A review of metaheuristic algorithms for solving TSP-based scheduling optimization problems - Toaza, Bladimir; Esztergár-Kiss, Domokos (Applied soft computing, 2023)
Activity-based scheduling optimization is a combinatorial problem built on the traveling salesman problem intending to optimize people schedules considering their trips and the available transportation network. Due to the difficulty of scheduling, traditional and exact methods are unable to provide appropriate solutions. Hence, new approaches have been introduced in the literature to settle these complex problems. One group of new techniques is known as metaheuristic algorithms, which provides a robust family of problem-solving methods created by mimicking natural phenomena. Although these new techniques might not find an optimal solution, they can find a near-optimal one in a moderate period. Furthermore, a myriad of novel algorithms has been introduced making it tedious for academics to select the appropriate technique. Thus, this paper investigates the contribution of metaheuristics to solve transportation-related optimization problems. To achieve this aim, we conducted a bibliometric analysis, and defined the descriptive and assessment features for 120 metaheuristics. The findings of the study reveal the usage tendencies of the algorithms, identify the most prevalent ones, and highlight those metaheuristics that have a potential use in upcoming research. The results demonstrate that the most applied metaheuristic algorithm is the genetic algorithm, but the ant colony optimization algorithm is the most popular one based on the number of citations. Lastly, we open a discussion on a few unexplored research gaps and expectations. •Review of 120 metaheuristics solving TSP-based scheduling optimization problems.•Tabular summary of descriptive and assessment features of the metaheuristics.•Automation of large amount of search terms using a programming language and an API.•GA is the most applied algorithm in publications, but ACO is the most cited one.