Optimization of Airline Support Facility Space at Non-Major Airports of India Using Goal Programming

According to the Committee of Estimates of the Civil Aviation Ministry in India, as revealed in the Lok Sabha, it was noted that in the fiscal year 2021-2022, only 10 out of the 109 operational airports, which are part of the 136 owned and managed by the Airports Authority of India (AAI), generated revenue [1]. The predominant revenue source for airports internationally, including those in India, is attributed to the air traffic managed, encompassing both aircraft movements and passenger flows. Other sources, such as the allocation of land, space, and additional services, account for a smaller revenue fraction. Within the AAI, it has been reported that aeronautical revenue, including Airport Services and Aeronautical Navigation Services, constitutes 84%, while non-aeronautical streams contribute 16% to the aggregate revenue. This distribution is elaborated in Section 4.3 of the present study. Given the limited air traffic at non-major airports, the urgency to identify and exploit alternative revenue streams is underscored. The optimization of land and space utilization emerges as a strategic approach to bolster the financial profiles of these airports.

The consolidated findings from the analysis of land lease combinations for four types of facilities (fuel stations, ground service equipment - GSE, hangars, and porta-cabins) using the Goal Programming model [15, 20] and Excel Solver are presented in Tables 5 and 6 as discussed in the preceding section.

1. For each of the twenty-three airports, the land allotment areas ranging from 1830 sq. meters (0.452 acres) to 19136 sq. meters (4.73 acres) would accommodate all four facilities (refer to Table 5) [12].

2. The lease revenue for each airport location, based on notified annual rates per sq. meter, varies from 0.83 million INR to 28.99 million INR (refer to Table 5, with an exchange rate of 1 US $ = 70 INR in 2018).

3. The expected total lease revenue for the land lease areas of all twenty-three airports sums up to 267.88 million INR, covering 216,285 sq. meters (53.45 acres) (refer to Table 5, with an exchange rate of 1 US $ = 70 INR in 2018).

4. The cumulative increase in total land area and revenue, considering the proposed land lease mix for the four facility types, is 95% for land area and 77% for land lease revenue, compared to the existing land allotment revenue at notified rates (refer to Table 6, with an exchange rate of 1 US $ = 70 INR in 2018) [10].

5. Comparing the ratios of land lease revenue to total revenue between the existing allotment and the proposed model, there is approximately a 95% increase in the latter (refer to Table 6, with an exchange rate of 1 US $ = 70 INR in 2018).

6. The analysis incorporates average values for area requirements and average floor rent per sq. meter per month from six airports. Assumptions include a 20% floor area limitation for commercial outlets and corresponding income, forming the basis for the weighted Goal Programming Model.

7. In Table 4 under Para 5, the Solver Model's output of "0" in the Objective Function Cell signifies the successful minimization of deviations based on the selected weightage for each of the six deviations. The resulting total area is 3,840 sq. meters within the earmarked limit of 4,000 sq. meters, generating total rental revenue of 58.56 lakh Indian Rupees or 5.85 million. Further scenarios can be explored by modifying input target numbers and prioritizing types of allotments, even with minor deviations, without complete depreciation (refer to Table 4, with an exchange rate of 1 US $ = 70 INR in 2018).

8. By adopting the presented methodology and results, the optimal mix of land and space allotments for enhancing revenue from both airport land and space resources can be determined within the given limitations [2, 7, 10].

In a chapter titled "Statistical Methods as Optimization Problems," George Mason University's publication highlights various approaches to accommodate multiple objectives and constraints in optimization problems. The simplest method involves forming a weighted sum, where constraints are incorporated as a weighted component of the objective function, allowing control over the extent to which the constraints are met. In optimization, there often requires interaction between decision-makers and the optimization procedure.

While formulating an optimization problem, careful consideration is essential to ensure it accurately captures the objective of the real problem. The impact of assumptions about the real problem can be magnified in optimization, necessitating caution in both formulating and analyzing the underlying problem. Even when the problem is correctly formulated, difficulties may arise in applying the optimization problem in a statistical method. In contrast to regular regression models, which can be validated using statistical parameters/tests such as sample size, R square, p-values, and assessments of multicollinearity, the validation of multi-objective models like Goal Programming involves addressing practical resource allocation problems under constraints while minimizing deviations from the objective function. The author acknowledges the absence of direct references to specific statistical tests for validating Goal Programming models. Furthermore, emphasizing that interpreting and validating a model solely from a statistical viewpoint may not always be feasible or justified. The above process, including the basis for data (pertaining to twenty-three airports), assumptions, etc., was thoroughly explained in the preceding subsections covering Methods and Data Analysis. The outcomes and validation of the model, presented as an optimal mix of land allotments for each of the twenty-three airports, were summarized in Tables 3 and 6 and discussed above.

With the projected exponential growth in aviation, particularly due to the substantial increase in aircraft fleets and the urgent requirement for establishing flight training and airline supporting facilities, allocating spaces on spare land at every airport becomes imperative. While aviation services are typically demand-driven, operating primarily between major cities, busy airports often have limited space for essential facilities like aircraft servicing hangars, aviation-related training centers, offices, and fuel stations. Given the varying sizes of these facilities across different non-major airports in India, the Model incorporates target values for the potential number of land patches, limits on total lease area, and expected lease rates. Traditional Linear Programming, which aims to maximize a single goal within a single resource constraint, may not be suitable for airports facing limitations and uncertain air traffic conditions. Instead, adopting a flexible approach that minimizes negative and positive deviations from selected goals by adjusting inputs (such as the number of outlets and resulting area and income within the limited space) proves more beneficial for airport operators. This study demonstrates that utilizing the Goal Programming method and comparing land and revenue area allotments with existing practices could result in a 77% increase in area utilization and a 95% increase in revenue outcomes.

This paper focuses on sorting, compiling, and modeling data from twenty-three non-major airports out of a total of eighty-three classified by the Ministry of Civil Aviation. Including the remaining airports would broaden the study's scope, allowing for a comprehensive assessment of potential revenue enhancement through land leasing and space renting. The model, utilizing the Weighted Goal Programming approach, calculates the number of units for leasing land to four types of facilities, encompassing both land and building space. For assessing additional revenue from spare land and building space, other frameworks or models, such as dynamic programming, non-linear frameworks, or simulations, could be explored. Future work may involve comparing the results with other multi-objective optimization methods, conducting sensitivity/risk analyses, and considering dynamic traffic and financial factors. Exploring alternate methods will contribute to a more comprehensive understanding of the potential revenue generation at non-major airport.