To better govern inventory control of spare parts in tandem with scheduling of maintenance operations in a railway setting, a Railway Spare Inventory & Maintenance Scheduling (R-SIMS) model is developed. To suit the railway industry setting the model includes an inventory control strategy which envokes continuous monitoring and a reorder level as well as an order up-to level. Moreover a condition based maintenance (CBM) strategy is used with periodical inspections. These strategies are combined in a simulation model which assumes stochastic step-wise deterioration of parts as well as stochastic lifetime lengths to predict the expected cost per part per period. The three maintenance scheduling and spare inventory decisions to be minimised in terms of their resulting costs are 1) when to replace parts based on their condition, 2) when to buy new spare parts and 3) how many spare parts to procure at each order. These decisions are represented by values of the three decision variables: the CBM threshold L_p, the reorder level s and the order up-to level S. The minimisation of these decision variables is performed through surrogate modelling, a branch of machine learning optimisation techniques which deals with complex black-box functions. It does so by running experiments and estimating a surrogate function which in turn is optimised mathematically. Multiple surrogate modelling methods are compared in experiments to test their performance, speed and stability. These experiments showed that the Tree-structured Parzen Estimator algorithm as used in the HyperOpt Python library was one of the best performing methods (only rivaled by the MVRSM algorithm), moreover it was shown to be the fastest as well as the most stable of the tested algorithms for this particular problem application. This research contributes to the existing theory by creating a modelling framework for the joint optimisation of spare inventory and maintenance scheduling decisions which is specifically tailored to the railway context and includes possibilities for minimal maintenance. In practice this model can be used by maintenance providers to increase the financial success of their maintenance operations and by infrastructure managers to increase insight into their maintenance providers' operations.

Collaborative Innovation can serve to elevate the production potential of firms and organisations as well as preparing them for the future. This process of two organisations collaborating together is very complex and becomes even more difficult when organisations are large and very different in their organisational culture or function. This research describes the design of a tool to aid Collaborative Innovative Capacity in public - private organisational partnerships. The research is based on a case study within the Dutch national rail infrastructure management organisation and focuses on the relationship between this organisation and its maintenance contractors. Through literature research and semi-structured interviews the studied situation is found to consist of two parties trying to innovate together using an imperfect collaborative system. Their separate, struggling relationships with this system are not shared or understood by the other party. This gives rise to a wall of misunderstanding. The designed tool is based on the assumption that through better understanding of the barriers which are present in the other party’s struggling relationship with the collaborative system, the wall of misunderstanding can be lowered which in turn increases Collaborative Innovative Capacity in the public - private organisational partnership. This tool takes the form of a serious game which presents players with scenarios which (potentially) hinder them in their game objective. These scenarios are representations of real-world barriers to innovation. Through confronting players with scenarios which are related to another organisational role than the one they occupy in their professional life, they should gain new perspectives about the struggling relationship of the other party with the collaborative system. Thereby lowering the wall of misunderstanding and increasing Collaborative Innovative Capacity. The results of the design and preliminary testing show that the developed serious game does have an effect in the form of gained attitudes towards the importance of sharing different experienced barriers towards innovations with one another. However measurements show no actual newly gained insights into practical barriers which were not already known to the players. While further research, more extensive testing and measurements, and further specification and expansion of scenarios can be undertaken to increase the value of this tool, after reconsideration a revised design objective is formulated which focuses on the interaction between the participants and relaxes its rigid view on the wall between them. This could yield a tool which is more in tune with the essence of the problem which would also cause it to be much simpler in nature.