Modelos de optimización para la producción de productos perecederos: una revisión de la literatura
Palabras clave:
productos perecederos, modelación lineal de enteros mixtos, secuenciación, programaci´ónBarra lateral del artículo
Contenido principal del artículo
Introducción: los problemas de secuenciación han sido ampliamente abordados por la literatura, sin embargo, la variable vida útil solo se ha incorporado recientemente a su modelación, por lo que se considera relevante conocer el tratamiento que ha tenido por parte de los investigadores.
Objetivo: se busca identificar la forma en que se ha incluido la consideración de vida útil en las decisiones de secuenciación de la producción y los métodos para su tratamiento.
Materiales y métodos: se realizó una búsqueda de literatura en las bases de datos SCOPUS y WEB OF SCIENCE, sin límites de tiempo, de la cual se obtuvieron 272 documentos que, una vez seleccionados, dejaron un grupo de 36 artículos para el análisis.
Resultados: La revisión permitió identificar cinco casos de agregación de decisiones entre los que sobresale la definición de distribución y ruteo asociado a decisiones de programación, así como tres formas en las cuales se incorpora la condición de perecibilidad de los productos o materias primas en los modelos. También se encuentra que la programación lineal de enteros mixtos es la metodología más usada en los casos presentados.
Conclusiones: La revisión ratifica la importancia que ha ganado la incorporación de la característica de perecibilidad en los problemas de programación de la producción. Adicionalmente, por el riesgo de pérdidas propio de estos entornos, las decisiones de programación suelen ir asociadas a decisiones de tamaño de lote, diseño de rutas y estrategias de costo que buscan minimizar las pérdidas o mejorar la utilidad o la satisfacción del consumidor.
1. Monahan JP. Production scheduling of perishable products. International Journal of Production Research. 1976;14(6):689-697
https://doi.org/10.1080/00207547608956387
2. Hariga M. Optimal inventory policies for perishable items with time-dependent demand. International Journal of Production Economics. 1997;50(1):35-41.
https://doi.org/10.1016/S0925-5273(97)00006-6
3. Mason AN., Villalobos JR. Coordination of perishable crop production using auction mechanisms. Agricultural Systems. 2015; 138:18-30.
https://doi.org/10.1016/j.agsy.2015.04.008
4. Vahdani B, Niaki STA, Aslanzade S. Production-inventory-routing coordination with capacity and time window constraints for perishable products: Heuristic and meta-heuristic algorithms. Journal of Cleaner Production. 2017; 161:598-618.
https://doi.org/10.1016/j.jclepro.2017.05.113
5. Shadkam E., Irannezhad E. A comprehensive review of simulation optimization methods in agricultural supply chains and transition towards an agent-based intelligent digital framework for agriculture 4.0. Engineering Applications of Artificial Intelligence. 2025
https://doi.org/10.2139/ssrn.4791793
6. Lin X, Negenborn RR, Lodewijks G. Predictive quality-aware control for scheduling of potato starch production. Computers and Electronics in Agriculture. 2018; 150(February):266-278.
https://doi.org/10.1016/j.compag.2018.04.020
7. Amorim, P., Meyr, H., Almeder, C., & Almada-Lobo, B. (2013). Managing perishability in production-distribution planning: A discussion and review. Flexible Services and Manufacturing Journal, 25(3), 389-413
https://doi.org/10.1007/s10696-011-9122-3
8. Chen S, Berretta R, Clark A, Moscato P. Lot Sizing and Scheduling for Perishable Food Products: A Review. In Reference Module in Food Science. 2019.
https://doi.org/10.1016/B978-0-08-100596-5.21444-3
9. Soto-Silva WE, Nadal-Roig E, González-Araya MC, Pla-Aragones LM. Operational research models applied to the fresh fruit supply chain. European Journal of Operational Research. 2016; 251(2):345-355.
https://doi.org/10.1016/j.ejor.2015.08.046
10. Neumann K, Schwindt C, Trautmann, N. Advanced production scheduling for batch plants in process industries. Or Spectrum. 2002; 251-279.
