Solving Inventory Routing Problems with Crow Search Algorithm

Subject Areas : Metaheuristics

Maria Krisnawati ¹ , Ayu Chris Monita ² , Amanda Sofiana ^{3 *}

1 - Department of Industrial Engineering, Universitas Jenderal Soedirman, Purwokerto, Indonesia
2 - Department of Industrial Engineering, Universitas Jenderal Soedirman, Purwokerto, Indonesia
3 - Department of Industrial Engineering, Universitas Jenderal Soedirman, Purwokerto, Indonesia

Received: 2025-02-09 Accepted : 2025-07-05 Published : 2025-07-04

Keywords: Crow search algorithm, Inventory cost, Inventory routing problem, Transportation costs.,

Abstract :

The intensifying competition in the business environment compels companies to proactively retain customers by minimizing costs across all aspects while upholding product or service quality. Given the pivotal role of inventory control in cost optimization, a good management of inventory levels becomes priority. Employing a strategic approach within supply chain management, specifically the Inventory Routing Problem (IRP), proves instrumental in achieving an optimal balance in inventory that subsequently impacts cost minimization. The IRP integrates inventory management with vehicle routing to enhance efficiency. In this research, the IRP was applied through the application of the Crow Search Algorithm (CSA), a method inspired by the foraging and caching behaviors of crows. By emulating these natural habits, the CSA method aims to deliver an optimal distribution schedule that minimizes both inventory and transportation costs. The study relied on secondary data in the form of a comprehensive database encompassing relevant data and solution outcomes. The solutions derived from the database serve as a benchmark for evaluating the efficacy of the CSA method. The findings of the research revealed that the total cost incurred through the implementation of the CSA method is notably lower than that obtained from the database results. Furthermore, the simulation results indicate no significant difference between the CSA method and the database, affirming the applicability of the CSA method for effectively addressing IRP.

References:

[1] P. Ghemawat, “Competition and Business Strategy in Historical Perspective,” Bus. Hist. Rev., vol. 76, no. 1, pp. 37–74, 2002, doi: 10.2307/4127751.
[2] K. Khadka and S. Maharjan, “CUSTOMER SATISFACTION AND CUSTOMER LOYALTY,” CENTRIA Univ. Appl. Sci., no. November, pp. 21–36, 2017, doi: 10.4337/9781781955970.00008.
[3] J. Heizer and B. Render, “Operations Management,” in Salemba Empat, 2014.
[4] C. Kwak, J. Sung, C. Ouk, and I. Kwon, “Expert Systems with Applications Situation reactive approach to Vendor Managed Inventory problem,” Expert Syst. Appl., vol. 36, no. 5, pp. 9039–9045, 2009, doi: 10.1016/j.eswa.2008.12.018.
[5] David Simchi-Levi, P. Kaminsky, and E. Simchi-Levi, Design and Managing The Suppl Chain Concepts Strategies and Case Studies. 2000.
[6] S. H. R. Pasandideh, S. T. A. Niaki, and A. R. Nia, “A genetic algorithm for vendor managed inventory control system of multi-product multi-constraint economic order quantity model,” Expert Syst. Appl., vol. 38, no. 3, pp. 2708–2716, 2011, doi: 10.1016/j.eswa.2010.08.060.
[7] D. Choudhary, R. Shankar, P. K. Dey, H. Chaudhary, and L. S. Thakur, “Benefits of retailer-supplier partnership initiatives under time-varying demand: A comparative analytical study,” Int. J. Prod. Res., vol. 52, no. 14, pp. 4279–4298, 2014, doi: 10.1080/00207543.2013.879615.
[8] S. M. J. Mirzapour Al-e-Hashem and Y. Rekik, “Multi-product multi-period Inventory Routing Problem with a transshipment option: A green approach,” Int. J. Prod. Econ., vol. 157, no. 1, pp. 80–88, 2014, doi: 10.1016/j.ijpe.2013.09.005.
[9] N. H. Moin, S. Salhi, and N. A. B. Aziz, “An efficient hybrid genetic algorithm for the multi-product multi-period inventory routing problem,” Int. J. Prod. Econ., vol. 133, no. 1, pp. 334–343, 2011, doi: 10.1016/j.ijpe.2010.06.012.
[10] O. Guemri, A. Bekrar, B. Beldjilali, and D. Trentesaux, “GRASP-based heuristic algorithm for the multi-product multi-vehicle inventory routing problem,” 4or, vol. 14, no. 4, pp. 377–404, 2016, doi: 10.1007/s10288-016-0315-1.
[11] A. J. Kleywegt, V. S. Nori, and M. W. P. Savelsbergh, “The stochastic inventory routing problem with direct deliveries,” Transp. Sci., vol. 36, no. 1, pp. 94–118, 2002, doi: 10.1287/trsc.36.1.94.574.
[12] M. T. K. dan Transmigrasi, “Peraturan Menteri Tenaga Kerja dan Transmigrasi Nomor Per.13/Men/X/2011 Tentang Nilai Ambang Batas Faktor Fisika dan Faktor Kimia di Tempat Kerja Tahun 2011,” Peratur. Menteri Tenaga Kerja Dan Transm., 2016.
[13] A. P. S, “Nature Inspired Metaheuristic Algorithms,” pp. 306–309, 2017.
[14] A. Askarzadeh, “A novel metaheuristic method for solving constrained engineering optimization problems : Crow search algorithm,” Comput. Struct., vol. 169, pp. 1–12, 2016, doi: 10.1016/j.compstruc.2016.03.001.
[15] A. Adhi, B. Santosa, and N. Siswanto, “A meta-heuristic method for solving scheduling problem : crow search algorithm A meta-heuristic method for solving scheduling problem : crow search algorithm,” 2018, doi: 10.1088/1757-899X/337/1/012003.
[16] N. Siswanto, A. N. Adianto, H. A. Prawira, and A. Rusdiansyah, “A crow search algorithm for aircraft maintenance check problem and continuous airworthiness maintenance program,” vol. 3, no. 2, pp. 115–123, 2019.
[17] L. C. Coelho, J. F. Cordeau, and G. Laporte, “Thirty years of inventory routing,” Transp. Sci., vol. 48, no. 1, pp. 1–19, 2014, doi: 10.1287/trsc.2013.0472.
[18] S. H. E. Abdel, A. F. Zobaa, and M. E. Balci, “Optimal resonance-free third-order high-pass filters based on minimization of the total cost of the filters using Crow Search Algorithm,” Electr. Power Syst. Res., vol. 151, pp. 381–394, 2017, doi: 10.1016/j.epsr.2017.06.009.
[19] Ö. Yeniay, “Penalty function methods for constrained optimization with genetic algorithms,” Math. Comput. Appl., vol. 10, no. 1, pp. 45–56, 2005, doi: 10.3390/mca10010045.
[20] R. G. Sargent, “Verification and validation of simulation models,” Winter Simul. Conf. Proc., vol. 1, pp. 121–130, 1998.
[21] Y. Barlas, “Multiple tests for validation of system dynamics type of simulation models *,” vol. 42, pp. 59–87, 1989.