlArjani A, et al. Aggregate production planning considering organizational learning with case-based analysis. Ain Shams Eng. 2022;13(2):101575. doi: 10.1016/j.asej.2021.09.002
   Web of Science ®Google Scholar
• Olhager J, Rudberg M, Wikner J. Long-term capacity management: linking the perspectives from manufacturing strategy and sales and operations planning. Int J Prod Econ. 2001;69(2):215–225. doi: 10.1016/S0925-5273(99)00098-5
   Web of Science ®Google Scholar
• Entringer TC, da Silva Ferreira A. Proposal for a reference model for sales operations planning and aggregate planning. Int J Prod Econ. 2018;5(8):264249. doi: 10.22161/ijaers.5.8.34
   Google Scholar
• Chaturvedi ND. Minimizing energy consumption via multiple installations aggregate production planning. Clean Technol Envir. 2017;19(7):1977–1984. doi: 10.1007/s10098-017-1376-3
   Web of Science ®Google Scholar
• Mehdizadeh E, Niaki STA, Hemati M. A bi-objective aggregate production planning problem with learning effect and machine deterioration: modeling and solution. Comput Oper Res. 2018;91:21–36. doi: 10.1016/j.cor.2017.11.001
   Web of Science ®Google Scholar
• Chakrabortty RK, Akhtar Hasin MAA, Sarker RA, et al. A possibilistic environment based particle swarm optimization for aggregate production planning. CAIE. 2015;88:366–377. doi: 10.1016/j.cie.2015.07.021
   Google Scholar
• Zaidan AA, Atiya B, Abu Bakar MR, et al. A new hybrid algorithm of simulated annealing and simplex downhill for solving multiple-objective aggregate production planning on fuzzy environment. Neural Comput Appl. 2019;31(6):1823–1834. doi: 10.1007/s00521-017-3159-5
   Web of Science ®Google Scholar
• Zhu B, Hui J, Zhang F, et al. An interval programming approach for multi-period and multi-product aggregate production planning by considering the decision maker’s preference. Int J Fuzzy Syst. 2018;20(3):1015–1026. doi: 10.1007/s40815-017-0341-y
   Web of Science ®Google Scholar
• Mirzapour Al-E-Hashem SMJ, Baboli A, Sazvar Z. A stochastic aggregate production planning model in a green supply chain: considering flexible lead times, nonlinear purchase and shortage cost functions. Eur J Oper Res. 2013;230(1):26–41. doi: 10.1016/j.ejor.2013.03.033
   Web of Science ®Google Scholar
• Jamalnia A, Yang JB, Xu DL, et al. Novel decision model based on mixed chase and level strategy for aggregate production planning under uncertainty: case study in beverage industry. CAIE. 2017;114:54–68. doi: 10.1016/j.cie.2017.09.044
   Google Scholar
• Jalanko M, Mahalec V. Gasoline blend planning under demand uncertainty: aggregate supply–demand pinch algorithm with rolling horizon. Industrial & Engineering Chemistry Research. 2020;59(1):281–298. doi: 10.1021/acs.iecr.9b00733
   Web of Science ®Google Scholar
• Mirzapour Al-E-Hashem SMJ, Malekly H, Aryanezhad MB. A multi-objective robust optimization model for multi-product multi-site aggregate production planning in a supply chain under uncertainty. Int J Prod Econ. 2011;134(1):28–42. doi: 10.1016/j.ijpe.2011.01.027
   Web of Science ®Google Scholar
• Aydin NS, Tirkolaee EB. A systematic review of aggregate production planning literature with an outlook for sustainability and circularity. Environ Dev Sustain. 2022;1–42. doi: 10.1007/s10668-022-02304-8
   Web of Science ®Google Scholar
• Pereira DF, Oliveira JF, Carravilla MA. Tactical sales and operations planning: a holistic framework and a literature review of decision-making models. Int J Prod Econ. 2020;228:107–695. doi: 10.1016/j.ijpe.2020.107695
   Web of Science ®Google Scholar
• Hrabec D, Hvattum LM, Hoff A. The value of integrated planning for production, inventory, and routing decisions: a systematic review and meta-analysis. Int J Prod Econ. 2022;248:108–468. doi: 10.1016/j.ijpe.2022.108468
   Web of Science ®Google Scholar
• Tyas PKA, Bakhtiar T, Silalahi BP. Analysis of aggregate production planning problem with goal programming model. J Phys Conf Ser. 2021;1863(1):012005. doi: 10.1088/1742-6596/1863/1/012005
   Google Scholar
• Galankashi MR, Madadi N, Helmi SA, et al. A multiobjective aggregate production planning model for lean manufacturing: insights from three case studies. IEEE Trans Eng Manag. 2020;69(5):1958–1972. doi: 10.1109/TEM.2020.2995301
   Web of Science ®Google Scholar
• Islam SR, Novoa C, Jin T. Multi-facility aggregate production planning with prosumer microgrid: a two-stage stochastic program. J Clean Prod. 2022;367:132–911. doi: 10.1016/j.jclepro.2022.132911
