Metaheuristics for the stochastic post-disaster debris clearance problem

Abstract

Post-disaster debris clearance is of utmost importance in disaster response and recovery. The goal in planning debris clearance operations in emergency response is to maximize road network accessibility and enable transport of casualties to medical facilities, primary relief distribution to survivors, and evacuation of survivors from the affected region. We develop a novel stochastic mathematical model to represent the debris clearance scheduling problem with multiple cleaning crews. The inherent uncertainty in the debris clearance planning problem lies in the estimation of clearance times for road debris. The durations required to clear road segments are estimated by helicopter surveys and satellite imagery. The goal is to maximize network accessibility throughout the clearance process. The model creates a schedule that takes all clearing time scenarios into consideration. To enable the usage of the model in practice, we also propose a rolling horizon approach to revise the initial schedule based on updated clearance time estimates received from the field. We use the Sample Average Approximation method to determine the number of scenarios required to adequately represent the problem. Since the resulting mathematical model is intractable for large-scale networks, we design metaheuristics that utilize Biased Random Sampling, Tabu Search, Simulated Annealing, and Variable Neighborhood Search algorithms.

Keywords:

• Debris clearance
• disaster response
• metaheuristics
• sample average approximation
• stochastic optimization

Additional information

Notes on contributors

Elifcan Yaşa

Elifcan Yaşa completed her BS degree in industrial engineering at Doğuş University in 2011 and received her MS in systems engineering from Yeditepe University in 2015. She is a PhD candidate at Yeditepe University. Her primary research interests include mathematical modeling, combinatorial optimization, metaheuristics, and disaster logistics.

Dilek Tüzün Aksu

Dilek Tüzün Aksu completed her BS degree in industrial engineering at Boğaziçi University in 1992 and received her PhD in the same field from Lehigh University in 1998. After graduating from Lehigh, she joined the Research and Development group within the Information Services Division at United Airlines. During her 6-year tenure at United, she led a range of R&D projects in the areas of inventory planning, supply chain management, scheduling, and manpower planning. Since 2005, she has been a professor at the Industrial Engineering Department of Yeditepe University in Istanbul, Turkey. Her primary research interests include mathematical modeling and combinatorial optimization with particular emphasis on problems in airline and airport operations, routing, public transportation, disaster management, and manufacturing.

Linet Özdamar

Linet Özdamar graduated from the Department of Industrial Engineering at Boğaziçi University Istanbul Turkey. She has worked as a professor in several universities since 1992 and currently holds the same title at Yeditepe University. Dr. Özdamar’s area of research started with project scheduling, moved on to hierarchical production planning, supply chains, global optimization, interval methods for discrete and nonlinear programming, environmental site mapping, disaster logistics/supply chains, and infrastructure restoration, always focusing on efficient modeling techniques and solution algorithms. Having published almost 80 journal articles in these areas, Dr. Özdamar has been listed as the 217th globally most influential author in the area of Operational Research by a Stanford University-based research published in 2019.