Integrated self-driving travel scheme planning

Abstract

Travel scheme planning is a crucial operational-level decision to be made in travel supply chain management. We investigate an integrated self-driving travel scheme planning (ISTSP) problem to optimize routing, hotel selection, and time scheduling under several streams of personalized considerations: best site-viewing time windows, rest requirements, and preference for site visiting sequences. The travel scheme planning problem is formulated in two models: (i) total cost minimization, and (ii) bi-objective optimization with total cost minimization and tourists’ utility maximization. A heuristic solution framework integrating multi-categorical attribute K-means clustering, dynamic programming algorithm, and constraint satisfaction procedure is designed to solve these two models. Finally, we provide illustrative examples to demonstrate the effectiveness and validity of the proposed models and solution methods.