63 research outputs found

    Solving the long haul crew pairing problem dc by Rajesh Gopaladrishna Shenoi.

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    Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 1994.Includes bibliographical references (leaves 107-118).M.S

    Column generation for airline problems

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    Issued as Progress report, and Final project report, Project no. E-24-66

    Fuzzy-logic controlled genetic algorithm for the rail-freight crew-scheduling problem

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    AbstractThis article presents a fuzzy-logic controlled genetic algorithm designed for the solution of the crew-scheduling problem in the rail-freight industry. This problem refers to the assignment of train drivers to a number of train trips in accordance with complex industrial and governmental regulations. In practice, it is a challenging task due to the massive quantity of train trips, large geographical span and significant number of restrictions. While genetic algorithms are capable of handling large data sets, they are prone to stalled evolution and premature convergence on a local optimum, thereby obstructing further search. In order to tackle these problems, the proposed genetic algorithm contains an embedded fuzzy-logic controller that adjusts the mutation and crossover probabilities in accordance with the genetic algorithm’s performance. The computational results demonstrate a 10% reduction in the cost of the schedule generated by this hybrid technique when compared with a genetic algorithm with fixed crossover and mutation rates

    Robust crew pairing for managing extra flights /

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    The airline industry encounters many optimization problems such as scheduling flights, assigning the fleet, scheduling the crew. Among them, the crew scheduling problem is the most studied one. The main reason is that the crew cost is one of the largest components of the operational cost for an airline company. Therefore, there are many models proposed in the literature to find a cost efficient crew schedule. Most of those models divide the crew scheduling problem into two separate problems, namely the crew pairing and the crew assignment problems. The crew pairing problem that we study here aims at finding the least costly subset of pairings, which cover the scheduled flights. Although there are many approaches to solve the crew pairing problem, most of them assume no disruptions during the operation. However disruptions due to weather conditions, maintenance problems, and so on are common problems leading to higher operational crew cost in practice. These kinds of disruptions result in delaying or canceling some scheduled flights. Another disruption that local airline companies face is adding an extra flight to predetermined (regular) flight schedule. In this study, we propose a model that provides robust crew pairing schedule in the case of adding an extra flight to the regular flight schedule. Two solution approaches are along with the mathematical model are proposed. The objective of the proposed model is to maximize the total number of solutions, while maintaining the increase in the crew cost at an acceptable level. A crew pairing problem is then solved by both the proposed model and the conventional model. Finally, computational experiments are conducted to demonstrate the benefits of the proposed model

    Optimisation simultanée des rotations et des blocs mensuels des équipages aériens

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    R´esum´e Le probl`eme int´egr´e de la construction des rotations et des blocs mensuels des pilotes consiste `a d´eterminer un ensemble de rotations et de blocs mensuels pour les pilotes tels que chaque segment de vol est couvert par une seule rotation et un seul bloc, et ce, tout en satisfaisant des contraintes suppl´ementaires comme la disponibilit´e des pilotes dans chaque base. Une rotation est une s´equence de vols effectu´ee par un ´equipage durant une p´eriode donn´ee partant et revenant `a la mˆeme base. Un bloc (ou horaire) mensuel est une s´equence de rotations s´epar´ees par des p´eriodes de repos. La construction des rotations et des blocs mensuels doit ˆetre conforme aux r`egles de la s´ecurit´e a´erienne, aux r`egles d’op´eration de la compagnie et aux r`egles contenues dans les conventions collectives entre les employ´es et la compagnie a´erienne. `A part l’introduction, la revue de litt´erature et la conclusion, cette th`ese est compos´ee de trois chapitres principaux dont chacun pr´esente les travaux r´ealis´ees pour un objectif de recherche bien pr´ecis. Ces trois chapitres utilisent les mˆemes instances du probl`eme bas´ees sur des donn´ees r´eelles fournies par une grande compagnie a´erienne am´ericaine. Le probl`eme de construction des rotations se r´esout traditionnellement en trois phases de mani`ere s´equentielle : un probl`eme journalier, un probl`eme hebdomadaire et un probl`eme mensuel. Cette approche interdit la r´ep´etition du mˆeme num´ero de vol dans une rotation. Le premier objectif de cette th`ese est de mettre en ´evidence deux faiblesses de cette approche s´equentielle et proposer `a la place une approche alternative qui permet la r´ep´etition des vols dans une mˆeme rotation. Premi`erement, nous montrons que lorsque l’horaire des vols est irr´egulier, les deux premi`eres phases ne sont qu’une perte de temps et on peut obtenir de meilleures solutions en moins de temps si le probl`eme mensuel est r´esolu directement en utilisant une approche d’horizon fuyant faisant appel `a une m´ethode de g´en´eration de colonnes. En effet, cette approche a permis de diminuer le gras de la solution de 34% en moyenne o`u le gras est une mesure de qualit´e portant sur le pourcentage du temps non travaill´e mais pay´e durant un horizon. Deuxi`emement, mˆeme si l’horaire des vols est compl`etement r´egulier, la qualit´e de la solution est meilleure si le probl`eme hebdomadaire est trait´e directement sans exploiter le probl`eme journalier. En effet, les diff´erents tests ont montr´e qu’une moyenne de 48.8% des rotations contiennent des r´ep´etitions causant une r´eduction moyenne de 16% dans le gras.----------Abstract The integrated crew pairing and crew assignment problem for pilots consists of producing a minimum-cost set of pairings and schedules such that each flight leg is covered once by one pairing and one schedule, and side constraints are satisfied such as pilot availability in each crew base station. A pairing is a sequence of duties separated by rest periods that must start and end at the same crew base. A duty is a sequence of flights separated by connections and ground waiting times, forming a working day for a crew. The construction of pairings and schedules must respect all safety and collective agreement rules. Besides the introduction, literature review and conclusion, this thesis is composed of three main chapters where each one presents the performed work for a specific research objective. These three chapters use the same problem instances based on real-data provided by a major US airline. The crew pairing problem has been traditionally solved in the industry by a heuristic three-phase approach that solves sequentially a daily, a weekly, and a monthly problem. This approach prohibits the repetition of the same flight number in a pairing. The first objective in this thesis is to highlight two weaknesses of the three-phase approach and propose an alternative solution approach that exploits flight number repetitions in pairings. First, when the flight schedule is irregular, we show that better quality solutions can be obtained in less computational times if the first two phases are skipped and the monthly problem is solved directly using a rolling horizon approach based on column generation. In fact, this approach has reduced the solution fat by 34%. The solution fat is a quality measure that shows the percentage of time not worked but paid. Second, even if the flight schedule is completely regular, we show that better quality solutions can be derived by skipping the daily problem phase and solving the weekly problem directly. Indeed, the proportion of pairings with such repetitions represents 48.8% causing a mean reduction in the solution fat by 16%. In practice, both the crew pairing and crew assignment problems are independently modeled and sequentially solved. The use of a sequential approach considerably reduces the complexity of the global problem but produces solutions that may not be conform with airline desires. The second objective in this thesis is to propose a model that fully integrates the crew pairing and crew assignment problems and solve it in a single step. Due to the large size of this integrated model, we propose a solution method that combines a column generation and a dynamic constraint aggregation method. Since the latter method requires a good initial partition, this partition is provided by a set of pairings found with the sequentia

