Application of Stork Optimization Algorithm for Solving Sustainable Lot Size Optimization  

作　　者：Tareq Hamadneh Khalid Kaabneh Omar Alssayed Gulnara Bektemyssova Galymzhan Shaikemelev Dauren Umutkulov Zoubida Benmamoun Zeinab Monrazeri Mohammad Dehghani 

机构地区：[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

出　　处：《Computers, Materials & Continua》2024年第8期2005-2030,共26页计算机、材料和连续体（英文）

基　　金：This research is funded by the Science Committee of the Ministry of Science and Higher Education of the Republic of Kazakhstan,Grant No.AP19674517.

摘　　要：The efficiency of businesses is often hindered by the challenges encountered in traditional Supply Chain Manage-ment(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_(2) 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.

关 键 词：OPTIMIZATION supply chain management sustainable lot size optimization BIO-INSPIRED METAHEURISTIC STORK 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]