Solving tourist trip planning problem via a simulated annealing algorithm

Abstract

Nowadays, by help of tourist trip planners, in many travel destinations, the trip itinerary could be prepared automatically. The trip planners enable customization of trip itinerary based on tourist preferences, available time and budget, where the itinerary is optimized by using optimization techniques from the field of metaheuristics. In this paper, we present a new approach based on simulated annealing algorithm for solving the tourist trip planning problem. The hard constraints include limited trip duration, working hours of points of interest (POIs) and tourist budget, while tourist preferences and traveling time comprise the soft constraints, which take part in a two component function for evaluation of quality of solutions. The search space is explored by using three types of operators, namely insert, swap and shake. The algorithm is tested against some real test data for the city of Prishtina in Kosova. In addition to fine tuning the values of algorithm parameters, further experiments are made for exploring different approaches of construction of initial solution, time duration of algorithm execution for various trip lengths, and influence of shake operator into the solution quality.

Trip Planning Based on subQUBO Annealing

“…The trip planning problem (TPP) in this paper is the problem that finds the optimal route to visit a series of point-ofinterests (POIs) and hotels over multiple days [1]- [4]. For example, Sylejmani et al [1] presented a method that solves the trip planning problem using a heuristic algorithm based on tabu search; Saeki et al [2] presented a method for planning a multi-objective trip using antcolony optimization; Fournier et al [3] showed a method that solves the bus passenger trip planning problem using an A*-guided and Pareto dominance-based heuristic; Garcia et al [4] presented two different methods to solving the time-dependent team orienteering problem with time windows; Shuai et al [5] presented a method that solves multiple traveling salesman problems by applying an NSGA-II framework; He et al [6] presented a hybrid method based on tabu search and intratour optimization to solve the multiple traveling salesman problems.…”

“…Ising machines search for an optimal solution by finding the combination of σ i , called the ground state, that minimizes the energy function by Eq. (1). The energy function of the Ising model E(σ) is represented as follows [28]:…”

“…We set N I = 20, N E = 5, and N S = 5 in the subQUBO annealing step, since these parameters give the best solution in [27]. 1 In this paper, we assume that the user travels by train. Since the average speed of the JR Yamanote Line is 34.5 km/h, the travel speed is set to be 35 km/h.…”

Orienteering Problem: A survey of recent variants, solution approaches and applications

“…In conclusion, they are able to produce a personal trip itinerary within reasonable computational times. Sylejmani et al (2014) extend the previous TTDP by modeling it as the Multi-Constraint TOP with Multiple Time Windows (MCTOPMTW). They propose an algorithm based on SA for solving this problem.…”