01524nas a2200241   4500000000100000000000100001008004100002260001200043653002200055653002700077653002700104100002400131700003600155700002300191700002700214700002600241245012700267856008000394300001200474490000600486520077600492022001401268       2023                            d  c06/202310aGreedy Randomized10aProbabilistic Learning10aCycle Location Problem1 aIsrael López-Plata1 aChristopher Expósito-Izquierdo1 aEduardo Lalla-Ruiz1 aBelén Melián-Batista1 aJ. Marcos Moreno-Vega00aA Greedy Randomized Adaptive Search With Probabilistic Learning for solving the Uncapacitated Plant Cycle Location Problem  uhttps://www.ijimai.org/journal/sites/default/files/2023-05/ijimai8_2_12.pdf  a123-1330 v83 aIn this paper, we address the Uncapacitated Plant Cycle Location Problem. It is a location-routing problem aimed at determining a subset of locations to set up plants dedicated to serving customers. We propose a mathematical formulation to model the problem. The high computational burden required by the formulation when tackling large scenarios encourages us to develop a Greedy Randomized Adaptive Search Procedure with Probabilistic Learning Model. Its rationale is to divide the problem into two interconnected sub-problems.
The computational results indicate the high performance of our proposal in terms of the quality of reported solutions and computational time. Specifically, we have overcome the best approach from the literature on a wide range of scenarios.  a1989-1660