Optimasi Logistic Cost dari Perusahaan Crossdock Menggunakan Pendekatan Linear Programming dengan Permintaan Stokastik

Deri Maryadi

doi:10.37090/indstrk.v9i1.1752

Authors

Deri Maryadi Universitas Tridinanti

DOI:

https://doi.org/10.37090/indstrk.v9i1.1752

Abstract

Efficiency within a company must be enhanced to maintain competitiveness and business sustainability, particularly in logistics firms. In logistics processes, minimizing costs is a critical indicator of operational efficiency. This study applies optimization through linear programming to a cross-dock company characterized by fluctuating demand, focusing on a fast-moving product. The analysis begins by calculating the minimum and maximum demand based on twice the standard deviation from the mean, followed by determining the optimal scenarios for each depot. The results indicate that, for the eight depots, the majority of the truck selection involved using single-axle trucks. Consequently, the total cost from the first to the seventh week was Rp. 1,785,000,000, while the LP simulation results indicated a total cost of Rp. 1,214,000,000, representing a potential cost saving of Rp. 554,000,000.

Keywords: Cross-Dock, Linear Programming, Logistic Cost, Optimization

Downloads

Download data is not yet available.

References

Abideen, A., & Mohamad, F. B. (2021). Improving the performance of a Malaysian pharmaceutical warehouse supply chain by integrating value stream mapping and discrete event simulation. Journal of Modelling in Management, 16(1), 70–102. https://doi.org/10.1108/JM2-07-2019-0159

Agatz, N., & Schmidt, M. (2016). Optimization Approaches for the Traveling Salesman Problem with Drone. 1–40.

Benrqya, Y. (2023). Costs and benefits of using cross-docking in the retail supply chain : A case study of an FMCG company International Journal of Retail & Distribution Management Article information : April. https://doi.org/10.1108/IJRDM-07-2018-0119

CALP, M. H., & AKCAYOL, M. A. (2018). Optimization of Project Scheduling Activities in Dynamic CPM and PERT Networks Using Genetic Algorithms. Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 22(2), 615. https://doi.org/10.19113/sdufbed.35437

Cao, Z., & Anggara, S. (2019). E-Commerce in Singapore and Indonesia: Comparison of Policies. International Journal of Science and Society, 1(1), 1–12. https://ijsoc.goacademica.com

Edition, S. (2017). Retail Supply Chain Management, Second Edition. In Retail Supply Chain Management, Second Edition. https://doi.org/10.1201/9781315151410

Estampe, D. (2020). Cross-docking or traditional warehousing : what is the right distribution strategy for your product ? April 2023. https://doi.org/10.1108/IJPDLM-03-2019-0091

Kanamori, K., Takagi, T., Kobayashi, K., & Arimura, H. (2020). DACE : Distribution-Aware Counterfactual Explanation by Mixed-Integer Linear DACE : Distribution-Aware Counterfactual Explanation by Mixed-Integer Linear Optimization. October. https://doi.org/10.24963/ijcai.2020/391

Kanan, M. (2023). Uncertain Supply Chain Management supply chain performance in the manufacturing sector of Saudi Arabia : An empirical study. 11, 1589–1598. https://doi.org/10.5267/j.uscm.2023.7.010

Kargar, S., Pourmehdi, M., & Paydar, M. M. (2020).. Reverse logistics network design for medical waste management in the epidemic outbreak of the novel coronavirus ( COVID-19 ). January.

Lagos, C., Guerrero, G., Cabrera, E., Moltedo-Perfetti, Andr. S., Johnson, F., & Paredes, F. (2018). An improved particle swarm optimization algorithm for the VRP with simultaneous pickup and delivery and time windows. IEEE Latin America Transactions, 16(6), 1732–1740. https://doi.org/10.1109/TLA.2018.8444393

Lo, S. (2022). A Particle Swarm Optimization Approach to Solve the Vehicle Routing Problem with Cross-Docking and Carbon Emissions.

Lo, S. (2023). Vehicle Routing Optimization with Cross-Docking Based on an Artificial Immune System in Logistics Management.

Manajemen, J. I. (2023). Improvement Performa Gudang Medium Mile dengan Menggunakan Value Stream Mapping Case Study : Warehouse Medium Mile di Kota Palembang. 3(1), 40–48.

Maryadi, D. (2021). Lean Six Sigma DMAIC Implementation to reduce Total Lead Time Internal Supply Chain Process. 2086–2096.

Mavi, R. K., Goh, M., Mavi, N. K., Jie, F., Brown, K., Biermann, S., & Khanfar, A. A. (2020). Cross-Docking : A Systematic Literature Review. 1–19.

Mousavi, S. M., Antuchevičienė, J., Zavadskas, E. K., Vahdani, B., & Hashemi, H. (2019). A NEW DECISION MODEL FOR CROSS-DOCKING CENTER LOCATION IN LOGISTICS NETWORKS UNDER INTERVAL-VALUED INTUITIONISTIC FUZZY UNCERTAINTY. 34(1), 30–40.

Putri, A. W., Satriani, R., & Zulkifli, L. (2021). Decision support system for truck scheduling in logistic network through cross-docking strategy Decision support system for truck scheduling in logistic network through cross-docking strategy. https://doi.org/10.1088/1742-6596/1811/1/012009

Sangaiah, A. K. (2019). Robust optimization and mixed-integer linear programming model for LNG supply chain planning problem. 6.

Santos, F. A., Mateus, G. R., & Salles, A. (2013). Computers & Operations Research The Pickup and Delivery Problem with Cross-Docking. Computers and Operation Research, 40(4), 1085–1093. https://doi.org/10.1016/j.cor.2012.11.021

Theophilus, O., Dulebenets, M. A., Pasha, J., & Abioye, O. F. (2019). Truck Scheduling at Cross-Docking Terminals : A Follow-Up State-Of-The-Art Review.

Yang, F., Dai, Y., & Ma, Z. J. (2020). A cooperative rich vehicle routing problem in the last-mile logistics industry in rural areas. Transportation Research Part E: Logistics and Transportation Review, 141(June), 102024. https://doi.org/10.1016/j.tre.2020.102024