Solving multi-objective team orienteering problem with time windows using adjustment iterated local search

Abstract

One of the problems tourism faces is how to make itineraries more effective and efficient. This research has solved the routing problem with the objective of maximizing the score and minimizing the time needed for the tourist's itinerary. Maximizing the score means collecting a maximum of various kinds of score from each destination that is visited. The profits differ according to whether those destinations are the favorite ones for the tourists or not. Minimizing time means traveling time and visiting time in the itinerary being kept to a minimum. Those are small case with 16 tourism destinations in East Java, and large case with 56 instances consists of 100 destinations each from previous research. The existing model is the Team Orienteering Problem with Time Window (TOPTW), and the development has been conducted by adding another objective, minimum time, become Flexible TOPTW. This model guarantees that an effective itinerary with efficient timing to implement will be produced. Modification of Iterated Local Search (ILS) into Adjustment ILS (AILS) has been done by replacing random construction in the early phase with heuristic construction, continue with Permutation, Reserved and Perturbation. This metaheuristic method will address this NP-hard problem faster than the heuristic method because it has better preparation and process. Contributing to this research is a multi-objective model that combines maximum score and minimum time, and a metaheuristics method to solve the problem faster and effectively. There are calibration parameter with 17 instances of 100 destinations each, small case test using Mixed Integer Linear Programming, and large case test comparing AILS with Multi-Start Simulated Annealing (MSA), Simulated Annealing (SA), Artificial Bee Colony (ABC), and Iterated Local Search. The result shows that the proposed model will provide itinerary with less number of visited destination 4.752% but has higher total score 8.774%, and 3836.877% faster, comparing with MSA, SA, and ABC. While AILS is compared with ILS, it has less visited destination 5.656%, less total score 56.291%, and faster 375.961%. Even though AILS has more efficient running time than other methods, it needs improvement in algorithm to create better result.

Sustainable closed-loop supply chain network under uncertainty: a response to the COVID-19 pandemic

“…robust design method) divides the properties of collaboration into two categories, i.e., noise and control factors. Based on the noise factors, Taguchi employs signal-to-noise (S/N) to quantify the response variation values of optimizers, cf., e.g., Hapsari et al ( 2019 ), Mirjalili and Lewis ( 2016 ), Fathollahi-Fard et al ( 2020c ). Considering the model under this study is a minimization, the lower value of S/N, the more preferable.…”

“…If the new solution outperforms the initial one, the initial solution will then be replaced with the new solution. It is worth noting that a rule must be devised to evaluate solution performance, and readers may refer to the works of Sahebjamnia et al ( 2018 ), Fathollahi-Fard et al ( 2020b ), Safaeian et al ( 2019 ), Buddala and Mahapatra ( 2019 ), Hapsari et al ( 2019 ) for solution evaluation of SA algorithm. However, the model in this study is a tri-objective problem, which makes the solution evaluation complicated.…”

“…There have been several bioinspired algorithms developed to solve various NP-hard problems in recent years (Buddala and Mahapatra 2019 ; Hapsari et al 2019 ), and the shared feature of those algorithms lies in the solution search phase. By balancing the search of solutions (i.e., intensification and diversification), the bioinspired algorithms tend to find the global solutions efficiently, rather than several local solutions.…”

Multi-Objective Trip Planning With Solution Ranking Based on User Preference and Restaurant Selection

“…Unfortunately, there are only a few papers that address the problem. They are Karimi and Bashiri [32], Hapsari et al [33], and Falco et al [34] [35] applied TOPTW with five objectives: maximize profit score, number of preferred POIs of the tourists, number of recommended POIs, time of the tour, and minimize the total distance. On the other hand, Mirzaei et al [36] attempted to balance the satisfaction level for each day.…”

Tourist trip design with heterogeneous preferences, transport mode selection and environmental considerations

“…Similarly, it is possible to consider the construction of routes that have more than one starting point (i.e., multiple deposits). Finally, in terms of the development of approximate methods, the use of hybrid methods may represent an improvement approach for the solutions of this type of problems (Hapsari et al, 2019 ), in addition to being a novel contribution to the literature due to their scarce application in the TTDP (Ruiz-Meza & Montoya-Torres, 2020 ).…”