Journal of Graphic Era University

A Greedy Reconstruction Heuristic for Solving the Minimum Travelling Salesman Tour Problem

Santosh Kumar OAM — 2025-06-02

Nearest neighbour approach for determination of the minimum travelling salesman tour is a well-documented heuristic for a quick identification of the salesman tour and its associated cost. This heuristic has been used on large sized networks for a quick approximate solution to the travelling salesman problem. For the last several years, an alternative approach to the minimum travelling salesman tour has been developed through the minimum spanning tree (MST). The MST was converted to an index restricted MST (IRMST) and that IRMST was used to find the minimum travelling salesman tour. These two approaches, i.e., the greedy heuristics and the index restricted minimum spanning tree seems to have stronger relations and it is this relationship, which has been explored in this paper. The nearest neighbour approach to find the travelling salesman tour has been modified by incorporating the index balancing theorem. A further modification to this heuristic has been suggested to identify the minimum travelling salesman tour. Many small size problems have been attempted using the heuristic discussed in this paper, and it resulted in an optimal solution but an analytical proof for optimality remains a challenge. This approach further strengthens a natural question about the ‘NP Hard’ category associated with the minimum travelling salesman tour problem.

Application of Kaizen Analysis in Automotive Industry: A Case Study

Amit Surya — 2025-06-17

The manufacturing sector has grown significantly during the last two to three decades. Because of the intense competition, customers now place a higher priority on price, delivery time, and product quality. This study employs lean methods and methodologies, including root-cause analysis, kaizen ideas, evaluation of kaizen ideas, and before-and-after comparison, to evaluate the operational performance of the organization. It accomplishes this by using kaizen analysis to assess the level of lean implementation in Indian manufacturing companies. A questionnaire was developed for the goal of collecting data, and industry experts were contacted personally and questioned. According to this study, common implementation of lean practices leads to a reduction of 68% in setup loss, 70% in setup time, 17% in energy consumption, 11% in electricity consumption, 51% in lead time, and an increase of 8.5 times in kaizen per team annually.

Transforming Digital Marketing with Machine Learning Algorithms

Sushma Malik — 2025-07-15

In the era of rapid technological advancements, digital marketing has evolved significantly, leveraging innovative technologies to enhance customer engagement, personalize experiences, and optimize marketing strategies. One of the most promising approaches to transforming digital marketing is the integration of ML algorithms, which can automate decision-making processes, predict consumer behavior, and improve marketing campaign effectiveness. This paper investigates the integration of ML techniques into digital marketing, aiming to enhance customer engagement, personalization, and campaign effectiveness through data-driven strategies. The objective is to demonstrate how ML can transform traditional digital marketing approaches by automating decision-making and predicting consumer behavior. Using a Python-based implementation, the study applies key ML models – classification, clustering, and regression – to practical digital marketing tasks such as customer segmentation, personalized content recommendation, and performance analytics. The methodology involves utilizing Python libraries including Scikit-learn, TensorFlow, and Keras to develop and evaluate ML models on relevant marketing datasets. The findings reveal that ML-driven marketing strategies can significantly improve customer targeting, increase return on investment, and deliver more personalized user experiences. Additionally, the paper identifies and discusses challenges such as data quality, algorithmic bias, and ethical concerns surrounding the use of personal data. These insights underscore the transformative potential of ML in digital marketing, while also emphasizing the importance of responsible and transparent implementation. The paper concludes that when thoughtfully applied, ML offers a powerful toolset for businesses seeking to innovate and optimize their marketing efforts in the digital age.