Stochastic Runway Scheduling Problem With Partial Distribution Information of Random Parameters

Ming Liu,Bian Liang,Maoran Zhu,Chengbin Chu

doi:10.1109/access.2020.2984513

Abstract

Air traffic flow management is one of the most important operations in terminal airports heavily relying on advanced intelligence transportation techniques. This work considers a two-stage runway scheduling problem given a set of flights with uncertain arrival times. The first-stage problem is to identify a sequence of aircraft weight classes (e.g., Heavy, Large and Small) that minimizes runway occupying time (i.e., makespan). Then the second-stage decision is dedicated to scheduling the flights as punctually as possible after their arrival times realized, which translates into determining a sequence of flights for each aircraft category such that the total deviation time imposed on the flights is minimized. Instead of an exactly known probability distribution, information on uncertain parameters is limited (i.e., ambiguous), such as means, mean absolute deviations and support set of random parameters derived from historical data. Under this information on the random parameters, an ambiguous mixed-integer stochastic optimization model is proposed. For such a problem, we approximately construct a worst-case discrete probability distribution with three possible realizations per random parameter, and adopt a hybrid sample average approximation algorithm in which genetic algorithms are used to replace commercial solvers. To illustrate the effectiveness and efficiency of the proposed model and algorithm, extensive numerical experiments are carried out.

Highlights

The demand of passengers on air transport is increasing with the growth of national economy
Motivated by the more practical approach for practitioners, in this work, we consider the case of ambiguous distribution characterized by partial distribution information on flight’s arrival time, and concentrate on solving the ambiguous stochastic runway scheduling problem (ASRSP) by using heuristic algorithm to boost the Time-Based Flow Management (TBFM) technique used in intelligence air transportation system
This work studies an ambiguous two-stage stochastic runway scheduling problem with partial distribution information available, in which the uncertainty mainly arises from the random aircraft arrival times

Summary

INTRODUCTION

The demand of passengers on air transport is increasing with the growth of national economy. For these reasons, we propose an ambiguous two-stage stochastic programming model involving integer decision variables, in which the probability distribution on random parameters is partially known. Motivated by the more practical approach for practitioners, in this work, we consider the case of ambiguous distribution characterized by partial distribution information on flight’s arrival time, and concentrate on solving the ambiguous stochastic runway scheduling problem (ASRSP) by using heuristic algorithm to boost the TBFM technique used in intelligence air transportation system. It is the first time to consider an ambiguous stochastic optimization problem with random aircraft arrival time in RSPs. The main contributions of this work are summarized as follows: 1) We address the ambiguous two-stage stochastic programming runway scheduling problem which aims to improve the runway operations with partial distribution information available.

LITERATURE REVIEW

APPROXIMATE WORST-CASE DISCRETE DISTRIBUTION

SAMPLE AVERAGE APPROXIMATION ALGORITHM FRAMEWORK

NUMERICAL EXPERIMENT

Findings

CONCLUSION