Optimization Model for an Airline Crew Rostering Problem: Case of Garuda Indonesia

Hadianti, Rieske; Novianingsih, Khusnul; Uttunggadewa, Saladin; Sidarto, Kuntjoro Adji; Sumarti, Novriana; Soewono, Edy

doi:10.5614/j.math.fund.sci.2013.45.3.2

Cited by 13 publications

(4 citation statements)

References 14 publications

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“…Santosa et al (2010) used the Differential Evaluation (DE) algorithm to solve CRP and finally compared it with the results of the column generation method in different numerical examples. Moreover, Hadianti et al (2013) proposed a new nonlinear integer model and solved it using the Simulated Annealing (SA) algorithm. The results showed that this approach was well-defined for the problem and could achieve good solutions over a short time.…”

Section: Literature Reviewmentioning

confidence: 99%

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

Saemi

Komijan

Tavakkoli–Moghaddam³

et al. 2021

JER is an international, peer-reviewed journal that publishes f

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Crew scheduling problem includes two separate subproblems, namely, crew pairing and crew rostering problems. Solving these two subproblems in a sequential order may not lead to an optimal solution. This study includes two main novelties. It combines these two subproblems and presents them in a single model. On the other hand, despite previous researches that considered a pairing continuously, the proposed model benefits from the capability of considering one or more days off in a pairing assigned to a crew member. This is extremely useful as it enables the crew to participate in required courses, doing medical checks, etc. Two solution approaches, namely, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), are used to solve the model. Eventually, the performance of the proposed algorithms is evaluated. Both ended to satisfactory results; however, PSO relatively outperformed GA in terms of solution optimality and computational time.

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Section: Literature Reviewmentioning

confidence: 99%

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

Saemi

Komijan

Tavakkoli–Moghaddam³

et al. 2021

JER is an international, peer-reviewed journal that publishes f

View full text Add to dashboard Cite

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“…However, it is also a tool for improving the quality of the working environment, so it is necessary to take this criterion into account during crew rostering [15]. Due to specific personal requirements, some authors solve the optimization of crew rostering in direct cooperation with airlines, such as in [16], where the authors apply a heuristic method for the solution using the method of simulated annealing. The authors of [17] chose an alternative approach, following up on their work presented in [18].…”

Section: Crew Schedulingmentioning

confidence: 99%

Dynamic Model of Contingency Flight Crew Planning Extending to Crew Formation

et al. 2021

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The creation of a flight schedule and the associated crew planning are clearly among the most complicated tasks in terms of traffic preparation. Even with a relatively small number of pilots and aircraft, numerous specific constraints arising from real operations must be included in the calculation, thus increasing the complexity of the planning process. However, even in a precision-planned operation, non-standard situations often occur, which must be addressed flexibly. It is at this point that an operational solution must be applied, the aims of which are to stabilize the flight schedule as soon as possible and minimize the financial impacts resulting from the non-standard situation. These problems are resolved by the airline’s Operational Control Center, which also uses various software approaches to solve the problem. The problem is approached differently by large air carriers, which use software products to address it, and small and medium-sized air carriers, which resolve the issue of operational rescheduling intuitively, based on the experience of dispatchers. However, this intuitive approach can lead to inaccuracies that can lead to unnecessary financial losses. In this paper, we present an optimization model that can serve as a tool to support the decision-making of employees of the operations centers of smaller and medium-sized air carriers.

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“…There are 2 distinct indices 1 ≤ 1 , 2 ≤ so that , 1 = , 2 = 1 and , = 0 for ≠ 1 , 2 . All flights having been coupled, the next step is finding two or more couples that can be combined as a pair, using the technique introduced in [6].…”

Section: Construction Of Flight Couples and Pairingsmentioning

confidence: 99%

Optimization of Personnel Cost in Aircrew Assignment Problem using a Simple Fuzzy Logic Approach

Sumarti¹,

Chandra²,

Minardi³

2017

mendel

Self Cite

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In aviation industries, the aircrew assignment problem is one of the most important factors in total operational cost optimization. This problem will be solved in two steps: flight pairing and aircrew scheduling. The constraints to be satisfied in flight pairing include having the same airport for first departure and final destination, and the limitations of flying time, duty time and transit time. The optimization process results in optimal flight pairings that minimize the number of personnel needed to serve a flight schedule over a given period of time. Further optimization is needed to obtain a schedule in which an aircrew team can serve a rotation with the largest possible number of pairings on the condition that all constraints are fulfilled. For aircrew scheduling, there are constraints on flying time, resting time, total number of takeoffs, and number of holidays and workdays. The investigated optimization process was designed to get optimal rotations along with maximum total personnel cost reduction. The data set used in this research is a one-month full flight schedule from a big airline in Indonesia. A simple fuzzy logic approach was used to find a new flying time constraint in order to optimize personnel cost and evenly distribute the assignments. The results show that the new optimal flying time constraint can reduce personnel cost up to 5.07% per month, so it can yield significant savings on a yearly basis.

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Optimization Model for an Airline Crew Rostering Problem: Case of Garuda Indonesia

Cited by 13 publications

References 14 publications

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

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

Dynamic Model of Contingency Flight Crew Planning Extending to Crew Formation

Optimization of Personnel Cost in Aircrew Assignment Problem using a Simple Fuzzy Logic Approach

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