Cost Optimization of Multi-Level Multi-Product Distribution Using An Adaptive Genetic Algorithm
DOI:
https://doi.org/10.25126/jitecs.20161218Abstract
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 algorithmReferences
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.
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.
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.
Kundu, P., S. Kar, and M. Maiti, Fixed charge transportation problem with type-2 fuzzy variables. Information Sciences, 2014. 255: p. 170-186.
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.
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.
Qiongbing, Z., A New Crossover Mechanism for Genetic Algorithms with Variable-length Chromosomes for Path Optimization Problems. Expert Systems With Applications, 2016.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Downloads
Published
How to Cite
Issue
Section
License
 Creative Common Attribution-ShareAlike 3.0 International (CC BY-SA 3.0)
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
