Submit

Login Login

Register Register

Home / Journals / CMC / Online First / doi:10.32604/cmc.2024.052401
Journal Menu
Special Issues
Table of Content

All issues

Online First

2024

2023

2022

2021

2020

2019

2018

2017

2016

2015

2014

2013

2012

2011

2010

2009

2008

2007

2006

2005

2004

Open Access

ARTICLE

Application of Stork Optimization Algorithm for Solving Sustainable Lot Size Optimization

Tareq Hamadneh1, Khalid Kaabneh2, Omar Alssayed3, Gulnara Bektemyssova4,*, Galymzhan Shaikemelev4, Dauren Umutkulov4, Zoubida Benmamoun5, Zeinab Monrazeri6, Mohammad Dehghani6,*
1 Department of Mathematics, Al Zaytoonah University of Jordan, Amman, 11733, Jordan
2 Faculty of Information Technology, Al-Ahliyya Amman University, Amman, 19328, Jordan
3 Department of Mathematics, Faculty of Science, The Hashemite University, P.O. Box 330127, Zarqa, 13133, Jordan
4 Department of Computer Engineering, International Information Technology University, Almaty, 050000, Kazakhstan
5 Faculty of Engineering, Liwa College, Abu Dhabi, 41009, United Arab Emirates
6 Department of Electrical and Electronics Engineering, Shiraz University of Technology, Shiraz, 7155713876, Iran
* Corresponding Author: Gulnara Bektemyssova. Email: email; Mohammad Dehghani. Email: email
(This article belongs to the Special Issue: Metaheuristic-Driven Optimization Algorithms: Methods and Applications)

Computers, Materials & Continua https://doi.org/10.32604/cmc.2024.052401

Received 01 April 2024; Accepted 03 June 2024; Published online 17 July 2024

Abstract

The efficiency of businesses is often hindered by the challenges encountered in traditional Supply Chain Management (SCM), which is characterized by elevated risks due to inadequate accountability and transparency. To address these challenges and improve operations in green manufacturing, optimization algorithms play a crucial role in supporting decision-making processes. In this study, we propose a solution to the green lot size optimization issue by leveraging bio-inspired algorithms, notably the Stork Optimization Algorithm (SOA). The SOA draws inspiration from the hunting and winter migration strategies employed by storks in nature. The theoretical framework of SOA is elaborated and mathematically modeled through two distinct phases: exploration, based on migration simulation, and exploitation, based on hunting strategy simulation. To tackle the green lot size optimization issue, our methodology involved gathering real-world data, which was then transformed into a simplified function with multiple constraints aimed at optimizing total costs and minimizing CO emissions. This function served as input for the SOA model. Subsequently, the SOA model was applied to identify the optimal lot size that strikes a balance between cost-effectiveness and sustainability. Through extensive experimentation, we compared the performance of SOA with twelve established metaheuristic algorithms, consistently demonstrating that SOA outperformed the others. This study’s contribution lies in providing an effective solution to the sustainable lot-size optimization dilemma, thereby reducing environmental impact and enhancing supply chain efficiency. The simulation findings underscore that SOA consistently achieves superior outcomes compared to existing optimization methodologies, making it a promising approach for green manufacturing and sustainable supply chain management.

Keywords

Optimization; supply chain management; sustainable lot size optimization; bio-inspired; metaheuristic; stork

  • 12

    View

  • 4

    Download

  • 0

    Like

Related articles
Copyright© 2024 Tech Science Press
© 1997-2024 TSP (Henderson, USA) unless otherwise stated

Share Link