Finding Outstanding Solutions for Multi-objective Optimization Problems

Choachaicharoenkul, Supoj; Wattanapongsakorn, Naruemon

doi:10.1145/3384613.3384623

Cited by 1 publication

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“…Then, the solution with the most extended length from itself perpendicular to the hyperplane is the knee solution. Several knee searching algorithms are based on this technique, e.g., [39][40][41].…”

Section: Itinerary Selection From Pareto Frontmentioning

confidence: 99%

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

2022

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The tourist trip design problem (TTDP) helps the trip planners, such as tourists, tour companies, and government agencies, automate their trip planning. TTDP solver chooses and sequences an optimal subset of point of interest (POIs), which adhere to the POIs attributes and tourist preferences, and then generates a travel itinerary that maximizes their pleasure. However, the traditional TTDP does not include the lunch period at a local restaurant, which causes the rest of the itinerary in the afternoon to shift, nor compulsory POIs that the trip planners must be included in the itinerary. Moreover, as tourism contributes to high greenhouse gas emissions, especially from its transportation, minimizing the itinerary's total distance is also considered. Unfortunately, this objective conflicts with the profit scores; no single itinerary can optimize both objectives simultaneously. Hence, the multi-objective technique and the results of non-dominated itineraries can be organized as a Pareto front. The trip planners can choose one suitable itinerary from the Pareto front based on their preferences. To address these real-world issues, we formulate a new variant of the well-known orienteering problem with time windows (OPTW) called the multi-objective orienteering problem with Time Windows, Restaurant Selection, and Compulsory POIs (MOPTW-RSCP). The proposed problem is provided with a mathematical formulation and two exact algorithms for solving them, i.e., greedy and branch-and-cut Pareto-based techniques. The algorithms' performance is tested against the Rattanakosin island (the old city of Bangkok) dataset. We conduct 24 test cases, and the computational results confirm the algorithms' efficiency.INDEX TERMS branch-and-cut algorithm, greedy algorithm, knee solution, low carbon tourism, multiobjective optimization, orienteering with time windows, point-of-interests, recommendation, solution ranking, tourist trip design problem

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Section: Itinerary Selection From Pareto Frontmentioning

confidence: 99%