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This article proposed a design and development of an algorithm for a travel itinerary planner which focused on facilitating travelers to reach destinations as much as possible under the time constraints. The proposed algorithm is based on a simulated annealing algorithm (SA) and applies a shortest path search technique (SPS) to increase the efficiency of the algorithm. The performance evaluation of the proposed algorithm is tested on the travel itinerary planner problem under time constraints (TIPP), which is the main problem in this research. In addition, the testing has been extended in the traveling salesman problem (TSP). The evaluation results of TIPP and TSP problems are in the same direction. There are two aspects of evaluation for each problem. The first aspect is to test the elapsed time of algorithms. The experimental results reveal that the proposed algorithm spends less elapsed time than a basic SA, a progressive routing algorithm (PR), and an exhaustive routing algorithm (ER). However, the proposed algorithm is slightly worse than a greedy best first search algorithm (GBFS), which is a high-speed processing algorithm. The second aspect is to test the quality of solution performance. The results indicate that the proposed algorithm provides more quality of the solution than SA and GBFS. However, the developed algorithm is slightly worse than PR and ER, which are algorithms that guarantee to find for an optimal solution. Nevertheless, the developed algorithm spends much less elapsed time.
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