Computational efficiency is essential for large-scale mathematical optimisation problems, such as the generation expansion planning problem, to be practically applicable. In linear programming solvers, crossover is frequently a bottleneck when solving optimisation problems. This study aims at determining why crossover is a bottleneck in solving the generation expansion planning problem and deriving a method which obtains an optimal solution faster. Symmetry was found to be disrupting crossover, causing its runtime to significantly increase. However, the preceding barrier method benefit from the symmetry handling of the solver. An alternative approach using problem-specific knowledge to reduce symmetry before serving the model to a solver was proposed. The key to the efficiency of this method is using the information of how symmetries are reduced when retrieving the solution to the original problem. The approach was applied to two types of symmetry we identified in the generation expansion planning problem, leading to a significant speed-up of the crossover phase and total runtime for the algorithm resolving a formulation symmetry, but a longer runtime for the method resolving a more practically available non-formulation symmetry. However, the latter was faster in a couple of cases and has many directions for future improvements. The results of this research, thus, demonstrate that domain knowledge of linear programming problems can be applied to reduce symmetry that state-of-the-art solvers cannot detect and to more efficiently obtain an optimal solution than traditional crossover.

With the increasing number of electric vehicles on the road, the routing problem has become more complex. As charging electric vehicles takes longer than fueling non-electric vehicles, congestion can occur at charging stations. This might lead to the shortest route not being the fastest route, due to long waiting times at the stations. By communicating the intentions of each vehicle, the vehicles can spread out over multiple stations. This thesis investigates the eect of such a routing system on the profitability of charging stations. In particular, the influence of a charging station's location on its profitability has been researched. In order to do this, a pricing model has been developed to extend the routing model. It has become clear that due to the intention-aware routing, the number of visits at stations is harder to predict, and there is not always a clear pattern to be found between the location and the number of visits. This research does, however, propose an optimisation model, which can decide where to build a new charging station, such that the number of visits will be the highest.

With the increasing number of electric vehicles onthe road, the routing problem has become morecomplex. As charging electric vehicles takes longerthan fueling non-electric vehicles, congestion canoccur at charging stations. This might lead to theshortest route not being the fastest route, due tolong waiting times at the stations. By commu-nicating the intentions of each vehicle, they canspread out over multiple stations. This paper in-vestigates the effect of such a routing system on theprofitability of charging stations in comparison toa more simple shortest-path algorithm. In particu-lar, the influence of a charging station’s location onits profitability has been researched for both rout-ing algorithms. In order to do this, a pricing modelhas been developed to extend the routing mod-els used for both the shortest-path algorithm andthe intention-based routing algorithm. Through-out several simulations, it became clear that for theshortest-path algorithm, more centralised stationsobtain a higher profit, whereas for the intention-based routing algorithm there were no significantdifferences in profitability between the more cen-tral stations, and the ones on longer routes.