The growth in the aviation industry means that with existing constraints, operational efficiency has to be improved in order to be sustainable. The bottlenecks at airports are usually the runways and consequently, the routing and scheduling decisions from the ATC pertaining to the route and order of the incoming and outgoing flights are of paramount importance. The objective of this research was to evaluate an advanced optimisation algorithm at Schiphol using publicly sourced data on different aspects, which were dual in nature, one was performance as compared to the incumbent practises and the other was fairness which dealt with the fair distribution of the decisions from the ATC for different airlines depending on cost incurred by each airline. The advanced algorithm was devised by drawing an analogy to job shop scheduling problem and solving the same using graph theory and associated (Meta) heuristics. The financial and fairness analysis was carried out through analogising game theory. The experimental design was set up through running the data through an optimisation model followed by financial analysis. The data consisted of schematics of Schiphol, so as to determine the time to traverse resources like approach air segment, glide path and runways, details of the aircraft and time of entry into the terminal control area of Schiphol along with expected time at gates. In total 49 data sets were evaluated through the model in different configurations. The configurations were as follows, 1. First Come First Serve (Incumbent) 2. Solver Scheduling 3. Solver Routing and Scheduling – the proposed algorithm 4. Equity 1 (Priority KLM) – proposed algorithm being partisan to KLM 5. Equity 2 (Priority Non- KLM) – proposed algorithm being partisan to non-KLM airlines The output was in the form of delay for individual aircraft which were then consolidated to delays for airlines. The delay(s) were the result of the decision which was based on the configuration used; this aspect was used to compare the performance of the various algorithms. Furthermore, the delay(s) for different airlines was used to analyse whether decisions which resulted in the delays are commensurate with the payments made by the airlines. The findings were quite consistent with the expected outcome of the experimental set-up. The proposed algorithm, in its normal and original state, performed the best amongst all other configurations. In all the data sets, there was improvement in the performance, by using the proposed algorithm, at a global level i.e. for the whole system as a whole. The factor of improvement from the incumbent practise depended on the initial status of the system. Having established the superior performance of the algorithm, the distribution of decision amongst airlines was analysed to establish fairness. The delays for the airlines were monetised using the value of time specific to aviation operation and the situation was analysed using a cooperative game theory approach, where airlines could agree to implement the proposed algorithm by forming a grand coalition or not agreeing thereby reverting back to the incumbent system for all. Only taking the operational cost incurred by the airlines and performance analysis conducted previously, the Shapley Value gave the fair distribution of the costs based on the marginal improvement each airline brought to the system. For all data sets, the Shapley Value was consistent and comparable to the actual costs albeit with minor inconsistencies; in some cases a few airlines paid more than what they should pay and in some cases they paid less than what they should pay. To tackle the inconsistencies a financial redistribution framework was proposed. The airlines paying less than what they should pay, contribute the default amount to a common fund and then, the money from the fund is redistributed amongst the airlines paying too much according to their Shapley Value ratio to minimise their loss. This system created a system wherein no outside interference is required and by transferring money internally, a sense of fairness could be introduced into the system. Also, this system took care of the local optimal after a global optimal had been established and in fact improved upon the global optimal. In all the data sets, the number of times an airline paid too much or too little was evenly distributed. Also, the grand coalition, wherein all the airlines agree to implement the new algorithm, was inherently stable due the game being inherently convex and the Shapley Value being present in the core. However, owing to the scale of operation of KLM, KLM could impact the performance of the whole system and actually benefitted the most from the proposed algorithm. To summarise, the proposed algorithm can be implemented to give a superior performance in terms of minimising the delay experienced by the whole airport. However, a further detailed study of the financial agreements between the airlines and Schiphol is required so as to align the actual financial transactions with that of the ideal or the fair financial transactions. Also, for any financial framework or agreement between Schiphol and various other airlines, the interests of KLM should always be taken into account since KLM is a dominant player whose individual (local) performance affects the global performance. Hence, it can be concluded that the proposed algorithm is definitely an improvement over the existing system and also a sense of fairness can be introduced in the decision support system to ensure participation of all the airlines.