A Novel Optimized Tourism Itinerary Recommender System: A Modified Capacitated Vehicle Routing Problem Approach

This paper proposes an optimized tourist itinerary recommender system (OTIRS) using a capacitated vehicle routing problem with constant service time (CVRPCST) approach, slightly modified from capacitated vehicle routing problem (CVRP). The goal is to maximize tourist destinations around a major city while taking the shortest routes. To achieve this, a comprehensive dataset of major cities and tourist destinations in India is constructed by scraping data from commercial websites. Post data accumulation, we formulate the problem as an Integer Linear Programming (ILP). The CVRPCST has been modified to represent each day of the itinerary as a vehicle, with its capacity determined by the available hours, while the tourist destinations and their respective travel and stay times are analogous to products with varying volumes in the vehicle routing problem. Modifications to the Miller-Tucker-Zemlin (MTZ) sub-tour elimination formulation improve itinerary optimization accuracy and effectiveness. Optimizing tourist itineraries based on user input parameters and accumulated data validates the system, providing efficient and personalized travel recommendations. This optimized tourist itinerary recommender system aims to improve travel and enable the exploration of diverse Indian destinations.