    Pairing Generation for Airline Crew Scheduling

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    Airline planning is a complex and difficult process. The biggest airlines in the world plan for and operate fleets of over 700 aircraft using tens of thousands of crew members. As such, small percentages in savings translate to millions of dollars. In this thesis, we study the pairing and duty generation problem in the context of airline crew scheduling, and propose approaches to improve the computational speed and the solution quality. We propose several enumeration algorithms to generate all possible duty periods of a given schedule to improve on the time required to generate duty periods; and present a set of column generation models to improve on the solution quality. When tested on a real test case study, the proposed approaches are found to improve the computational times from 142 seconds down to less than one second, and the cost savings of 13.7%

    FLIGHT RISK MANAGEMENT AND CREW RESERVE OPTIMIZATION

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    There are two key concerns in the development process of aviation. One is safety, and the other is cost. An airline running with high safety and low cost must be the most competitive one in the market. This work investigates two research efforts respectively relevant to these two concerns. When building support of a real time Flight Risk Assessment and Mitigation System (FRAMS), a sequential multi-stage approach is developed. The whole risk management process is considered in order to improve the safety of each flight by integrating AHP and FTA technique to describe the framework of all levels of risks through risk score. Unlike traditional fault tree analysis, severity level, time level and synergy effect are taken into account when calculating the risk score for each flight. A risk tree is designed for risk data with flat shape structure and a time sensitive optimization model is developed to support decision making of how to mitigate risk with as little cost as possible. A case study is solved in reasonable time to approve that the model is practical for the real time system. On the other hand, an intense competitive environment makes cost controlling more and more important for airlines. An integrated approach is developed for improving the efficiency of reserve crew scheduling which can contribute to decrease cost. Unlike the other technique, this approach integrates the demand forecasting, reserve pattern generation and optimization. A reserve forecasting tool is developed based on a large data base. The expected value of each type of dropped trip is the output of this tool based on the predicted dropping rate and the total scheduled trips. The rounding step in current applied methods is avoided to keep as much information as possible. The forecasting stage is extended to the optimization stage through the input of these expected values. A novel optimization model with column generation algorithm is developed to generate patterns to cover these expected level reserve demands with minimization to the total cost. The many-to-many covering mode makes the model avoid the influence of forecasting errors caused by high uncertainty as much as possible

    Pricing in column generation for a robust airline crew pairing problem

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    The crew pairing problem is to find the least costly set of pairings so that each flight given in the flight schedule is covered. In this study, the robust crew pairing problem is considered. That is, the selected pairings cover the regular flights and also provide solutions to cover some extra flights which may be introduced into the flight schedule during the operation at a later point in time. The crew pairing problem is usually solved by column generation in which the pricing subproblem becomes a multi-label shortest path problem. For the robust crew pairing problem the multi-label shortest path problem requires some modifications to solve two column generation approaches proposed by Çoban [10]. These modifications of the pricing problem with associated labels and the domination rules are presented. The complexity of the multi-label shortest path problem grows exponentially as the number of flights (nodes) in the flight schedule increases. This curse of dimensionality is solved by using approximate and exact pruning rules. Also, a buffer column pool is formed as an intermediate step in order to find a negative reduced cost pairing without solving the multi-label shortest path problem at every iteration of the column generation algorithm. In the multi-label shortest path problem, the approximate rules based on the score-calculation are used for early pruning of the paths on the processed nodes. The optimal solution may be missed because of the coarse structure of the approximate rules. When a pairing that improves the objective function cannot be found by applying the approximate rules, we switch to the exact pruning. Another method is using a hybrid approach that applies both approximate and exact rules in the same iteration to find the optimal solution. The performance of our solution approach is demonstrated through a computational study by using actual data from a local airline
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