Mohamed Salim Amri Sakhri

Comparative analysis of different crossover structures for solving a periodic inventory routing problem

• Authors Details :  
• Mohamed Salim Amri Sakhri

Journal title : International Journal of Data Science and Analytics

Publisher : Springer Science and Business Media LLC

Online ISSN : 2364-4168

535 Views Original Article

One of the most important challenges for a company is to manage its supply chain efficiently. One way to do this is to control and minimize its various logistics costs together to achieve an overall optimization of its supply network. One such system that integrates two of the most important logistics activities, namely inventory holding and transportation, is known as the inventory routing problem. Our replenishment network consists of a supplier that uses a single vehicle to distribute a single type of item during each period to a set of customers with independent and deterministic demand. The objectives considered are the management of supplier and customer inventories, the assignment of customers to replenishment periods, the determination of optimal delivery quantities to avoid customer stock-outs, the design and optimization of routes. A genetic algorithm (GA) is developed to solve our IRP. Different crossover structures are proposed and tested in two sets of reference instances. A comparison of the performance of different crossover structures was established. Then, it was used to find the most appropriate crossover structure that provides better results in a minor computation time. The obtained results prove the competitiveness of GAs compared to literature approaches, demonstrate the performance of our approach to best solve large scale instances and provide better solution quality in fast execution time.

Article DOI & Crossmark Data

DOI : https://doi.org/10.1007/s41060-021-00280-2

Article Subject Details

• Artificial Intelligence
• Business management
• Logistics management
• Supply chain

