Evolutionary learning algorithm for reliable facility location under disruption

• Published in: Expert systems with applications 2019-01, Vol.115, p.223-244
• Main Authors: Afify, Badr, Ray, Sujoy, Soeanu, Andrei, Awasthi, Anjali, Debbabi, Mourad, Allouche, Mohamad
• Format: Article
• Language: English
• Subjects: Combinatorial optimization; Computing costs; Disruption; Evolutionary algorithms; Evolutionary learning; Facility location; Genetic algorithms; Heuristic; Heuristics; Machine learning; Operations research; Optimization algorithms; Reliability; Site selection; Supply chains
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2018.07.045

•Investigation of two reliable facility location problems under disruption.•Elaboration of an evolutionary learning based solution generation approach.•Application of the approach over an illustrative example and benchmark datasets.•Comparative analysis and benefit assessment against previously obtained results.•Sensitivity analysis on demand priority and different distance calculation methods. Facility location represents an important supply chain problem aiming at minimizing facility establishment and transportation cost to meet customer demands. Many facility location problem (FLP) instances can be modelled as p-median problems (PMP) and uncapacitated facility location (UFL) problems. While, most solution approaches assume totally reliable deployed facilities, facilities often experience disruptions and their failure often leads to a notably higher cost. Therefore, determination of facility locations and fortification of a subset of them within a limited budget are crucial to supply chain organizations to provide cost effective services in presence of probable disruptions. We propose an evolutionary learning technique to near-optimally solve two research problems: Reliable p-Median Problem and Reliable Uncapacitated Facility Location Problem considering heterogeneous facility failure probabilities, one layer of backup and limited facility fortification budget. The technique is illustrated using a case study and its performance is evaluated via benchmark results. We also provide an analysis on the effects on facility location by prioritizing customer demands and adopting geographic distance calculation. The approach allows fast generation of cost-effective and complete solution using reasonable computing power. Moreover, the underlying technique is customizable offering a trade-off between solution quality and computation time.