Optimization of Neutrosophic Vendor-Buyer Economic Order Quantity Model Using Particle Swarm Optimization

K. Kalaiarasi1,2,*, N. Anitha3, S. Swathi4, B. Ranjitha5

1PG and Research Department of Mathematics, Cauvery College for Women (Autonomous), Affiliated to Bharathidasan University, Tiruchirappalli-620018, Tamil Nadu, India.

2D. Sc (Mathematics) Researcher Fellow, Srinivas University, Surathkal, Mangaluru, Karnataka-574146.

3Department of Mathematics, Periyar University Centre for Postgraduate and Research Studies, Dharmapuri - 635205, Tamil Nadu, India

4Ph.D Research Scholar, PG and Research Department of Mathematics, Cauvery College for Women (Autonomous), Affiliated to Bharathidasan University, Tiruchirappalli-620018, Tamil Nadu, India.

5Department of Mathematics, Mohan Babu University, Tirupati-517501, Andra Pradesh, India.

 

Emails: kalaishruthi120@gmail.com; anithaarenu@gmail.com; swathimaths30@gmail.com, ranjitha.b@vidyanikethan.edu 

 

Abstract

This research introduces the Neutrosophic Vendor-Buyer Economic Order Quantity (EOQ) model, integrating Neutrosophic Set Theory and Particle Swarm Optimization (PSO) for advanced inventory management. Addressing uncertainties in demand and costs, Neutrosophic Sets quantify truth, indeterminacy, and falsity degrees for key parameters. The model, employing PSO inspired by collective behaviour in nature, aims to minimize the combined total cost (C) encompassing vendor and buyer expenses. A grocery store scenario illustrates the approach, demonstrating substantial total cost reduction through the optimization of decision variables. MATLAB R2015a visualizations include a mesh plot depicting cost changes across varying EOQ and demand variability values, emphasizing optimal solutions. A bar chart compares initial and optimized total costs, showcasing efficiency gains. Cost breakdowns and pie charts detail the impact on vendor and buyer expenses. Sensitivity analysis systematically explores variable influences, aiding decision-makers in understanding trade-offs and optimal ranges by using Python. This comprehensive framework contributes empirical insights for practical implementation, enabling businesses to make informed decisions and enhance adaptive inventory strategies efficiently.

Keywords: Neutrosophic Set; Economic Order Quantity; Optimization; Total Cost; MATLAB R2015a; Python.