Artificial intelligence (AI) is one of the fields in science and engineering and encompasses a wide variety of subfields, ranging from general areas (learning and perception) to specific topics, such as mathematical theorems. AI and, specifically, multi-objective genetic algorithms (MOGAs) for risk-based life cycle cost (LCC) analysis should be performed to estimate the optimal replacement time of tunnel fan systems, with a view towards reducing the ownership cost and the risk cost and increasing company profitability from an economic point of view. MOGA can create systems that are capable of solving problems that AI and LCC analyses cannot accomplish alone.

The purpose of this thesis is to develop a two-level MOGA method for optimizing the replacement time of reparable system. MOGA should be useful for machinery in general and specifically for reparable system. This objective will be achieved by developing a system that includes a smart combination of techniques by integrating MOGA to yield the optimized replacement time. Another measure to achieve this purpose is implementing MOGA in clustering and imputing missing data to obtain cost data, which could help to provide proper data to forecast cost data for optimization and to identify the optimal replacement time.

In the first stage, a two-level MOGA is proposed to optimize clustering to reduce and impute missing cost data. Level one uses a MOGA based on fuzzy c-means to cluster cost data objects based on three main indices. The first is cluster centre outliers; the second is the compactness and separation ( ) of the data points and cluster centres; the third is the intensity of data points belonging to the derived clusters. Level two uses MOGA to impute the missing cost data by using a valid data period from that are reduced data in size. In the second stage, a two-level MOGA is proposed to optimize time series forecasting. Level one implements MOGA based on either an autoregressive integrated moving average (ARIMA) model or a dynamic regression (DR) model. Level two utilizes a MOGA based on different forecasting error rates to identify proper forecasting. These models are applied to simulated data for evaluation since there is no control of the influenced parameters in all of the real cost data. In the final stage, a two-level MOGA is employed to optimize risk-based LCC analysis to find the optimal replacement time for reparable system. Level one uses a MOGA based on a risk model to provide a variation of risk percentages, while level two uses a MOGA based on an LCC model to estimate the optimal reparable system replacement time.

The results of the first stage show the best cluster centre optimization for data clustering with low  and high intensity. Three cluster centres were selected because these centres have a geometry that is suitable for the highest data reduction of 27%. The best optimized interval is used for imputing missing data. The results of the second stage show the drawbacks of time series forecasting using a MOGA based on the DR model. The MOGA based on the ARIMA model yields better forecasting results. The results of the final stage show the drawbacks of the MOGA based on a risk-based LCC model regarding its estimation. However, the risk-based LCC model offers the possibility of optimizing the replacement schedule.

However, MOGA is highly promising for allowing optimization compared with other methods that were investigated in the present thesis.

With increase in the use of railway transport, ensuring robustness in railway timetables has never been this important. In a dense railway timetable even a small disturbance can propagate easily and affect trains' arrival and departure times. In a robust timetable small delays are absorbed and knock-on effects are prevented effectively. The aim of this thesis is to study how optimization tools can support the generation of robust railway traffic timetables. We address two Train Timetabling Problems (TTP) and for both problems we apply Mixed Integer Linear Programming (MILP) to solve them from network management perspectives. The first problem is how robustness in a given timetable can be assessed and ensured. To tackle this problem, a headway-based method is introduced. The proposed method is implemented in real timetables and evaluated from performance perspectives. Furthermore, the impact of the proposed method on capacity utilization, heterogeneity and the speed of trains, is monitored. Results show that the proposed method can improve robustness without imposing major changes in timetables. The second problem addressed in the thesis is how robustness can be assessed and maintained in a given timetable when allocating additional traffic and maintenance slots. Different insertion strategies are studied and their consequences on capacity utilization and on the properties of the timetables are analyzed. Two different insertion strategies are considered: i) simultaneous and ii) stepwise insertion. The results show that inserting the additional trains simultaneously usually results in generating more optimal solutions. However, solving this type of problem is computationally challenging. We also observed that the existing robustness metrics cannot capture the essential properties of having more robust timetables. Therefore we proposed measuring Channel Width, Channel Width Forward, Channel Width Behind and Track Switching.

Furthermore, the experimental analysis of the applied MILP model shows that some cases are computationally hard to solve and there is a need to decrease the computation time. Hence several valid inequalities are developed and their effects on the computation time are analyzed.

This thesis contains three papers which are appended. The results of this thesis are of special interests for railway traffic planners and it would support their working process. However, railway traffic operators and passengers also benefit from this study.

För att kunna ta ansvaret för infrastrukturen över hela dess livscykel måste Trafikverket ha förmågan att, i egen regi, långsiktigt tillgodogöra sig data, information och kunskap om anläggningens tillstånd. Denna förmåga utgör en bas för myndighetens arbete som proaktiv tillgångsförvaltare, möjliggör en effektiv trafikstyrning och är en förutsättning för att kunna agera professionellt i beställarrollen. En del i att bygga och vårda denna förmåga handlar om att etablera interna arbetssätt och resurser i Trafikverket för att nyttja Big Data/AI i tillgångsförvaltningen på ett systematiskt och hållbart sätt.

Background: Establishing traceability from requirements documents to downstream artifacts early can be beneficial as it allows engineers to reason about requirements quality (e.g. completeness, consistency, redundancy). However, creating such early traces is difficult if downstream artifacts do not exist yet. Objective: We propose to use domain-specific taxonomies to establish early traceability, raising the value and perceived benefits of trace links so that they are also available at later development phases, e.g. in design, testing or maintenance. Method: We developed a recommender system that suggests trace links from requirements to a domain-specific taxonomy based on a series of heuristics. We designed a controlled experiment to compare industry practitioners' efficiency, accuracy, consistency and confidence with and without support from the recommender. Results: We have piloted the experimental material with seven practitioners. The analysis of self-reported confidence suggests that the trace task itself is very challenging as both control and treatment group report low confidence on correctness and completeness. Conclusions: As a pilot, the experiment was successful since it provided initial feedback on the performance of the recommender, insight on the experimental material and illustrated that the collected data can be meaningfully analysed. © 2020 IEEE.