This paper presents the design and implementation of an air-crew assignment system for producing and refining a solution to this problem based on the Artificial Intelligence principles and techniques of abductive reasoning as captured by the framework of Abductive Logic Programming (ALP). The system offers a high-level of flexibility in addressing both the tasks of crew scheduling and rescheduling. It can be used to generate a valid and good quality initial solution and then to help the human operators adjust and refine further this solution in order to meet extra requirements of the problem. These additional needs can arise either due to new foreseen requirements that the company wants to have or experiment with for a particular period in time or due to unexpected events that have occurred while the solution (crew-roster) is in operation. Our work shows the ability and flexibility of abduction, and more specifically of ALP, in tackling problems of this type with complex and changing r...

. This paper presents the design and implementation of an aircrew assignment system based on the Artificial Intelligence principles and techniques of abductive reasoning as captured by the framework of Abductive Logic Programming (ALP). The aim of this work was to produce a system for Cyprus Airways that can be used to provide a solution to the airline's crew scheduling problem whose quality was comparable with the manual solutions generated by human experts on this particular problem. In addition to this the system should also constitute a tool with which its operators could continually customize the solutions to new needs and preferences of the company and the crew. The abductive approach (using ALP) adopted in our work offers a flexible modeling environment in which both the problem and its constraints can be easily represented directly from their high-level natural specification. This high-level representation offers two main advantages in the development of an applica...

Abstract. We present an algorithm for the crew pairing problem, an optimization problem that is part of the airline crew scheduling procedure. A pairing is a round trip starting and ending at the home base, which is susceptible to constraints that arise due to laws and regulations. The purpose of the crew pairing problem is to generate a set of pairings with minimal cost, covering all flight legs that the company has to carry out during a predefined time period. The proposed solution is a two-phase procedure. For the first phase, the pairing generation, a depth first search approach is employed. The second phase deals with the selection of a subset of the generated pairings with near optimal cost. This problem, which is modelled by a set covering formulation, is solved with a genetic algorithm. The presented method was tested on actual flight data of Olympic Airways. 1

In this paper, we deal with the crew assignment problem, which is a subproblem of the airline crew scheduling problem. The aim of the crew assignment problem is the optimal allocation of a given set of crew pairings to crew members, in a way that a set of constraints is satisfied. The optimality criterion we employ in this work requires the flight time fair distribution among all crew members. This problem has been traditionally tackled with Operations Research techniques. In recent years, the Constraint Logic Programming paradigm has been successfully used for solving hard combinatorial optimization problems. We propose a formulation of the crew assignment problem as a constraint satisfaction problem and we use a branch-and-bound technique combined with some heuristics in order to find quickly a solution identical, or at least very close, to the optimal.

Abstract. In the ever increasing competitive environment of the airline industry, efficient personnel’s planning is among the most challenging tasks and solely responsible for the largest impact on an airline’s cost structure. The problem is theoretically appealing because it is computationally difficult due to the huge number of possibilities, and the amount of rules that determine crew planning. This work proposes a methodology to determine the most efficient and least costly way to serve the flights in an airline schedule during a weekly planning horizon considering layovers and deadheads. The strategy is composed of three optimization models: a binary programming model, a model based on the set partitioning problem, and a third model based on the set covering problem, the two latter solved via column generation. Computational results for the three models are presented using test instances built from a mid-sized Colombian airline.