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Crew planning
Published in Peter J. Bruce, Yi Gao, John M. C. King, Airline Operations, 2018
To deal with these issues as they occur, airlines have personnel to manage the tactical, day-to-day events, often called Crew Control or Crew Scheduling, whose primary role is to ensure that flights are fully crewed, and to maintain the operational integrity of the flying program. Their duties include, but are by no means limited to, monitoring crew legality, handling sick calls, calling up reserve crew, providing advice and guidance to the Operations Control team during periods of schedule disruption, plus handling a myriad of other unforeseen upsets. In addition to this, Crew Control will often support crew members by assisting them with trip trades and duty swaps. Some airlines also involve them in the crew logistics, which requires the booking of dead-head flights (where crews sit in passenger seats on the flight), hotels, and arrangement of visas.
Airline scheduling and disruption management
Published in Lucy Budd, Stephen Ison, Air Transport Management, 2020
Cheng-Lung Wu, Stephen J Maher
The task of crew scheduling is then to assign individual crew members to flying duties in accordance with their qualifications and working hour limitations. The aim of crew scheduling is to maximise resource efficiency and utilisation while minimising operational expenses (most notably crew expenses since labour is often the second largest cost to an airline, after fuel). It must also satisfy legal requirements of crew competence and minimum crew numbers for each aircraft type.
Models for Cross-Border Land Transportation of Ocean Containers
Published in Petros A. Ioannou, Intelligent Freight Transportation, 2008
The third level is when we can make decisions in multiple stages. The demands in stage 1 are known and the average (or expected) amount of demands in the later stages is also available. Figure 5.2 shows a three-stage network. Using this framework, we can generate tours to cover the demands (the loaded container movement). A common approach is to use tour-generation techniques that have been applied widely in the crew-scheduling literature.
Operational aircraft maintenance routing problem incorporating cruise speed control
Published in Engineering Optimization, 2022
Qing Zhang, Felix T. S. Chan, S. H. Chung, Xiaowen Fu
Given the initial strategic decisions, airline planners need to solve a series of problems before the actual operation. These are flight scheduling, fleet assignment, aircraft maintenance routing and crew scheduling (Belobaba 2009). Among these, the operational aircraft maintenance routing problem (OAMRP) plays a crucial role in achieving an airline’s profitability because it directly affects aircraft utilization. In the OAMRP, inefficient flight connections and inappropriate maintenance arrangements will incur unnecessary ground time, thus having tremendously adverse impacts on aircraft utilization. However, to maximize aircraft utilization, the aircraft maintenance route is constructed with little buffer time for absorbing even minor delays, resulting in rapid delay propagation. In practice, airlines suffer from various uncertainties, such as aircraft breakdowns, crew sickness and bad weather (Ahmed, Mansour, and Haouari 2017). Accordingly, severe disruptions can easily be incurred, followed by significant delay costs and decreased aircraft utilization. Therefore, in addition to increasing aircraft utilization, it is critical to improve schedule stability and flexibility, so that schedules are less susceptible to disruptions or can be easily repaired once disrupted. As such, this study intends to construct aircraft maintenance routes with higher aircraft utilization while maintaining schedule stability and flexibility simultaneously. The critical feature of this study is the consideration of flexible cruise times during the construction of aircraft maintenance routes.
Metaheuristics for the stochastic post-disaster debris clearance problem
Published in IISE Transactions, 2022
Elifcan Yaşa, Dilek Tüzün Aksu, Linet Özdamar
Demand satisfaction objectives: Yan and Shih (2012) propose a time–space network flow formulation for the multi-crew road repair and relief distribution problem and develop an ant colony-based hybrid algorithm. Çelik et al. (2015) propose a dynamic stochastic DCP with the goal of maximizing demand satisfaction. A Markov decision process is used and a heuristic approach is proposed. Moreno et al. (2019) propose a mixed-integer linear programming model for the crew scheduling and routing problem using a partial accessibility-related objective that minimizes the time intervals during which demand nodes stay inaccessible from the depot. A Branch-and-Benders-Cut algorithm that decomposes crew scheduling and routing problems is proposed. Ulusan and Ergun (2018) aim to satisfy demand with an exponential earliness reward, where a new centrality measure based on the shortest paths between supply–demand node pairs is defined to prioritize demand urgency. A constructive heuristic that prioritizes blocked links based on several centrality measures is used to solve the problem. A multi-stage stochastic mixed-integer model for relief distribution and debris clearance is proposed by Rezaei et al. (2018) where the goal is to minimize weighted unmet demand. Morshedlou et al. (2018) propose a model that maximizes the ratio of pre-disaster to post-disaster flows at demand nodes. The authors employ some pre-processing and feasibility algorithms to solve timing conflicts of common works between groups.
Customized bus service design for uncertain commuting travel demand
Published in Transportmetrica A: Transport Science, 2021
Xueping Dou, Qiang Meng, Kai Liu
Due to the NP-hardness, we design a tailored branch-and-price method to solve the above model [CBSD]. Branch-and-price embeds a column generation technique into a branch-and-bound framework for solving the linear programming (LP) relaxation at each node of the tree, which has been effectively applied to a number of vehicle routing and crew scheduling problems (Barnhart et al. 1998). However, in the model [CBSD], Constraints (5) are associated with services; that is, the number of Constraints (5) grows exponentially with the number of requests. In other words, it is infeasible to directly apply the column generation technique to the LP relaxation of the model [CBSD]. As a result, we further relax the LP relaxation of the model [CBSD] by combining the capacity constraints associated with services that have the same bus type and traverse the same arc , so as to obtain the following master problem (MP) in the context of a branch-and-price method.