Work

Managing Demand and Travel Time Uncertainties in Pandemic Emergencies: A Risk-Averse Multi-Objective Location- Routing Model

Year: 2026

Type: article

Abstract: During pandemic emergencies, demand for relief supplies in affected areas surges abruptly and evolves randomly and dynamically, resulting in highly asymmetric supply and demand. Ensuring timely and reliable supply requires robust decision-making under risk. This study addresses a stochastic multi-objective location-routing problem (LRP) that simultaneously considers demand uncertainty and travel time variability. A multi-scenario stochastic programming model is developed with three objectives: minimizing total system cost, minimizing total waiting time, and minimizing the composite conditional value at risk (CVaR–Rcomp) to capture tail risks under extreme scenarios. A novel regret-based risk mechanism is introduced to unify temporal and cost dimensions, enabling joint evaluation of uncertainties within a single framework. To solve this challenging high-dimensional problem, a reinforcement learning-enhanced NSGA-III (RL-NSGAIII) is proposed. Specifically, Q-learning generates high-quality initial solutions, which accelerate convergence and improve population diversity for NSGA-III. Case studies demonstrate that the proposed method outperforms traditional evolutionary algorithms in convergence efficiency and Pareto solution quality, while effectively revealing potential risk blind spots. The results provide quantitative decision support and robust optimization insights for emergency logistics networks operating under uncertain conditions.

Source: Symmetry

DOI: 10.3390/sym18030534

Authors: Fenggang Li, Xiaodong Sun, Bangxing Xue, Jing Zhang, Pengpeng Yao, Qingbin Zou

Institution: Hefei University of Technology

Language: English

Topic: Facility Location and Emergency Management

Subfield: Organizational Behavior and Human Resource Management

Field: Business, Management and Accounting

Domain: Social Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $2,896