https://doi.org/10.1007/s00291-002-0100-8
11. Lin GC, Kroll DE, Lin CJ. Determining a common production cycle time for an economic lot scheduling problem with deteriorating items. European Journal of Operational Research. 2006; 173(2):669-682.
https://doi.org/10.1016/j.ejor.2005.03.014
12. Sel Ç, Bilgen B, Bloemhof-Ruwaard J. Planning and scheduling of the make-and-pack dairy production under lifetime uncertainty. Applied Mathematical Modelling. 2017; 51:129-144.
https://doi.org/10.1016/j.apm.2017.06.002
13. Soler WAO, Santos MO, Akartunalı K. MIP approaches for a lot sizing and scheduling problem on multiple production lines with scarce resources, temporary workstations, and perishable products. Journal of the Operational Research Society. 2019; 0(0):1-16.
https://doi.org/10.1080/01605682.2019.1640588
14. Yao MJ, Huang JX. Solving the economic lot scheduling problem with deteriorating items using genetic algorithms. Journal of Food Engineering. 2005; 70(3):309-322.
https://doi.org/10.1016/j.jfoodeng.2004.05.077
15. Pahl J, Voß S. Discrete lot-sizing and scheduling including deterioration and perishability constraints. Lecture Notes in Business Information Processing. 2010; 46 LNBI:345-357.
https://doi.org/10.1007/978-3-642-12494-5_31
16. Pahl J, Voß S, Woodruff D. Discrete lot-sizing and scheduling with sequence-dependent setup times and costs including deterioration and perishability constraints. Lecture Notes in Business Information Processing, 2011; 345-357.
https://doi.org/10.1007/978-3-642-12494-5_31
17. Amorim P, Costa AM, Almada-Lobo B. A hybrid path-relinking method for solving two-stage stochastic integer problems. International Transactions in Operational Research. 2015; 22(1):113-127.
https://doi.org/10.1111/itor.12084
18. Tempelmeier H, Copil K. Capacitated lot sizing with parallel machines, sequence-dependent setups, and a common setup operator. OR Spectrum. 2016; 38(4): 819-847.
https://doi.org/10.1007/s00291-015-0410-2
19. Amorim P, Antunes CH, Almada-Lobo, B. Multi-objective lot-sizing and scheduling dealing with perishability issues. Industrial and Engineering Chemistry Research. 2011;50(6): 3371-3381.
https://doi.org/10.1021/ie101645h
20. Amorim P, Belo-Filho MAF, Toledo, FMB, Almeder C, Almada-Lobo B. Lot sizing versus batching in the production and distribution planning of perishable goods. International Journal of Production Economics. 2013; 146(1):208-218.
https://doi.org/10.1016/j.ijpe.2013.07.001
21. Belo-Filho MAF, Amorim P, Almada-Lobo, B. An adaptive large neighbourhood search for the operational integrated production and distribution problem of perishable products. International Journal of Production Research. 2015; 53(20): 6040-6058.
https://doi.org/10.1080/00207543.2015.1010744
22. Alipour Z, Jolai F, Monabbati E, Zaerpour N. General lot-sizing and scheduling for perishable food products. RAIRO - Operations Research. 2020; 54(3): 913-931.
https://doi.org/10.1051/ro/2019021
23. Govindan K, Jafarian A, Khodaverdi R, Devika K. Two-echelon multiple-vehicle location-routing problem with time windows for optimization of sustainable supply chain network of perishable food. International Journal of Production Economics. 2014
https://doi.org/10.1016/j.ijpe.2013.12.028
24. Rahbari A, Nasiri MM, Werner F, Musavi MM, Jolai F. The vehicle routing and scheduling problem with cross-docking for perishable products under uncertainty: Two robust bi-objective models. Applied Mathematical Modelling. 2019; 70: 605-625.
