基于新型冠状病毒肺炎条件下飞机航路和机组人员排班问题的集成模型,为机组人员制定公平的飞行计划

IF 0.5 Q4 ENGINEERING, AEROSPACE International Journal of Sustainable Aviation Pub Date : 2022-01-01 DOI:10.1504/ijsa.2022.10046150
A. Shoja, A. R. Komijan, M. Mirjafari
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引用次数: 1

摘要

本文重点研究了同时考虑COVID-19感染风险的飞机路线和机组人员名单问题。由于机场是新冠肺炎疫情的高危地区之一,机组人员更愿意减少在机场的停留时间,并在每个执勤日结束后返回基地。在本研究中,开发了一个集成模型来分配机组人员和飞机的航班,以实现机组人员的公平调度。目标函数是最小化机组人员坐位时间之间的差异。此外,在该模型中,考虑了维护需求的一个包括飞行小时数、天数和起飞次数的框架。采用粒子群算法(PSO)求解。为了验证求解方法,使用GAMS和粒子群算法求解了20个测试问题。结果表明,在最优解中,粒子群算法显著提高了CPU时间(平均提高98.279%),与GAMS算法的差距为1.902%。
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An integrated model of aircraft routing and crew rostering problems to develop fair schedule for the crew under COVID-19 condition
This paper focuses on aircraft routing and crew rostering problems simultaneously considering the risk of COVID-19 infection. As airports are among high-risk places in COVID-19 pandemic, the crew prefer to spend less sit time in airports and come back to their home base at the end of each duty day. In this research, an integrated model is developed to assign crew and aircraft to flights in order to achieve a fair schedule for the crew. The objective function is minimisation of the difference between crew sit times. Moreover in this model, a framework including flight hours, number of days and number of take-offs is considered for maintenance requirements. Particle swarm optimisation (PSO) is used as the solution approach. To validate the solution approach, 20 test problems were solved using GAMS and PSO. The results show that PSO improved CPU time significantly (98.279% in average) in turn of 1.902% gap with GAMS in optimum solution.
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