Streamlining emergency response: A K-adaptable model and a column-and-constraint-generation algorithm

Abstract

Emergency response refers to the systematic response to an unexpected, disruptive occurrence such as a natural disaster. The response aims to mitigate the consequences of the occurrence by providing the affected region with the necessary supplies. A critical factor for a successful response is its timely execution, but the unpredictable nature of disasters often prevents quick reactionary measures. Preallocating the supplies before the disaster takes place allows for a faster response, but requires more overall resources because the time and place of the disaster are not yet known. This gives rise to a trade-off between how quickly a response plan is executed and how precisely it targets the affected areas. Aiming to capture the dynamics of this trade-off, we develop a K-adjustable robust model, which allows a maximum of K second-stage decisions, i.e., response plans. This mitigates tractability issues and allows the decision-maker to seamlessly navigate the gap between the readiness of a proactive yet rigid response and the accuracy of a reactive yet highly adjustable one. The approaches we consider to solve the K-adaptable model are twofold: Via a branch-and-bound method as well as a static robust reformulation in combination with a column-and-constraint generation algorithm. In a computational study, we compare and contrast the different solution approaches and assess their potential.

Paula Weller

Doctoral Researcher

Paula Weller is a doctoral reseracher at Aalto University investigating optimization under uncertainty.

Fabricio Oliveira

Associate Professor of Operations Research

Fabricio Oliveira is an Associate Professor of Operations Research at DTU Management. He also holds a position of Adjunct Professor in the Department of Mathematics and Systems Analysis at Aalto University