Hub Location under Uncertainty: a Minimax Regret Model for the Capacitated Problem with Multiple Allocations

TL;DR

This paper introduces a minimax regret model for the capacitated hub location problem with multiple allocations, accounting for demand and cost uncertainties to find robust, practical solutions.

Contribution

It presents an efficient formulation for the uncertain capacitated hub location problem with multiple allocations, incorporating stochasticity and providing a robust solution approach.

Findings

The model effectively accounts for demand and cost uncertainty.

Compared to seasonal optimization, the model offers improved efficiency and practicality.

Application to a real case demonstrates robust hub network solutions under uncertainty.

Abstract

In this paper the capacitated hub location problem is formulated by a minimax regret model, which takes into account uncertain setup cost and demand. We focus on hub location with multiple allocations as a strategic problem requiring one definite solution. Investigating how deterministic models may lead to sub-optimal solutions, we provide an efficient formulation method for the problem. A computational analysis is performed to investigate the impact of uncertainty on the location of hubs. The suggested model is also compared with an alternative method, seasonal optimization, in terms of efficiency and practicability. The results indicate the importance of incorporating stochasticity and variability of parameters in solving practical hub location problems. Applying our method to a case study derived from an industrial food production company, we solve a logistical problem involving seasonal demand and uncertainty. The solution yields a definite hub network configuration to be implemented throughout the planning horizon.