Solving the Uncapacitated Single Allocation p-Hub Location Problem with Fixed Cost Using Anti-Predatory NIA

Abstract

Background: Hub Location Problem (HLP) deals with long-term strategic decision planning in various application domains with the aim of reducing overall transportation cost. It deals with identifying hubs and allocating spokes to hubs in order to route the flow of goods between origin-destination locations. Due to the complex nature of the problem, meta-heuristic algorithms are best suited to solve HLPs. The existing algorithms face the accuracy and consistency related issues for solving the HLPs. Objective: This paper attempts to solve a variant of HLP, which is known as Uncapacitated Single Allocation p-Hub Location Problem with Fixed Cost (USApHLP-FC), using Anti-Predatory Nature- Inspired Algorithm (APNIA) to improve accuracy and consistency in results. Methods: APNIA is a recently proposed meta-heuristic nature-inspired algorithm, which is based on the anti-predatory behavior of frogs. For solving the HLP, APNIA is used for both identifying the hubs and allocating the spokes to hubs in order to reduce the total cost of goods transportation. Results: A numerical problem with 10 locations is used for empirical study. The experimental result shows that APNIA outperforms other leading proposals in terms of total cost and gap value. The obtained results of APNIA are compared with the genetic algorithm, particle swarm optimization, artificial bee colony, firefly algorithm, teacher learning based optimization and Jaya algorithm. The comparative study indicates at least 0.86% improvement in accuracy and at least a 10% gain in consistency by APNIA for the different number of generations. Conclusion: The experimental evaluation and performance comparison signify that APNIA based approach has improved accuracy and consistency in solutions than other compared algorithms. It establishes the robustness of anti-predatory NIA for solving the hub location problems.