A Proactive Robust Scheduling Method for Aircraft Carrier Flight Deck Operations with Stochastic Durations

Abstract

The operations on the aircraft carrier flight deck are carried out in a time-critical and resource-constrained environment with uncertainty, and it is of great significance to optimize the makespan and obtain a robust schedule and resource allocation plan for a greater sortie generation capacity and better operational management of an aircraft carrier. In this paper, a proactive robust optimization method for flight deck scheduling with stochastic operation durations is proposed. Firstly, an operation on node-flow (OONF) network is adopted to model the precedence relationships of multi-aircraft operations, and resource constraints categorized into personnel, support equipment, workstation space, and supply resource are taken into consideration. On this basis, a mathematical model of the robust scheduling problem for flight deck operation (RSPFDO) is established, and the goal is to maximize the probability of completing within the limitative makespan (PCLM) and minimize the weighted sum of expected makespan and variance of makespan (IRM). Then, in terms of proactive planning, both serial and parallel schedule generation schemes for baseline schedule and robust personnel allocation scheme and equipment allocation adjustment scheme for resource allocation are designed. In terms of executing schedules, an RSPFDO-oriented preconstraint scheduling policy (CPC) is proposed. To optimize the baseline schedule and resource allocation, a hybrid teaching-learning-based optimization (HTLBO) algorithm is designed which integrates differential evolution operators, peak crossover operator, and learning-automata-based adaptive variable neighborhood search strategy. Simulation results shows that the HTLBO algorithm outperforms both some other state-of-the-art algorithms for deterministic cases and some existing algorithms for stochastic project scheduling, and the robustness of the flight deck operations can be improved with the proposed resource allocation schemes and CPC policy.

A dual population multi-operator genetic algorithm for flight deck operations scheduling problem

“…The transfer time sequence is similar to that in constraint (7). Constraint (9) indicates that if more than one operation of aircraft i require a sharing equipment of the type at any time period, these operations are allocated to the same one. Constraints ( 10) and ( 11) together make sure that the number of personnel or equipment required for performing operation O ij is equal to the total number of personnel or equipment allocated to it respectively.…”

Knowledge based approach to ground refuelling optimization of commercial airplanes

“…The optimization of fuel tank structures for an aircraft wing tank, another possible approach that has been evaluated for instance in Li, Liu, and Zheng (2018), is out of the scope of this work. The general problem of aircraft ground handling at airports has also been addressed (Su et al, 2018; Tabares & Mora‐Camino, 2017), as well as the aircrafts refuelling from the point of view of employees' optimization (Carotenuto, Giordani, Salvatore, & Biasini, 2019). But none of them consider on‐ground refuelling for the mass estimation before the take‐off when it is a crucial aspect of aircraft performance (Chati & Balakrishnan, 2018; Sun, Ellerbroek, & Hoekstra, 2018).…”

“…Aircraft ground handling is a critical and complex process that includes all the operations that must be performed on an aircraft when parked on a stand (Su, Han, Wu, Zhang, & Liu, 2018), including maintenance (Gharoun, Keramati, Nasiri, & Azadeh, 2019). Much importance has been given to the correct loading of the aircraft and the takeoff weight.…”

A baseline-reactive scheduling method for carrier-based aircraft maintenance tasks

“…The abovementioned studies have inspired researchers to develop a mathematical model for the MTSPCA. Dynamic flight deck operation scheduling has been investigated in rolling horizon scheduling [20], robust scheduling [21], proactive scheduling [22], and proactive robust scheduling [23].…”