Dynamic Scheduling Strategy to solve Aircraft Landing Problem

Ayman Almabruk A. M. (u. a.)

ISBN 10: 6204983520 / ISBN 13: 9786204983523

Published by LAP LAMBERT Academic Publishing, 2022

About this Item

Dynamic Scheduling Strategy to solve Aircraft Landing Problem | Ayman Almabruk A. M. (u. a.) | Taschenbuch | Englisch | 2022 | LAP LAMBERT Academic Publishing | EAN 9786204983523 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.

Seller Inventory # 122465021

Bibliographic Details

• Title: Dynamic Scheduling Strategy to solve Aircraft Landing Problem
• Publisher: LAP LAMBERT Academic Publishing
• Publication Date: 2022
• Language: English
• ISBN 10: 6204983520
• ISBN 13: 9786204983523
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 9D millimeters 220 millimeters width by 150 millimeters height by 9 millimeters depth
• Weight: 221 grams
• Seller catalogs:
  • Bücher

About this title

Synopsis

Over the last 70 years, airlines have developed from basic contract mail carriers to complex corporations. The present airline industry is very competitive and dynamic. The real-time solution of large-scale optimization models has had a considerable impact on the aviation sector today. Finding an ideal schedule for aircraft landings is becoming increasingly difficult. Airlines have had a difficult time designing an aircraft schedule, that is, the timetable and corresponding personnel on each flight leg, to fit shifting passenger demand. Each aircraft's landing time must fall within a time interval that includes a target landing time. This study provides two scheduling strategies for resolving the aircraft landing problem: (i) A Dynamic Aircraft scheduling method using optimized First Come First Serve (FCFS) centered on Krill Herd Optimization Algorithm (ii) An Optimized Entropy-based scheduling technique using optimized FCFS centered on Cuckoo Search Optimization Algorithm. The proposed scheduling technique was compared with an existing memetic algorithm in terms of computational time, CPU time, target deviation, and scheduling.