Question

Q/ explain various Air Cargo scheduling Problems (ACSP) with examples taking any imaginary route/data of your choice.

Answer 1

A major problem faced by every airline company is to construct a daily schedule for a heterogeneous aircraft fleet. The implementation of aircraft routing and scheduling for cargo transportation, known as one of the scheduling problems in transportation, in an airline company is presented. First, problems faced by the company are defined, and then implementation steps and expected improvements that will result from carrying out the solution of the mathematical model of the problem are given in detail. The purpose of this paper is to describe, analyze, and evaluate a case study of how aircraft scheduling was managed in an airline company.

Much research by the air industry as well as academics has already been devoted to fleet routing and flight scheduling problems. Researches on flight scheduling have mainly focused on passenger transportation, which is fundamentally different from cargo transportation. In particular, the selection of airports in a passenger service network usually involves long-term planning, but in cargo transport, this is not the case To respond to significant rapid fluctuations in demand, carriers must perform their airport selection, fleet routing and timetable setting to formulate short-term plans, while still considering demand and profit. Moreover, passengers are more time-sensitive than cargos. Too many transfers in a passenger service may result in a significant loss of customers, but cargos are not lost, provided they can be delivered on time Research on freight transportation and fleet routing has been performed by few researchers. The earlier studies commonly focused on a pure hub-and-spoke network for air express carriers, hierarchical network design problems, hub location, and routing problems. Also, meta-heuristics (genetic algorithm (GA), tabu search (TS), threshold accepting (TA), and simulated annealing (SA) methods) have been employed to solve network flow problems, optimal communication spanning tree problem, probabilistic minimum spanning tree problem, bipartite transportation network problems; concave cost transshipment problems. When recent studies on freight transportation and fleet routing are inspected, it is observed that the following studies are remarkable.

Yan, Lai, and Chen (2005) developed a short-term flight scheduling model for air express carriers to determine suitable routes and flight schedules to minimize operating costs, subject to related operating constraints. The model is formulated as an integer multiple commodity network flow problem solved using mathematical programming. Belanger et al. (2006) proposed a model for the periodic fleet assignment problem with time windows in which departure times are also determined. They proposed a non-linear integer multi-commodity network flow formulation and developed new branch-and-bound strategies that are embedded in their branch-and-price solution strategy. Sherali, Bish, and Zhu (2006) presented a tutorial on the basic and enhanced models and approaches that have been developed for the fleet assignment problem (FAP).

Yan, Chen, and Chen (2006) studied on air cargo fleet routing and timetable setting with multiple on-time demands. In their research, they combined airport selection, fleet routing, and timetable setting to develop an integrated scheduling model. The model is formulated as a mixed-integer program that is characterized as NP-hard. Yan, Tang, and Lee (2007) developed a short-term flight scheduling model with variable market shares to help an airline to solve for better fleet routes and flight schedules in today’s competitive markets. The model was formulated as a non-linear mixed-integer program, characterized as an NP-hard problem, which is more difficult to solve than the traditional fixed market share flight scheduling problems, often formulated as integer/mixed-integer linear programs. They developed a heuristic method to efficiently solve the model. Tang, Yan, and Chen (2008) develop an integrated scheduling model that combines passenger, cargo, and combi flight schedules.

They employ network flow techniques to construct the model which is formulated as an integer multiple commodity network flow problem that is characterized as NP-hard. They developed a family of heuristics, based on Lagrangian relaxation, a sub-gradient method, heuristics for the upper bound solution, and a flow decomposition algorithm, to solve the model. Yan and Chen (2007, 2008) employed network flow techniques to construct coordinated scheduling models for passenger and cargo transportation, respectively. These models are formulated as mixed-integer multiple commodity network flow problems with side constraints (NFPWS) that are characterized as NP-hard. Problem sizes are expected to be huge making the model more difficult to solve than traditional passenger/cargo flight scheduling problems. Therefore, Chen, Yan, and Chen (2010) developed a family of Lagrangian based algorithm to solve the coordinated fleet routing and flight scheduling problems.

The fleet assignment problem (FAP) deals with assigning aircraft types, each having a different capacity, to the scheduled flights, based on equipment capabilities and availabilities, operational costs, and potential revenues. An airline’s fleeting decision highly impacts its revenues, and thus, constitutes an essential component of its overall scheduling process. However, due to the large number of flights scheduled each day, and the dependency of the FAP on other airline processes, solving the FAP has always been a challenging task for the airlines.

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