A multi-objective risk-based approach for airlift task scheduling using stochastic bin packing

Wenjing Zhao, Jing Liu, Hussein A. Abbass, Axel Bender

Abstract: An important aspect of airlift problems is to find the smallest fleet of aircraft to move cargo from one or more locations to a destination. In critical airlift operations, such as emergency evacuations, disaster relief and defence operations, a compromise needs to be struck between minimizing the time needed for completing all tasks and minimizing the size of the fleet. Usually, the time to complete a task is stochastic. A deterministic model, therefore, will under-estimate fleet size which results in increased levels of risk to achieve the overall airlift mission. In this paper, we introduce a stochastic version of the two-dimensional bin packing problem. We test a number of objective functions to measure different levels of risk. We then use an evolutionary multi-objective algorithm to solve a number of test problems. Analysis demonstrates that the different risk functions and level of variability/uncertainty in performing each task affect solutions non-linearly. Moreover, the multi-objective approach provides the analyst with an estimate of the range of risk; thus solutions can be selected based on criticality of meeting airlift demands.