Optimizing the Newspaper Distribution Scenarios using Genetic Algorithm: A Case Study of India

This paper attempts to optimize the various newspaper distribution scenario as observed in India. It found from the literature that earlier research work carried out in optimizing the vehicle routing networks problem using time window and scope was limited to distribution from newspaper printing location to the vendor point of distribution. This paper focuses on the distribution of newspaper from vendor to the newspaper reader/customer. In this study case study method is used and the circulation activity is modelled with different field scenarios with a aim of minimizing the overall (total) delivery time of the Newspaper. The major aim of this paper was to identify the issues, while distributing the newspaper by the vendor at the hub to the end reader. The company under consideration had an objective of finishing the last newspaper delivery by 6:30 am. To optimize the same the paper identified the following scenario:

Case 1. Delivery to Independent Houses Located in a Community
Case 2. Delivery to an Apartment Cluster
Case 3. Delivery considering the various modes of transportation

The present paper optimized the same using genetic algorithm as the optimizing tool. The model with different scenarios presented in the paper will help the newspaper distribution planners to understand the various issues on the field and achieve to have a successful distribution strategy within the set objective of reaching the last copy of newspaper by 6:30 am to the reader. This will help not only the newspaper company under consideration but the entire newspaper industry. This paper contributes to body of knowledge of newspaper industry its major research implications are 1. It provide insights for a particular type of functioning environment, where speed and time play an critical role. 2. In certain type the time delays in the distribution network will impact the consumer’s preference which will lead to improved market share and more satisfied customers.