Optimization of Neutrosophic EOQ Model for Effective Demand Management in Uncertain Environment Using Genetic Optimization
Manjula G. J.1, N. Anitha2*, A. P. Pushpalatha3, K. Vinaya Laxmi4, M. Premalatha5, Mekala Selvaraj6
1Department of Mathematics, Siddaganga Institute of Technology, Tumakuru – 572103, Karnataka, India
2*Department of Mathematics, Periyar University Centre for Postgraduate and Research Studies, Dharmapuri - 635205, Tamil Nadu, India
3Department of Mathematics, Velammal College of Engineering and Technology, Madurai- 625009, Tamil Nadu, India
4Department of Management Studies, Vardhaman College of Engineering, Hyderabad - 501218, Telangana, India
5Department of Mathematics, Vel Tech Rangarajan Dr Sagunthala R & D Institute of Science and Technology, Chennai – 600062, Tamil Nadu, India
6Department of Mathematics, School of Engineering and Technology, CMR University, Hennur, Bagalur, Bengaluru – 562149, Karnataka, India
Emails: gjm@sit.ac.in; anithaarenu@gmail.com; app@vcet.ac.in; inayakasani123@gmail.com; drmpremalatha@veltech.edu.in; mekala.s@cmr.edu.in
Abstract
Inventory management is characterized by a continuous struggle to lower goods levels and related costs while also providing customers with the goods they need. However, reducing costs while simultaneously striving for ideal inventory levels is difficult, notably in the current situation of high unpredictability of goods demand and lead time. Traditional inventory models are not strong enough to endure changes like goods demand and lead-time demand. As a result, it must be adjusted to achieve results. The oeuvre below presents a new kind of inventory model that deals with uncertainty in the demand for goods and lead time. In this regard, the presented work, the novel Neutrosophic Economic Order Quantity approach is a mechanism to account for the likely imprecision in the model. Specifically, the Neutrosophic set theory is integrated into the EOQ model so that it can handle variations in the demand and lead-time pattern successfully. An objective function is established for obtaining economical order quantities that include demand, lead-time, and other necessary components’ irregularities. The process variables in the model are given the final values using genetic algorithms and simulated annealing. To highlight the impact of the proposed Neutrosophic approach, it is then applied to several realistic examples. This will provide the audience a sense of how effective inventory management may be in high-uncertainty situations. The rapid evolution of organizations necessitates innovative inventory control tactics to meet growing demands.
Keywords: EOQ; Inventory Model; Neutrosophic model; Optimization; Python.