https://doi.org/10.1016/j.apm.2019.01.047
25. Tavallali PA, Feylizadeh MR, Amindoust A. Presenting a mathematical programming model for routing and scheduling of cross-dock and transportation. Polish Journal of Management Studies. 2020; 22(1): 545-564
https://doi.org/10.17512/pjms.2020.22.1.35
26. Chen HK, Hsueh CF, Chang MS. Production scheduling and vehicle routing with time windows for perishable food products. Computers and Operations Research. 2009; 36(7): 2311-2319.
https://doi.org/10.1016/j.cor.2008.09.010
27. Amorim P, Günther HO, Almada-Lobo B. Multi-objective integrated production and distribution planning of perishable products. International Journal of Production Economics. 2012; 138(1): 89-101.
https://doi.org/10.1016/j.ijpe.2012.03.005
28. Bilgen B, Çelebi Y. Integrated production scheduling and distribution planning in dairy supply chain by hybrid modelling. Annals of Operations Research. 2013; 211(1): 55-82.
https://doi.org/10.1007/s10479-013-1415-3
29. Lacomme P, Moukrim A, Quilliot A, Vinot M. Supply chain optimisation with both production and transportation integration: multiple vehicles for a single perishable product. International Journal of Production Research. 2018; 56(12): 4313-4336.
https://doi.org/10.1080/00207543.2018.1431416
30. Guarnaschelli A, Salomone HE, Méndez CA. A stochastic approach for integrated production and distribution planning in dairy supply chains. Computers and Chemical Engineering. 2020; 140:106-966.
https://doi.org/10.1016/j.compchemeng.2020.106966
31. Jafari NF, Behnamian J. Hyper-heuristic for integrated due-window scheduling and vehicle routing problem for perishable products considering production quality. Engineering Optimization. 2020; 0(0): 1-20.
https://doi.org/10.1080/0305215X.2020.1837792
32. Manouchehri F, Nookabadi AS, Kadivar M. Production routing in perishable and quality degradable supply chains. Heliyon. 2020; 6(2): e03376.
https://doi.org/10.1016/j.heliyon.2020.e03376
33. Aazami A, Saidi-Mehrabad M, Seyedhosseini SM. A bi-objective robust optimization model for an integrated production-distribution problem of perishable goods with demand improvement strategies: A case study. International Journal of Engineering.Transactions A: Basics. 2021; 34(7): 1766-1777.
https://doi.org/10.5829/ije.2021.34.07a.21
34. Solina V, Mirabelli G. Integrated production-distribution scheduling with energy considerations for efficient food supply chains. Procedia Computer Science. 2021
https://doi.org/10.1016/j.procs.2021.01.355
35. Ghasemkhani A, Tavakkoli-Moghaddam R, Rahimi Y, Shahnejat-Bushehri S, Tavakkoli-Moghaddam H. Integrated production-inventory-routing problem for multi-perishable products under uncertainty by meta-heuristic algorithms. International Journal of Production Research. 2022; 60(9): 2766-2786.
https://doi.org/10.1080/00207543.2021.1902013
36. Mousavi R, Bashiri M, Nikzad E. Stochastic production routing problem for perishable products: Modeling and a solution algorithm. Computers and Operations Research. 2022
https://doi.org/10.1016/j.cor.2022.105725
37. Lejarza F, Baldea M. An efficient optimization framework for tracking multiple quality attributes in supply chains of perishable products. European Journal of Operational Research. 2022; 297(3): 890-903.
https://doi.org/10.1016/j.ejor.2021.04.057
38. Hashemi-Amiri O, Ghorbani F, Ji R. Integrated supplier selection, scheduling, and routing problem for perishable product supply chain: A distributionally robust approach. Computers and Industrial Engineering. 2023; 175: 108845.
https://doi.org/10.1016/j.cie.2022.108845
39. Sun H, Sun S, Zhou Y, Xue Y. Trade-offs between economic and environmental goals of production-inventory-routing problem for multiple perishable products. Computers and Industrial Engineering. 2023; 178(3663): 109133.
https://doi.org/10.1016/j.cie.2023.109133
40. Li N. A Two-stage Algorithm for Production Distribution Optimization of Fresh Products. International Journal of Industrial Engineering: Theory, Applications and Practice. 2025; 32(1).
