Fuzzy location and allocation Hub Network Design for Air Cargo Transportation Considering Sustainability and Time Window

TL;DR

This paper develops a fuzzy, multi-criteria hub network design model for air cargo transportation that considers sustainability, time windows, and demand uncertainty, solved using advanced optimization algorithms.

Contribution

It introduces a novel multi-objective fuzzy programming model for air cargo hub network design considering multiple sustainability criteria and demand uncertainty.

Findings

NSGA-II outperforms other algorithms in solution quality.

Increasing demand uncertainty raises costs and pollution.

The model effectively balances economic, environmental, and social goals.

Abstract

Hub location Problems seek to find hub facilities and assign non-hub nodes to them in such a way that the flow between origin and destination should be effectively established according to the desired goal. In general, in the literature of location, it is assumed that the time horizon of hub network design is a single time horizon. In the last two decades these problems have attracted special attention in the field of facility location problems and have wide applications in different fields including air cargo transportation. Cargo transportation is one of the most important economic sectors of any country. There are different ways to transport cargo, but air transport is preferred because it has high speed and security, so it is suitable for transporting goods related to technology, food, medicines, etc. In this article designing a hub network for air cargo transportation, taking into account hard and soft time windows along with considering the limited capacity for each hub under uncertainty is discussed. The proposed model is a developed model of an existing model in literature. Our Study has three linear functions: economic, environmental and social. In this article method of Fuzzy programming has been used to control the non-deterministic demand parameter. Results of model that has been solved by epsilon limitation method, NSGA-II, MOPSO and MOWOA algorithm show that as the uncertainty rate increases, the total costs of the system as well as the amount of environmental pollution increases. The reviews indicate the high performance of the NSGA-II algorithm in solving the proposed model.