English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Crew Pairing Optimization aims at generating a set of flight sequences (crewpairings), covering all flights in an airline's flight schedule, at minimumcost, while satisfying several legality constraints. CPO is criticallyimportant for airlines' business viability, considering that the crew operatingcost is their second-largest expense. It poses an NP-hard combinatorialoptimization problem, to tackle which, the state-of-the-art relies on relaxingthe underlying Integer Programming Problem (IPP) into a Linear ProgrammingProblem (LPP), solving the latter through Column Generation (CG) technique, andintegerization of the resulting LPP solution. However, with the growing scaleand complexity of the flight networks (those with a large number of flights,multiple crew bases and/or multiple hub-and-spoke subnetworks), the utility ofthe conventional CG-practices has become questionable. This paper proposed anAirline Crew Pairing Optimization Framework, AirCROP, whose constitutivemodules include the Legal Crew Pairing Generator, Initial Feasible SolutionGenerator, and an Optimization Engine built on heuristic-basedCG-implementation. In this paper, besides the design of AirCROP's modules,insights into important questions related to how these modules interact, whichthe literature is otherwise silent on, have been shared. These relate to thesensitivity of AirCROP's performance towards: sources of variability overmultiple runs for a given problem, initialization method, and terminationparameters for LPP-solutioning and IPP-solutioning. The efficacy of the AirCROPhas been demonstrated on real-world large-scale and complex flight networks(with over 4200 flights, 15 crew bases, and billion-plus pairings). It is hopedthat with the emergence of such complex flight networks, this paper shall serveas an important milestone for affiliated research and applications.

Airline Crew Pairing Optimization Framework for Large Networks with Multiple Crew Bases and Hub-and-Spoke Subnetworks