   Web of Science ®Google Scholar
• Matos C, Sola AVH, Matias GDS, et al. Model for integrating the electricity cost consumption and power demand into aggregate production planning. Appl Sci. 2022;12(15):7577. doi: 10.3390/app12157577
   Google Scholar
• Andiappan V, Foo DCY, Tan RR. Automated targeting for green supply chain planning considering inventory storage losses, production and set-up time. J Ind Prod Eng. 2022;39(5):341–352. doi: 10.1080/21681015.2021.1991015
   Web of Science ®Google Scholar
• Sereshti N, Adulyasak Y, Jans R. The value of aggregate service levels in stochastic lot sizing problems. Omega. 2021;102:102–335. doi: 10.1016/j.omega.2020.102335
   Web of Science ®Google Scholar
• Ning Y, Pang N, Wang S, et al. An uncertain APP model with allowed stockout and service level constraint for vegetables. Symmetry. 2021;13(12):2332. doi: 10.3390/sym13122332
   Google Scholar
• Nikolaisen JB, Vågen SS, Schütz P. Solving a maritime inventory routing problem under uncertainty using optimization and simulation. Comput Manag Sci. 2023;20(1):27. doi: 10.1007/s10287-023-00459-x
   Web of Science ®Google Scholar
• Hirunwat A, Aungkulanon P, Jairueng S, et al. Aggregate production planning of ethanol-based hand sanitizer to meet rising demand during COVID-19 pandemic in Thailand. IJETT. 2022;69(6):131–135. doi: 10.14445/22315381/IJETT-V69I6P220
   Google Scholar
• Kalaf BA, Mohammed GJ, Salman MD. A new hybrid meta-heuristics algorithms to solve APP problems. J Phys Conf Ser. 2021;1897(1):012011. doi: 10.1088/1742-6596/1897/1/012011
   Google Scholar
• Dohale V, Ambilkar P, Gunasekaran A, et al. A multi-product and multi-period aggregate production plan: A case of automobile component manufacturing firm. Benchmarking. 2022;29(10):3396–3425. doi: 10.1108/BIJ-07-2021-0425
   Web of Science ®Google Scholar
• Raa B, Dullaert W, Aghezzaf EH. A matheuristic for aggregate production–distribution planning with mould sharing. Int J Prod Econ. 2013;145(1):29–37. doi: 10.1016/j.ijpe.2013.01.006
   Web of Science ®Google Scholar
• Demirel E, Özelkan EC, Lim C. Aggregate planning with flexibility requirements profile. Int J Prod Econ. 2018;202:45–58. doi: 10.1016/j.ijpe.2018.05.001
   Web of Science ®Google Scholar
• Qasim M, Wong K, Komarudin. A review on aggregate production planning under uncertainty: insights from a fuzzy programming perspective. Eng Appl Artif Intell. 2024;128:107436. doi: 10.1016/j.engappai.2023.107436
   Web of Science ®Google Scholar
• Gómez-Rocha JE, Hernández-Gress ES, Rivera-Gómez H, et al. Production planning of a furniture manufacturing company with random demand and production capacity using stochastic programming. PLoS One. 2021;16(6):e0252801. doi: 10.1371/journal.pone.0252801
   PubMed Web of Science ®Google Scholar
• Powell WB. Reinforcement learning and stochastic optimization: a unified framework for sequential decisions. New York: John Wiley & Sons. Inc; 2022.
   Google Scholar
• Shapiro A, Dentcheva D, Ruszczynski A. Lectures on stochastic programming: modeling and theory. Philadelphia: Society for Industrial and Applied Mathematics; 2021.
   Google Scholar
• Atlihan M, Cunningham K, Laude G, et al. Challenges in adding a stochastic programming/scenario planning capability to a general-purpose optimization modeling system in A long view of research and practice in operations research and management science: the past and the future. Boston (MA): Springer; 2010.
   Google Scholar
• Statista [Internet]. Mexico: average monthly rental price for industrial warehouses 2016. by City; 2022. Available from: https://www.statista.com/statistics/938357/average-monthly-rental-price-per-square-meter-industrial-warehouse-mexico-city/
   Google Scholar
• Ramaekers K, Janssens GK. On the choice of a demand distribution for inventory management models. Eur J Ind Eng. 2008;2(4):479. doi: 10.1504/EJIE.2008.018441
   Web of Science ®Google Scholar
• Miller AC III, Rice TR. Discrete approximations of probability distributions. Manage Sci. 1983;29(3):352–362. doi: 10.1287/mnsc.29.3.352
   Web of Science ®Google Scholar
• Barbiero A, Hitaj A. Approximation of continuous random variables for the evaluation of the reliability parameter of complex stress–strength models. Ann Oper Res. 2022;315(2):1–22. doi: 10.1007/s10479-021-04010-6
   PubMed Web of Science ®Google Scholar