Article Keywords Details

• inventory routing problem
• supply chain
• Genetic algorithm
• Optimization

Article File

Full Text PDF

---

---

Article References

• (1). Baller, A.C., VanEe, M., Hoogeboom, M., Stougie, L.: Complexity of inventory routing problems when routing is easy. Networks 75(2), 113–123 (2020). https://doi.org/10.1002/net.21908
• (2). Alinaghian, M., Tirkolaee, E.B., Dezaki, Z.K., Hejazi, S.R., Ding, W.: An augmented Tabu search algorithm for the green inventory-routing problem with time windows. Swarm Evol. Comput. 60, 100802 (2021). https://doi.org/10.1016/j.swevo.2020.100802
• (3). Karakostas, P., Sifaleras, A., Georgiadis, M.C.: A general variable neighborhood search-based solution approach for the location-inventory-routing problem with distribution outsourcing. Comput. Chem. Eng. 126, 263–279 (2019). https://doi.org/10.1016/j.compchemeng.2019.04.015
• (4). Wong, L., Moin, N.H.: Ant colony optimization for split delivery inventory routing problem. Malays. J. Comput. Sci. 30(4), 333–348 (2017). https://doi.org/10.22452/mjcs.vol30no4.5
• (5). Nazifa, H., Lee, L.S.: Optimised crossover genetic algorithm for capacitated vehicle routing problem. Appl. Math. Model. 36(5), 2110–2117 (2012). https://doi.org/10.1016/j.apm.2011.08.010
• (6). Ruiz, E., Soto-Mendoza, V., Barbosa, A.E., Reyes, R.: Solving the open vehicle routing problem with capacity and distance constraints with a biased random key genetic algorithm. Comput. Ind. Eng. 133, 207–219 (2019). https://doi.org/10.1016/j.cie.2019.05.002
• (7). Hiassat, A.H., Diabat, A., Rahwan, I.: A genetic algorithm approach for location-inventory-routing problem with perishable products. J. Manuf. Syst. 42, 93–103 (2017). https://doi.org/10.1016/j.jmsy.2016.10.004
• (8). Azadeh, A., Elahi, S., Farahani, M.H., Nasirian, B.: A genetic algorithm-Taguchi based approach to inventory routing problem of a single perishable product with transshipment. Comput. Ind. Eng. 104, 124–133 (2017). https://doi.org/10.1016/j.cie.2016.12.019
• (9). Fakhrzad, M.B., Alidoosti, Z.: A realistic perish ability inventory management for location-inventory-routing problem based on genetic algorithm. J. Ind. Eng. Manag. Stud. 5(1), 106–121 (2018). https://doi.org/10.22116/JIEMS.2018.66507
• (10). Amri-Sakhri, M.S., Tlili, M., Korbaa, O.: A hybrid genetic algorithm for the inventory routing problem, In: IEEE/ACS 14th International Conference on Computer Systems and Applications (AICCSA’2017), 987-994 (2017). https://doi.org/10.1109/AICCSA.2017.25
• (11). Archetti, C., Bertazzi, L., Laporte, G., Speranza, M.G.: A branch-and-cut algorithm for a vendor-managed inventory-routing problem. Transp. Sci. 41, 382–391 (2007). https://doi.org/10.1287/trsc.1060.0188
• (12). Bertazzi, L., Speranza, M.G.: Inventory routing problems: an introduction. EURO J. Transp. Log. 1, 307–326 (2012). https://doi.org/10.1007/s13676-012-0016-7
• (13). Moin, N.H., Salhi, S.: Inventory routing problems: a logistical overview. J. Oper. Res. Soc. 58, 1185–1194 (2007). https://doi.org/10.1057/palgrave.jors.2602264
• (14). Andersson, H., Hoff, A., Christiansen, M., Hasle, G., Løkketangen, A.: Industrial aspects and literature survey: combined inventory management and routing. Comput. Oper. Res. 37(9), 1515–1536 (2010). https://doi.org/10.1016/j.cor.2009.11.009
• (15). Coelho, L.C., Cordeau, J.F., Laporte, G.: Consistency in multivehicle inventory routing. Transp. Res. Part C Emerg. Technol. 24, 270–287 (2012). https://doi.org/10.1016/j.trc.2012.03.007
• (16). Nadershahi, M., Neemat, M.N., Sohrabi, M.S.: A genetic Algorithm method for the inventory routing and optimal pricing in a two-echelon supply chain with demand function. Eur. J. Appl. Eng. Sci. Res. 2(1), 14–19 (2013)
• (17). Rabbani, M., Baghersad, M., Jafari, R.: A new hybrid GA-PSO method for solving multi-period inventory routing problem with considering financial decisions. J. Ind. Eng. Manag. 6(4), 909–929 (2013). https://doi.org/10.3926/jiem.629
• (18). Nevin, A.: A Genetic Algorithm on Inventory Routing Problem. Emerg. Mark. J. 3(3), 59–66 (2014)
• (19). Park, Y.B., Yoo, J.S., Park, H.S.: A genetic algorithm for the vendor-managed inventory routing problem with lost sales. Expert Syst. Appl. (2016). https://doi.org/10.1016/j.eswa.2016.01.041
• (20). Cheng, C., Qi, M., Wang, X., Zhang, Y.: Multi-period inventory routing problem under carbon emission regulations. Int. J. Prod. Econ. 182, 263–275 (2016). https://doi.org/10.1016/j.ijpe.2016.09.001
• (21). Hiassat, A., Diabat, A., Rahwan, I.: A genetic algorithm approach for location-inventory-routing problem with perishable products. J. Manuf. Syst. 42, 93–103 (2017). https://doi.org/10.1016/j.jmsy.2016.10.004
• (22). Saif-Eddine, A.S., El-Beheiry, M.M., El-Kharbotly, A.K.: Optimizing total supply chain cost in inventory location routing problem using developed hybrid genetic algorithm. Ain Shams Eng. J. 10(1), 63–76 (2019). https://doi.org/10.1016/j.asej.2018.09.002
• (23). Timajchi, A., Ale-Hashem, S.M., Rekik, Y.: Inventory routing problem for hazardous and deteriorating items in the presence of accident risk with transshipment option. Int. J. Prod. Econ. 209, 302–315 (2019). https://doi.org/10.1016/j.ijpe.2018.01.018
• (24). Golsefidi, A.H., Jokar, M.R.A.: A robust optimization approach for the production-inventory-routing problem with simultaneous pickup and delivery. Comput. Ind. Eng. 143, 106388 (2020). https://doi.org/10.1016/j.cie.2020.106388
• (25). Prins, C.: A simple and effective evolutionary algorithm for the vehicle routing problem. Comput. Oper. Res. 31(12), 1985–2002 (2004). https://doi.org/10.1016/S0305-0548(03)00158-8
• (26). Archetti, C., Bertazzi, L., Hertz, A., Speranza, M.G.: A hybrid heuristic for an inventory routing problem. INFORMS J. Comput. 24, 101–116 (2012). https://doi.org/10.1287/ijoc.1100.0439
• (27). Keshari, A., Mishra, N., Shukla, N., McGuire, S., Khorana, S.: Multiple order-up-to policy for mitigating bullwhip effect in supply chain network. Ann. Oper. Res. 269(1–2), 361–386 (2017). https://doi.org/10.1007/s10479-017-2527-y
• (28). Wu, W., Zhou, W., Lin, Y., Xie, Y., Jin, W.: A hybrid metaheuristic algorithm for location inventory routing problem with time windows and fuel consumption. Expert Syst. Appl. 166, 114034 (2021). https://doi.org/10.1016/j.eswa.2020.114034
• (29). Coelho, L.C., Cordeau, J.F., Laporte, G.: The inventory-routing problem with transshipment. Comput. Oper. Res. 39, 2537–2548 (2012). https://doi.org/10.1016/j.cor.2011.12.020

---

More Article by Mohamed Salim Amri Sakhri