Purpose

One of the main advantages of the proposed model is that it is flexible to generate n-day pairings simultaneously. It means that, despite previous researches, one-day to n-day pairings can be generated in a single model. The flexibility in generating parings causes that the proposed model leads to better solutions compared to existing models. Another advantage of the model is minimizing the risk of COVID-19 by limitation of daily flights as well as elapsed time minimization. As airports are among high risk places in COVID-19 pandemic, minimization of infection risk is considered in this model for the first time. Genetic algorithm is used as the solution approach, and its efficiency is compared to GAMS in small and medium-size problems.

Design/methodology/approach

One of the most complex issues in airlines is crew scheduling problem which is divided into two subproblems: crew pairing problem (CPP) and crew rostering problem (CRP). Generating crew pairings is a tremendous and exhausting task as millions of pairings may be generated for an airline. Moreover, crew cost has the largest share in total cost of airlines after fuel cost. As a result, crew scheduling with the aim of cost minimization is one of the most important issues in airlines. In this paper, a new bi-objective mixed integer programming model is proposed to generate pairings in such a way that deadhead cost, crew cost and the risk of COVID-19 are minimized.

Findings

The proposed model is applied for domestic flights of Iran Air airline. The results of the study indicate that genetic algorithm solutions have only 0.414 and 0.380 gap on average to optimum values of the first and the second objective functions, respectively. Due to the flexibility of the proposed model, it improves solutions resulted from existing models with fixed-duty pairings. Crew cost is decreased by 12.82, 24.72, 4.05 and 14.86% compared to one-duty to four-duty models. In detail, crew salary is improved by 12.85, 24.64, 4.07 and 14.91% and deadhead cost is decreased by 11.87, 26.98, 3.27, and 13.35% compared to one-duty to four-duty models, respectively.

Originality/value

The authors confirm that it is an original paper, has not been published elsewhere and is not currently under consideration of any other journal.

中文翻译：

---

考虑 COVID-19 风险的用于机组配对优化以生成 n 天配对的灵活数学模型：真实案例研究

目的

所提出模型的主要优点之一是可以灵活地同时生成n天配对。这意味着，尽管有先前的研究，但可以在单个模型中生成1 天到n天的配对。生成配对的灵活性导致与现有模型相比，所提出的模型导致更好的解决方案。该模型的另一个优点是通过限制每日航班和最小化经过时间来最小化 COVID-19 的风险。由于机场是 COVID-19 大流行中的高风险场所，因此该模型首次考虑将感染风险降至最低。采用遗传算法作为求解方法，在中小型问题中其效率与GAMS相比。

设计/方法/方法

航空公司最复杂的问题之一是机组调度问题，它分为两个子问题：机组配对问题（CPP）和机组排班问题（CRP）。生成机组配对是一项艰巨而令人筋疲力尽的任务，因为可能会为航空公司生成数百万个配对。此外，机组成本在航空公司总成本中的份额仅次于燃油成本。因此，以成本最小化为目标的机组调度是航空公司最重要的问题之一。在本文中，提出了一种新的双目标混合整数规划模型，以最小化死区成本、船员成本和 COVID-19 风险的方式生成配对。

发现

该模型适用于伊朗航空公司的国内航班。研究结果表明，遗传算法解与第一和第二目标函数的最佳值分别平均只有 0.414 和 0.380 的差距。由于所提出模型的灵活性，它改进了由具有固定任务配对的现有模型产生的解决方案。乘员成本与一任务到四任务模型相比降低了 12.82、24.72、4.05 和 14.86%。具体而言，船员工资分别比一班到四班机型提高了12.85、24.64、4.07和14.91%，空头成本分别降低了11.87、26.98、3.27和13.35%。

原创性/价值

作者确认这是一篇原创论文，尚未在其他地方发表，目前没有任何其他期刊考虑。