A Greedy Randomized Adaptive Search With Probabilistic Learning for solving the Uncapacitated Plant Cycle Location Problem

In 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.