Pareto Frontier Approach to Determining the Optimal Path on Multi-Objectives

Authors

  • Annisa Pratiwi Graduate Student of Mathematics Department, Universitas Sumatera Utara, Indonesia
  • M.K.M. Nasution Mathematics Department Universitas Sumatera Utara https://orcid.org/0000-0003-1089-9841
  • E. Herawati Mathematics Department Universitas Sumatera Utara

DOI:

10.33395/sinkron.v8i3.12465

Keywords:

Ant Colony Optimization, Multiobjective, Pareto Frontier, Time Windows, Vehicle Routing Problem

Abstract

Every issue we face in daily life can be resolved through mathematical modeling. The use of mathematical modeling to generate solutions frequently produces value that serves a single purpose. Sometimes a single-purpose function's solution does not offer the best solution value. In this study, the author models the multiobjective time-dependent vehicle routing problem using the Ant Colony Optimization (ACO) metaheuristic algorithm. The author then applies the pareto optimization principle to the determination of the optimal starting point for the route. An optimal Pareto frontier principle solution on a multi-objective model under control of the Ant Colony Optimization algorithm is the outcome of this study.

 

GS Cited Analysis

Downloads

Download data is not yet available.

Author Biographies

M.K.M. Nasution, Mathematics Department Universitas Sumatera Utara

 

 

E. Herawati, Mathematics Department Universitas Sumatera Utara

 

 

References

Bentley, P. J., & Wakefield, J. P. (1997). Finding Acceptable Pareto-Optimal Solutions using Multiobjective Genetic Algorithms. Soft Computing in Engineering Design and Manufacturing, 1–16.

Camisa, A., Farina, F., Notarnicola, I., & Notarstefano, G. (2021). Distributed constraint-coupled optimization via primal decomposition over random time-varying graphs. Automatica, 131, 109739. https://doi.org/10.1016/j.automatica.2021.109739

Djunaidy, A., Angresti, N. D., & Mukhlason, A. (2019). Hyper-heuristik untuk Penyelesaian Masalah Optimasi Lintas Domain dengan Seleksi Heuristik berdasarkan Variable Neighborhood Search. Khazanah Informatika: Jurnal Ilmu Komputer Dan Informatika, 5(1), 51–60. https://doi.org/10.23917/khif.v5i1.7567

Ferliani, M., Schmidt, S., Schulz, V., Binois, M., Picheny, V., Vodopija, A., Tušar, T., Filipič, B., Roostapour, V., Neumann, A., Neumann, F., Friedrich, T., Personal, M., Archive, R., ESTECO, Roberts, M. C., Dizier, A. S. T., Vaughan, J., Ŝcap, D., … Lu, X. (2022). Determination of the pareto frontier for multiobjective optimization problem. مجلة اسيوط للدراسات البيئة, 10(2), 103597. https://doi.org/10.1016/j.artint.2021.103597

Frazzoli, E. (2010). Copyright © by SIAM . Unauthorized reproduction of this article is prohibited . Society, 48(5), 3224–3245.

Fu, Y., & Diwekar, U. M. (2004). An efficient sampling approach to multiobjective optimization. Annals of Operations Research, 132(1–4), 109–134. https://doi.org/10.1023/B:ANOR.0000045279.46948.dd

Kachroudi, S., & Bhouri, N. (2009). A multimodal traffic responsive strategy using particle swarm optimization. In IFAC Proceedings Volumes (IFAC-PapersOnline) (Vol. 42, Issue 15). IFAC. https://doi.org/10.3182/20090902-3-US-2007.0038

Kumar, S. (2022). Modeling usage intention for sustainable transport: Direct, mediation, and moderation effect. Sustainable Production and Consumption, 32, 781–801. https://doi.org/10.1016/j.spc.2022.05.019

L’Hostis, A. (2017). Detour and break optimising distance, a new perspective on transport and urbanism. Environment and Planning B: Urban Analytics and City Science, 44(3), 441–463. https://doi.org/10.1177/0265813516638849

Lin, X., Chen, H., Pei, C., Sun, F., Xiao, X., Sun, H., Zhang, Y., Ou, W., & Jiang, P. (2019). A pareto-eficient algorithm for multiple objective optimization in e-commerce recommendation. RecSys 2019 - 13th ACM Conference on Recommender Systems, 20–28. https://doi.org/10.1145/3298689.3346998

Liu, Y., Ishibuchi, H., Nojima, Y., Masuyama, N., & Han, Y. (2019). Searching for Local Pareto Optimal Solutions: A Case Study on Polygon-Based Problems. 2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings, 61876075, 896–903. https://doi.org/10.1109/CEC.2019.8790066

Nordström, P. (2014). Multi-objective optimization and Pareto navigation for voyage planning. Uppsala Universitet.

Shan, S., & Wang, G. G. (2005). An efficient Pareto set identification approach for multiobjective optimization on black-box functions. Journal of Mechanical Design, Transactions of the ASME, 127(5), 866–874. https://doi.org/10.1115/1.1904639

Zhu, S., Sun, H., & Guo, X. (2022). Cooperative scheduling optimization for ground-handling vehicles by considering flights’ uncertainty. Computers and Industrial Engineering, 169(March), 108092. https://doi.org/10.1016/j.cie.2022.108092

Downloads


Crossmark Updates

How to Cite

Pratiwi, A., Nasution, M., & Herawati, E. (2023). Pareto Frontier Approach to Determining the Optimal Path on Multi-Objectives . Sinkron : Jurnal Dan Penelitian Teknik Informatika, 7(3), 1379-1388. https://doi.org/10.33395/sinkron.v8i3.12465

Issue

Vol. 7 No. 3 (2023): Article Research Volume 7 Issue 3, July 2023

Section

Articles

License

Copyright (c) 2023 Annisa Pratiwi, Mahyuddin Nasution, Elvina Herawati

Creative Commons License

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

Crossref
Scopus
Google Scholar
Europe PMC