https://doi.org/10.23055/ijietap.2025.32.1.10119
41. Eskandar H, Sadollah A, Bahreininejad A, Hamdi M. Water cycle algorithm - A novel metaheuristic optimization method for solving constrained engineering optimization problems. Computers and Structures. 2012; 110-111: 151-166.
https://doi.org/10.1016/j.compstruc.2012.07.010
42. Prins C. A GRASP × evolutionary local search hybrid for the vehicle routing problem. Studies in Computational Intelligence. 2009; 161: 35-53.
https://doi.org/10.1007/978-3-540-85152-3_2
43. Borodin V, Bourtembourg J, Hnaien F, Labadie N. Handling uncertainty in agricultural supply chain management: A state of the art. European Journal of Operational Research. 2016; 254(2): 348-359.
https://doi.org/10.1016/j.ejor.2016.03.057
44. Schouten R, Van Kooten O, Van Der Vorst J, Marcelis W, Luning P. Quality Controlled Logistics in vegetable supply chain networks: How can an individual batch reach an individual consumer in the optimal state? Acta Horticulturae. 2012; 936: 45-52.
https://doi.org/10.17660/ActaHortic.2012.936.4
45. Entrup ML, Günther HO, Van Beek P, Grunow M, Seiler, T. Mixed-integer linear programming approaches to shelf-life-integrated planning and scheduling in yoghurt production. International Journal of Production Research 2005; 43(23): 5071-5100.
https://doi.org/10.1080/00207540500161068
46. Singh NK, Kuthambalayan TS. Integrating operations and marketing decisions to manage perishability risks with target minimum remaining shelf-life available to consumers. Computers and Industrial Engineering. 2022; 163: 107812.
https://doi.org/10.1016/j.cie.2021.107812
47. Steinbacher LM, Rippel D, Schulze P, Rohde AK, Freitag M. Quality-based scheduling for a flexible job shop. Journal of Manufacturing Systems. 2023; 70(June): 202-216.
https://doi.org/10.1016/j.jmsy.2023.07.005
48. Sel C, Bilgen B, Bloemhof-Ruwaard JM, van der Vorst JGAJ. Multi-bucket optimization for integrated planning and scheduling in the perishable dairy supply chain. Computers and Chemical Engineering. 2015; 77: 59-73.
https://doi.org/10.1016/j.compchemeng.2015.03.020
49. Claassen GDH, Gerdessen JC, Hendrix EMT, van der Vorst JGAJ. On production planning and scheduling in food processing industry:Modelling non-triangular setups andproduct decay. Computers and Operations Research. 2016; 76: 147-154.
https://doi.org/10.1016/j.cor.2016.06.017
50. Wei W, Amorim P, Guimarães L, Almada-Lobo B. Tackling perishability in multi-level process industries. International Journal of Production Research. 2018; 57(17): 5604-5623.
https://doi.org/10.1080/00207543.2018.1554916
51. Buisman ME, Haijema R, Akkerman R, Bloemhof JM. Donation management for menu planning at soup kitchens. European Journal of Operational Research. 2018; 272(1): 324-338.
https://doi.org/10.1016/j.ejor.2018.06.005
52. Cai XQ, Chen J, Xiao YB, Xu XL. Product selection, machine time allocation, and scheduling decisions for manufacturing perishable products subject to a deadline. Computers and Operations Research. 2008; 35(5): 1671-1683.
Downloads
Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-CompartirIgual 4.0.
Los autores que publican en esta revista están de acuerdo con los siguientes términos:
Los autores ceden los derechos patrimoniales a la revista y a la Universidad del Valle sobre los manuscritos aceptados, pero podrán hacer los reusos que consideren pertinentes por motivos profesionales, educativos, académicos o científicos, de acuerdo con los términos de la licencia que otorga la revista a todos sus artículos.
Los artículos serán publicados bajo la licencia Creative Commons 4.0 BY-NC-SA (de atribución, no comercial, sin obras derivadas).