Article Sidebar

Download
PDF

Main Article Content

Abstract

Distribution is the challenging and interesting problem to be solved. Distribution problems have many facets to be resolved because it is too complex problems such as limited multi-level with one product, one-level and multi-product even desirable in terms of cost also has several different versions. In this study is proposed using an adaptive genetic algorithm that proved able to acquire efficient and promising result than the classical genetic algorithm. As the study and the extension of the previous study, this study applies adaptive genetic algorithm considering the problems of multi-level distribution and combination of various products. This study considers also the fixed cost and variable cost for each product for each level distributor. By using the adaptive genetic algorithm, the complexity of multi-level and multi-product distribution problems can be solved. Based on the cost, the adaptive genetic algorithm produces the lowest and surprising result compared to the existing algorithm

Article Details

Author Biographies

Mohammad Zoqi Sarwani, Universitas Brawijaya

Faculty of Computer Science

Wayan Firdaus Mahmudy, Universitas Brawijaya

Faculty of Computer Science

Agus Naba, Universitas Brawijaya

Faculty of Science
How to Cite
Sarwani, M. Z., Mahmudy, W. F., & Naba, A. (2017). Cost Optimization of Multi-Level Multi-Product Distribution Using An Adaptive Genetic Algorithm. Journal of Information Technology and Computer Science, 1(2), 53–64. https://doi.org/10.25126/jitecs.20161218
Download Citation
Endnote/Zotero/Mendeley (RIS)
BibTeX

References

  1. Bahrampour, P., M. Safari, and M.B. Taraghdari, Modeling Multi-Product Multi-Stage Supply Chain Network Design, in 1st International Conference on Applied Economics and Business, ICAEB 2015 Modeling2016, Elsevier B.V. p. 70-80.
  2. Langroodi, R.R.P. and M. Amiri, A system dynamics modeling approach for a multi-level, multi-product, multi-region supply chain under demand uncertainty. Expert Systems with Applications, 2016. 51: p. 231-244.
  3. Ebrahimnejad, A., A simplified new approach for solving fuzzy transportation problems with generalized trapezoidal fuzzy numbers. Applied Soft Computing Journal, 2014. 19: p. 171-176.
  4. Kundu, P., S. Kar, and M. Maiti, Fixed charge transportation problem with type-2 fuzzy variables. Information Sciences, 2014. 255: p. 170-186.
  5. Rahmi, A., M.Z. Sarwani, and W.F. Mahmudy, Genetic Algorithms for Optimization of Multi-Level Product Distribution. Accepted in International Journal of Intelligent Engineering & Systems, 2016.
  6. Sarwani, M.Z., A. Rahmi, and W.F. Mahmudy, An Adaptive Genetic Algorithm for Cost Optimization of Multi-Stage Supply Chain. Accepted in Journal of Telecommunication, Electronic and Computer Engineering, 2016.
  7. Qiongbing, Z., A New Crossover Mechanism for Genetic Algorithms with Variable-length Chromosomes for Path Optimization Problems. Expert Systems With Applications, 2016.
  8. Thakur, M. and A. Kumar, Electrical Power and Energy Systems Optimal coordination of directional over current relays using a modified real coded genetic algorithm : A comparative study. International Journal of Electrical Power and Energy Systems, 2016. 82: p. 484-495.
  9. Mahmudy, W.F., R.M. Marian, and L.H.S. Luong, Modeling and optimization of part type selection and loading problems in flexible manufacturing system using real coded genetic algorithms. International Journal of Electrical, Computer, Electronics and Communication Engineering, 2013. 7(4): p. 251-260.
  10. Kong, H., N. Li, and Y. Shen, Adaptive double chain quantum genetic algorithm for constrained optimization problems. Chinese Journal of Aeronautics, 2015. 28: p. 214-228.
  11. Magalhães-Mendes, J., A comparative study of crossover operators for genetic algorithms to solve the job shop scheduling problem. WSEAS Transactions on Computers, 2013. 12: p. 164-173.
  12. Welikala, R.A., et al., Genetic algorithm based feature selection combined with dual classification for the automated detection of proliferative diabetic retinopathy. Computerized Medical Imaging and Graphics, 2015. 43: p. 64-77.
  13. Wang, L. and D.-b. Tang, An improved adaptive genetic algorithm based on hormone modulation mechanism for job-shop scheduling problem. Expert Systems with Applications, 2011. 38(6): p. 7243-7250.
  14. Abdoun, O., J. Abouchabaka, and C. Tajani, Analyzing the Performance of Mutation Operators to Solve the Travelling Salesman Problem. International Journal of Emerging Sciences, 2012. 2: p. 61-77.
  15. Karami, A.H. and M. Hasanzadeh, An adaptive genetic algorithm for robot motion planning in 2D complex environments. Computers & Electrical Engineering, 2015. 43: p. 317-329.
  16. Horng, S.-C., S.-S. Lin, and F.-Y. Yang, Evolutionary Algorithm for Stochastic Job Shop Scheduling with Random Processing Time. Expert Systems with Applications, 2012. 39(1): p. 3603-3610.
  17. Rahmi, A., W.F. Mahmudy, and S. Anam, A Crossover in Simulated Annealing for Population Initialization of Genetic Algorithm to Optimize the Distribution Cost. Accepted in Journal of Telecommunication, Electronic and Computer Engineering, 2016.