This Thesis proposes an optimisation model to minimize operational cost of a fleet of electric thin-haul aircraft under a minimum RPK (Revenue-Passenger Kilometer) constraint. A solution that minimises cost per RPK can be found by varying the minimum RPK level. The solutions desc
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This Thesis proposes an optimisation model to minimize operational cost of a fleet of electric thin-haul aircraft under a minimum RPK (Revenue-Passenger Kilometer) constraint. A solution that minimises cost per RPK can be found by varying the minimum RPK level. The solutions describes how many aircraft are needed, as well as a schedule for each aircraft for a single day. We consider a set of airports where charging infrastructure is assumed to be present. All aircraft must start and end the day from the assigned hub airport. Next, we consider a discretised time space between a start time and end time with constant time steps. The problem is represented on a time-space network where each node uniquely defines a location (airport) and point in time, and arcs connect the nodes. An arc connecting two consecutive nodes at the same airport are ground arcs and represent waiting on the ground. An arc connecting two nodes at different airports are flight arcs. The cost, duration and energy consumption on flight arcs is determined in advance. A schedule is represented as a sequence of arcs. The number of passengers on a flight is limited by the demand. We developed a method to find a schedule that minimises costs while meeting a minimum RPK constraint. This method was then illustrated on a network with 5, 10, 15, 20 and 30 airports. The results show that our method is much faster than a traditional linear programming model. The obtained solution is a local minimum and is very close to the global optimum. We found that cost per RPK shows a sawtooth pattern, gradually decreasing as aircraft utilisation increases but spiking up when an additional aircraft is added to the fleet. Furthermore, the sawtooth pattern shows an increasing trend. The schedule shows a strong preference for connections with a longer distance and with high demand. The algorithm first fills up these connections, often using back-and-forth flights, until they are largely saturated. When there is little demand left on these connections, the algorithm starts adding flights on connections with a shorter distance or less demand.