Fusion of Two Metaheuristic Approaches to Solve the Flight Gate Assignment Problem

AbstractOne of the most important activity in airport operations is the gate scheduling. It is concerned with finding an assignment of flights to terminal and ramp positions (gates), and an assignment of the start and completion times of the processing of a flight at its position. The objectives related to the flight gate assignment problem (FGAP) include the minimization of the number of flights assigned to remote terminals and the minimization of the total walking distance. The main aim of this research is to find a methodology to solve the FGAP. In this paper, we propose a hybrid approach called Biogeography-based Bee Colony Optimization (B-BCO). This approach is obtained fusing two metaheuristics: biogeography-based (BBO) and bee colony optimization (BCO) algorithms. The proposed B-BCO model integrates the BBO migration operator into to bee's search behaviour. Results highlight better performances of the proposed approach in solving FGAP when compared to BCO

Robustness Algorithms for the Airport Gate Assignment Problem

Assigning commercial flights to available airport gates can have a major impact on the efficiency of flight schedules as well as on the level of passenger satisfaction with the service. These assignments also depend on the service requirements of flights and the capacity of stand facilities. Unexpected changes also called perturbations, like those due to air traffic delays, severe weather conditions, or equipment failures, may disrupt the initial assignments and increase the difficulty of maintaining smooth operations, which will detrimentally affect costumer satisfaction. The provision of solutions which reduce the potential detrimental effect of perturbations in the stands already assigned on the day of operation is desirable and some approaches are presented here, and compare between them to help identify their performance and trends

Robustness Algorithms for the Airport Gate Assignment Problem

Assigning commercial flights to available airport gates can have a major impact on the efficiency of flight schedules as well as on the level of passenger satisfaction with the service. These assignments also depend on the service requirements of flights and the capacity of stand facilities. Unexpected changes also called perturbations, like those due to air traffic delays, severe weather conditions, or equipment failures, may disrupt the initial assignments and increase the difficulty of maintaining smooth operations, which will detrimentally affect costumer satisfaction. The provision of solutions which reduce the potential detrimental effect of perturbations in the stands already assigned on the day of operation is desirable and some approaches are presented here, and compare between them to help identify their performance and trends

Robust assignment of airport gates with operational safety constraints

This paper reviews existing approaches to the airport gate assignment problem (AGAP) and presents an optimization model for the problem considering operational safety constraints. The main objective is to minimize the dispersion of gate idle time periods (to get robust optimization) while ensuring appropriate matching between the size of each aircraft and its assigned gate type and avoiding the potential hazard caused by gate apron operational conflict. Genetic algorithm is adopted to solve the problem. An illustrative example is given to show the effectiveness and efficiency of the algorithm. The algorithm performance is further demonstrated using data of a terminal from Beijing Capital International Airport (PEK)

Working Notes from the 1992 AAAI Spring Symposium on Practical Approaches to Scheduling and Planning

The symposium presented issues involved in the development of scheduling systems that can deal with resource and time limitations. To qualify, a system must be implemented and tested to some degree on non-trivial problems (ideally, on real-world problems). However, a system need not be fully deployed to qualify. Systems that schedule actions in terms of metric time constraints typically represent and reason about an external numeric clock or calendar and can be contrasted with those systems that represent time purely symbolically. The following topics are discussed: integrating planning and scheduling; integrating symbolic goals and numerical utilities; managing uncertainty; incremental rescheduling; managing limited computation time; anytime scheduling and planning algorithms, systems; dependency analysis and schedule reuse; management of schedule and plan execution; and incorporation of discrete event techniques

Stochastic airport gate assignment problem

The uncertainties inherent in the airport flight arrival and departure traffic may lead to the unavailability of gates when needed to accommodate scheduled flights. Mechanical failures, severe weather conditions, heavy traffic volume at the airport are some typical causes of the uncertainties in the input data. Incorporating such random disruptions is crucial in constructing effective flight-gate assignment plans. We consider the flight-gate assignment problem in the presence of uncertainty in arrival and departure times of the flights and represent the randomness associated with these uncertain parameters by a finite set of scenarios. Using the scenario-based approach, we develop new stochastic programming models incorporating alternate robustness measures to obtain assignments that would perform well under potential random disruptions. In particular, we focus on the number of confficting flights, the buffer and idle times as robustness measures. Minimizing the expected variance of idle times or the expected semi-deviation of idle times from a buffer time value are some examples of the objectives that we incorporate in our models to appropriately distribute the idle times among gates, and by this way, to decrease the number of potential flight confficts. The proposed stochastic optimization models are formulated as computationally expensive large-scale mixed-integer programming problems, which are hard to solve. In order to find good feasible solutions in reasonably short CPU times, we employ tabu search algorithms. We conduct an extensive computational study to analyze the proposed alternate formulations and show the computational effectiveness of the proposed solution methods

On the construction of stable project baseline schedules.

The vast majority of project scheduling efforts assume complete information about the scheduling problem to be solved and a static deterministic environment within which the pre-computed baseline schedule will be executed. In reality, however, project activities are subject to considerable uncertainty, which generally leads to numerous schedule disruptions. It is of interest to develop pre-schedules that can absorb disruptions in activity durations without affecting the planning of other activities, such that co-ordination of resources and material procurement for each of the activities can be performed as smoothly as possible. The objective of this paper is to develop and evaluate various approaches for constructing a stable pre-schedule, which is unlikely to undergo major changes when it needs to be repaired as a reaction to minor activity duration disruptions.

The comparison of the metaheuristic algorithms performances on airport gate assignment problem

Bu çalışma, 05-07 Eylül 2016 tarihleri arasında İstanbul[Türkiye]’da düzenlenen 19. European-Operational-Research-Societies Working Group on Transportation Meeting (EWGT)’da bildiri olarak sunulmuştur.The airport gate assignment problem (AGAP) is an important research area in air transportation planning and optimization. In this paper we study the airport gate assignment problem where the objectives are to minimize the number of ungated flights and the total walking distances. In order to solve the problem, we proposed a new tabu search (TS) algorithm which uses a probabilistic approach as an aspiration criterion. We compared two metaheuristics, namely, TS, and simulated annealing (SA). A greedy algorithm used as a benchmark. We compared the performances of the algorithms and analyzed at different problem sizes. Experimentations showed that the new proposed metaheuristic algorithm gave promising results.EURO Working Grp TransportatEMAY Int Eng & Consultancy Incİstanbul Teknik ÜniversitesiTürkiye Bilim ve Teknoloji Konsey