Developing An Intelligent Logistics and Distribution System For A Large Number of Retail Outlets: A Big Data Analytics Approach

Vol 3, No 1 (2020) > Articles >

Developing An Intelligent Logistics and Distribution System For A Large Number of Retail Outlets: A Big Data Analytics Approach

Zulkarnain Zulkarnain, Komarudin Komarudin, Fauziah Arofah, Irvanu Rahman

Abstract:

The logistics and distribution system in the retail industry in Indonesia has its own complexity. The growth and productivity of the retail outlets in Indonesia have been increasing from year to year. Distribution activities in this study are related to the formation of salesman visit routes involving around 38,900 customer base retail outlets, which are quite numerous in number, calling for a challenging approach to find the optimized solution. Therefore, the case study in this research will be discussed on the concept of the Traveling Salesman Problem (TSP) and the Vehicle Routing Problem (VRP), considering the work balance and visit pattern constraints. The methods used in this research are the balanced K-means and the Minimum Span Tree – Kruskal’s Walk algorithm, which are proven to solve the problem with a shorter computation time and a more balanced daily route than the current conditions as the results.

Keywords: Balanced K-Means, Intelligent Logistics, Optimization, Vehicle Routing Problem

Published at: CSID - JID Vol 3, No 1 (2020)

DOI: https://doi.org/10.32783/csid-jid.v3i1.106

Full PDF Download Citation Format

References

Agusta, Y., 2007. K-Means – Penerapan, Permasalahan dan Metode Terkait. Jurnal Sistem dan Informatika,Volume 3, pp.47–60.

Carlsson, J. G., Behroozi, M., 2016. Worst-case Demand Distributions in Vehicle Routing, European Journal of Operational Research, Volume 256(2), pp. 462–472.

Cousineau, D., Chartier, S., 2010. Outliers Detection and Treatment: A Review. International Journal of Psychological Research, Volume 3, pp. 58–67.

Decerle, J., Grunder, O., Hassani, A. H. E., Barakat, O., 2017. A General Model for the Home Health Care Routing and Scheduling Problem with Route. IFAC-PapersOnLine, Volume 50, pp. 14662–14667.

Djumena, E., 2015. Jumlah Toko di RI Terbesar Kedua Dunia, [online] available:

https://entertainment.kompas.com/read/2011/03/15/12333392/Jumlah.Toko.di.RI.Terbesar.Kedua.Dunia.

Du, L., He, R., 2012. Combining Nearest Neighbor Search with Tabu Search for Large-Scale Vehicle Routing Problem. Physics Procedia, Volume 25, pp. 1536–1546.

Groer, C., Golden, B., Wasil, E., 2010. A Library of Local Search Heuristics for the Vehicle Routing Problem. Mathematical Programming Computation, Volume 2(2), pp. 79–101.

Hernandeza F., Gendreaua M., Potvina, J.Y., 2017. Heuristics for Tactical Time Slot Management a Periodic Vehicle Routing Problem View. International Transportation in Operation Research, Volume 24(6), pp. 1233–1252.

Kytöjoki, J., Nuortio, T., Bräysy, O., Gendreau, M., 2007. An Efficient Variable Neighborhood Search Heuristic for Very Large Scale Vehicle Routing Problems. Computers & Operations Research, Volume 34, pp. 2743 – 2757.

Laporte, G., Gendreau, M., Potvin, J. Y., Semet, F., 2000. Classical And Modern Heuristics for The Vehicle Routing Problem, International Transportation in Operation Research, Volume 7, pp. 285–300.

Mohammed, M. A., Ghani, M. K. A., Hamed, R. I., Mostafa, S. A., Ibrahim, D. A., Jameel, H. K., Alallah, A. H., 2017. Solving Vehicle Routing Problem by Using Improved K-Nearest Neighbor Algorithm for Best Solution. Journal of Computational Science, Volume 21, pp. 232–240

Mohammed, M. A., Ghani, M. K. A., Hamed, R. I., Mostafa, S. A., Ahmad, M. S., Ibrahim, D. A., 2017. Solving Vehicle Routing Problem by Using Improved Genetic Algorithm for Optimal Solution. Journal of Computational Science, Volume 21, pp. 255–262.

Naderi, S., Kilic, K., 2016. A Decision Support System for Staff Workload Balancing. Procedia Computer Science, Volume 102, pp.67–73.

Nikolakopoulou, G., Kortesis, S., Synefaki, A., Kalfakakou, R., 2004. Solving a Vehicle Routing Problem by Balancing the Vehicles Time Utilization. European Journal of Operational Research, Volume 152, pp.520–527.

O’Donnell, C. R., Eggemeier, F. T., 1986. Workload Assessment Methodology: chapter 42. in: Boff, K. R., Kaufman, L., Thomas, J. P. (Eds.), Handbook of Perception and Human Performance, Volume 2, pp. 1–49.

Pandin, M. L., 2009. Potret Bisnis Ritel Di Indonesia: Pasar Modern, Economic Review, No. 215, March 2009.

Pavone, M., Arsie, A., Frazzoli, E., Bullo, F., 2011. Distributed Algorithms for Environment Partitioning in Mobile Robotic Networks. IEEE Transactions on Automatic Control, Volume 56(8), pp.1834–1848.

Pena, J. M., Lozano, J. A., Larranaga, P., 1999. An Empirical Comparison of Four Initialization Methods for The K-means Algorithm. Pattern Recognition Letter, Volume 20, pp. 1027–1040.

Pop, C., Zelina, I., Lupse, V., Sitar, C. P., Chira, C., 2011. Heuristic Algorithms for Solving the Generalized Vehicle Routing. International Journal of Computers, Communications & Control, Volume 6, pp. 158-165.

Razzazi, M., Esmaeeli, A., 2014. Balanced Allocation Mechanism: An Optimal Mechanism for Multiple Keywords Sponsored Search Auctions. Information Sciences, Volume 262, pp. 190–214.

Shah, S. S. H., Jaffari, A. R., Aziz, J., Ejaz, W., Ul -Haq, I., Raza, S. N., 2011. Workload and Performance of Employees. Interdisciplinary Journal of Contemporary Research in Business, Volume 3, pp. 256–267.

Soliha, E., 2008. Analisis Industri Ritel di Indonesia. Jurnal Bisnis dan Ekonomi, Volume 15, pp. 128–142.

Zhou, G., Min, H., Gen, M., 2002. The Balanced Allocation of Customers to Multiple Distribution Centers in the Supply Chain Network: a genetic algorithm approach. Computers and Industrial Engineering, Volume 43, pp.251–261.

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.