01883nas a2200217   4500000000100000000000100001008004100002260001200043653002800055653002300083653001700106653002200123100002000145700002000165245009800185856007900283300001000362490000600372520127300378022001401651       2022                            d  c09/202210aAnt Colony Optimization10aGenetic Algorithms10aOptimization10aTelecommunication1 aJoão Henriques1 aFilipe Caldeira00aA Model for Planning TELCO Work-Field Activities Enabled by Genetic and Ant Colony Algorithms  uhttps://www.ijimai.org/journal/sites/default/files/2022-09/ijimai7_6_3.pdf  a24-300 v73 aTelecommunication Company’s (TELCO) are continuously delivering their efforts on the effectiveness of their daily work. Planning the activities for their workers is a crucial sensitive, and time-consuming task usually taken by experts. This plan aims to find an optimized solution maximizing the number of activities assigned to workers and minimizing the inherent costs (e.g., labor from workers, fuel, and other transportation costs). This paper proposes a model that allows computing a maximized plan for the activities assigned to their workers, allowing to alleviate the burden of the existing experts, even if supported by software implementing rule-based heuristic models. The proposed model is inspired by nature and relies on two stages supported by Genetic and Ant Colony evolutionary algorithms. At the first stage, a Genetic Algorithms (GA) identifies the optimal set of activities to be assigned to workers as the way to maximize the revenues. At a second step, an Ant Colony algorithm searches for an efficient path among the activities to minimize the costs. The conducted experimental work validates the effectiveness of the proposed model in the optimization of the planning TELCO work-field activities in comparison to a rule-based heuristic model.  a1989-1660