Authors:
T. Anand, R. Sudhakara Pandian, Maged Farouk, Dipakkumar Kanubhai Sachani, P. Sudha

Addresses:
1Department of Aeronautical Engineering, Sathyabama Institute of Science and Technology, Chennai, Tamil Nadu, India.  2Department of Mechanical Engineering, Vellore Institute of Science and Technology, Vellore, Tamil Nadu, India. 3Department of Administration, College of Humanities and Administration Studies, Onaizah Colleges, Unayzah,  Saudi Arabia. 3Department of Industrial Relations, Workers University, Nasr City, Cairo Governorate, Egypt. 4Department of Business Analyst, Arth Energy Corporation, Pittsburgh, Pennsylvania, United States of America. 5Department of Business Administration, Dhaanish Ahmed College of Engineering, Chennai, Tamil Nadu, India. anand.aero@sathtyabama.ac.in1, sudhame@gmail.com2, maged.f@oc.edu.sa3,dks. info@arthenergycorp.com4, sudhap@dhaanishcollege.in5

Abstract:

This study is addressed in two parts: the reinforcement learning algorithm and the nearest neighbourhood algorithm. The nearest neighbourhood algorithm paves the way to find the shortest trans-partition distance among the province, and the reinforcement learning algorithm gives an optimal working plan by reducing the workload, transfer cost and maximum profit. The proposed model explains how the operations were carried out to reduce the expenditure. The material is collected from the customer on the doorstep by the retailers. The collected materials were transported to the nearby regional distribution centre and transferred to the national distribution centre. This is the first line of the process. The materials stored in the national distribution centre are taken to regional distribution centres and later distributed to the customers by retailers. Thus, forming separate pick-up points based on the study of track records helps improve service quality and decrease transportation costs. To explain this model here and consider it, Madurai is the national distribution centre. It is divided into several regional distribution centres, and each regional distribution centre is subdivided into retailers. Thus, the process uses these working bodies to collect and deliver materials. The collection process is scheduled and divided based on the availability of the source, which is calculated using a Reinforcement learning algorithm. The result shows that it reduces expenditure by 10% compared to the previously existing model and increases the percentage of profit earned.

Keywords: Reinforcement Learning Algorithm; Nearest Neighbourhood Algorithm; Courier Industry; Integrated Supply Chain Networks; Customer and Retailer; Regional Distribution Centre; National Distribution Centre; Processing Industry.

Received on: 19/08/2023, Revised on: 02/11/2023, Accepted on: 11/12/2023, Published on: 26/12/2023

FMDB Transactions on Sustainable Management Letters, 2023 Vol. 1 No. 4, Pages: 168-180

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