Route Planning for Tourists and Scoring Systems Metaheuristic Approach and DEA Optimization for Designing One-Day Tourist Route with Bergen as a Case

Abstract

This thesis indicates the challenges travelers face when planning trips in cities they are unfamiliar with and proposes a method to help travelers plan their routes. It would use Bergen as an example. The main issues in tourist route planning include selecting relevant attractions and optimizing the route. Therefore, this thesis consists of two parts: a scoring system and route planning. For the scoring systems part, Data Envelopment Analysis (DEA), mainly cross-efficiency DEA, is used to rank attractions and restaurants separately. As for route planning, a metaheuristic approach is implemented to deal with the complex optimization problems regarding route planning. Specifically, the GRASP (Greedy Randomized Adaptive Search Procedure) metaheuristic is applied. The thesis concludes by presenting the results of a one-day tourist route in Bergen, discussing the limitations of the DEA scoring and the metaheuristic route design, and providing directions for future researchers.

This thesis indicates the challenges travelers face when planning trips in cities they are unfamiliar with and proposes a method to help travelers plan their routes. It would use Bergen as an example. The main issues in tourist route planning include selecting relevant attractions and optimizing the route. Therefore, this thesis consists of two parts: a scoring system and route planning. For the scoring systems part, Data Envelopment Analysis (DEA), mainly cross-efficiency DEA, is used to rank attractions and restaurants separately. As for route planning, a metaheuristic approach is implemented to deal with the complex optimization problems regarding route planning. Specifically, the GRASP (Greedy Randomized Adaptive Search Procedure) metaheuristic is applied. The thesis concludes by presenting the results of a one-day tourist route in Bergen, discussing the limitations of the DEA scoring and the metaheuristic route design, and providing directions for future researchers.