Heuristics to solve the integrated airline crew assignment problem

Abstract

Article Info AbstractKeywords: air transportation airline crew assignment combinatorial optimization heuristics A typical problem related to airline crew management consists of optimally assigning the required crew members to planned flights for a given period of time, while complying with a variety of labor regulations, safety rules and policies of the airline. This problem, called crew assignment problem (CAP), is of the NPHard class. So, it is usually divided into two independent subproblems, crew pairing problem (CPP) and crew rostering problem (CRP), modeled and solved sequentially. This division does not provide a global treatment to the CAP in terms of total cost and quality of the final solution. IntroductionThis paper presents the main results of a research addressed to solve the crew assignment problem (CAP) with a proposed heuristic procedure. The importance of this type of approach is given by the combinatorial nature of the problem, which turns it impossible to be solved through exact models for large instances. The proposed heuristics presented results equal to or better than those obtained by exact models for the tested instances related to small and medium-sized Brazilian airlines (up to 894 flights and 36 crew members), with reduced CPU times (less than 5 seconds). Exact models led to feasible solutions in instances with up to 656 flights and 20 crew members.The CAP treated in this paper is defined as the problem of assigning the required crew members of the same category (only technical crew, in this case) to a set of flights of a given aircraft type, such that it minimizes the total cost of the aircrew, taking into consideration the proper legislation and the satisfaction of the crew members.CAP has been decomposed in the literature into two subproblems, both of which are solved sequentially: crew pairing problem (CPP) and crew rostering problem (CRP). The CPP seeks to provide an optimal set of pairings that covers all the planned flights. Then, in the CRP, the best combination of rosters (composed by the pairings of CPP and other activities) to crew members is determined, seeking the optimal coverage of planned flights and, eventually, the balancing of the total flying time among the crew members (Andersson et al., 1998;Barnhart et al., 2003; Kohl and Karisch, 2004;Gopalakrishnan and Johnson, 2005).Pairing (or crew rotation) is the work accomplished by crew member starting and ending at the same home base, featuring a cycle. A pairing can be formed by one or more duty periods, which are series of sequential flights comprising a workday for a crew member.Subproblems CPP and CRP are usually modelled as a set partitioning (or covering) problem, and solved through approaches based on heuristics or metaheuristics. The exact methods are only used for small-sized problems (Andersson et al., 1998;Barnhart et al., 2003;Cabral et al., 2000;Kohn and Karisch, 2004;Gopalakrishnan and Johnson, 2005;Lucic and Teodorovic, 2007;Maenhout and Vanhoucke, 2010).This decomposition does not incorporate all the cre...

A new mathematical model to cover crew pairing and rostering problems simultaneously

“…The obtained results on the real data showed a higher efficiency of the proposed method. Gomes and Gualda (2015) presented a new constructive heuristic algorithm that generated integer solutions during a reasonable time for the problems of CSP with small and medium sizes. The results showed superiority in comparison to the previous results.…”

An exact model for airline flight network optimization based on transport momentum and aircraft load factor

“…In Crew Pairing, lights are grouped into sequences called pairings, which must respect labor laws and technical criteria. In Crew Rostering, the pairings -and, thus, the lights -are assigned to the crew members (BARNHART et al, 2003;GOMES, 2014;GOMES & GUALDA, 2011;GOMES & GUALDA, 2015;KLABJAN, 2004;RABETANETY et al, 2006). The competition among airlines and the complexity of aircraft, crew and passenger management lead to large-scale models for airlines operational planning.…”

“…Seeking global optimization, models are addressed to solve two or more subproblems simultaneously, increasing the computational complexity (KLABJAN, 2004;SHERALI et al 2013;RABETANETY et al, 2006). Consequently, heuristics and metaheuristics approaches have been proposed, such as the one by Caetano and Gualda (2011), addressed to solve the Schedule Generation and Fleet Assignment integrated problem with an Ant Colony Metaheuristic approach, and the ones by Gomes and Gualda (2011;2015), for solving the integrated Airline Crew Assignment problem.…”

“…Each step is optimized based on different objective functions: the Flight De inition step is usually solved using marketing, revenue, aircraft availability and other operational restrictions as optimization criteria (KLABJAN, 2004;RABETANETY et al, 2006); the Aircraft Assignment step usually involves mainly the consideration of costs and revenues to maximize pro itability (HANE et al, 2004;KLABJAN, 2004;SHERALI et al, 2006); and, inally, the Crew Assignment step is based on the availability of human resources, legal matters and costs (BARNHART et al, 2003;GOMES, 2014;GOMES & GUALDA, 2011;GOMES & GUALDA, 2015;KLABJAN, 2004).…”