Discrete Event Simulation and Optimization Approaches for the Predictive Maintenance of Railway Infrastructure

This thesis is carried out within the PhD Course in Logistics and Transport at CIELI - Italian Centre of Excellence on Logistics, Transport and Infrastructures, University of Genoa. In this work, a discrete event simulation and optimization model is created to schedule the predictive maintenance activities. Nowadays, after a severe decrease of transport demand during the pandemic period, rail public transport is resuming a central role for both freight and passenger transport. To cope with this increase in demand, to maintain high safety standards and to avoid unnecessary costs, the idea is to switch to predictive maintenance strategy, intervening before an asset failure and when it has reached a certain state of degradation. The degradation and asset future conditions are predicted according to probabilistic models and maintenance deadlines are defined by applying a risk based approach. The problem is first formulated as a MILP (Mixed Integer Linear Programming) optimization problem and then transformed into a simulation-based optimization problem using the ExtendSim software. Different simulative models are created to take into account the stochastic nature of some variables in real processes. After the formal description of the models, some real-world applications are presented. Finally, considerations on the proposed approach are reported highlighting limits and challenges in predictive maintenance planning, such as lack of data and the stochastic and complex environment

A framework for creating production and inventory control strategies

In multiproduct manufacturing systems, it is difficult to assure that an optimised setting of a pull production control strategy will be able to maintain its service level and inventory control performances. This is because the competition for resources among products is liable to make them affect the service levels of one another. By comparing different pull strategies, this research has observed that tightly coupled strategies are able to maintain lower amount of inventory than decoupled strategies, but they do so at the detriment of service level robustness. As a result, tightly coupled strategies are better suited to manufacturing environments with low variability, while decoupled strategies are more robust in high variability environments. Here, robustness is a measure of how well a strategy is able to minimise the drop below its original optimised service level when the initial system conditions change. Furthermore, the Kanban allocation policy applied under a strategy plays a major role in its ability to manage the performances of multiple products. Experimental results show that the Shared Kanban Allocation Policy (SKAP) keeps a lower amount of inventory than the Dedicated Kanban Allocation Policy (DKAP), but it is more susceptible to the variability in the demand or processing times of one product impacting the service level of another. Therefore, a Hybrid Kanban allocation policy (HKAP) that combines both the DKAP and the SKAP has been implemented. This approach considers products’ demand and processing time attributes before categorising them into the same Kanban sharing group. The results of the implementation of the HKAP show that it can keep as low inventory as the SKAP and avoid products impacting the service levels of one another. Additionally, it offers a better approach to managing large multiproduct systems, as the performances of product groups can be differentially managed through the combination of Kanban sharing and dedication policies. Lastly, the observations on the performances of strategies and policies under different system conditions can be used as a framework through which line designers select strategies and policies to suit their manufacturing system

Combining scenario planning and system dynamics : an application based study

Informed policies and strategies (hereon policies) are important to any government or organisation. Information is used to inform policy development and make them more robust. Interventions can help identify and structure complex information and inform mental models to assist policy development. This work aims to combine scenario planning and system dynamics to inform policy development. Scenario planning is an approach that collects and structures information to assist developing policies for the future. However, it has flaws that can lead to problems with the scenarios they develop and ineffective or even maladaptive policies. Such flaws include its subjective nature and dependence on informed mental models, for which the field provides little formal guidance to address. System dynamics aims to inform peoples' mental models. It aims to uncover the endogenous causes behind the behaviour of a system. The approach employs techniques that encourage people to surface and question their mental models and formally tests them using mathematical models. System dynamics has flaws of its own: it is often misapplied, its goals and limitations miscommunicated, and its ability to address different systems questioned. This work aims to help scenario planning and system dynamics overcome their flaws by combining them in an applied approach. Five studies were conducted to test if system dynamics could inform scenario planning and to test the reverse was also true. The studies ranged from a predominantly scenario planning exercise with a industry federation in the United Kingdom, which involved minimal systems mapping, to a greater integration of the two approaches that was conducted with a community organisation. The studies developed a workshop based scenario planning approach that executed a series of activities to generate scenarios. The studies demonstrated that preliminary system maps of the scenarios were often of little use for developing further using system dynamics and, according to participants, these maps assisted little with scenario development. The studies identified the need to educate participants about system dynamics and the need for a specific targeted problem for a system mapping exercise. The studies exemplified how the two approaches can identify and provide novel information for the other. Mixed evidence was found regarding the mapping of systems observed in different scenarios and integrating these perspectives. These attempts to integrate the approaches also identified theoretical differences, including system dynamics' narrower focus and more specific view of causal relationships. These studies highlighted how scenario planning and system dynamics can be used to inform each other. In practice, effectively-executed system mapping and system dynamics modelling helped surface and test peoples' mental models, assisting scenario planning. Scenario planning, however, offered minimal assistance to system dynamics. The approaches offered each other mutual assistance, particularly with framing and preventing information filtering (exclusion), learning, and communication. This work highlights the limitations and benefits of integrating these approaches. With this understanding more work can now be conducted to take scenario planning and system dynamics forward and develop them as co-informing and co-supporting structures of policies for governments and organisations, both for the present and the future

Mixed structural models for decision making under uncertainty using stochastic system simulation and experimental economic methods: application to information security control choice

This research is concerned with whether and to what extent information security managers may be biased in their evaluation of and decision making over the quantifiable risks posed by information management systems where the circumstances may be characterized by uncertainty in both the risk inputs (e.g. system threat and vulnerability factors) and outcomes (actual efficacy of the selected security controls and the resulting system performance and associated business impacts). Although ‘quantified security’ and any associated risk management remains problematic from both a theoretical and empirical perspective (Anderson 2001; Verendel 2009; Appari 2010), professional practitioners in the field of information security continue to advocate the consideration of quantitative models for risk analysis and management wherever possible because those models permit a reliable economic determination of optimal operational control decisions (Littlewood, Brocklehurst et al. 1993; Nicol, Sanders et al. 2004; Anderson and Moore 2006; Beautement, Coles et al. 2009; Anderson 2010; Beresnevichiene, Pym et al. 2010; Wolter and Reinecke 2010; Li, Parker et al. 2011) The main contribution of this thesis is to bring current quantitative economic methods and experimental choice models to the field of information security risk management to examine the potential for biased decision making by security practitioners, under conditions where information may be relatively objective or subjective and to demonstrate the potential for informing decision makers about these biases when making control decisions in a security context. No single quantitative security approach appears to have formally incorporated three key features of the security risk management problem addressed in this research: 1) the inherently stochastic nature of the information system inputs and outputs which contribute directly to decisional uncertainty (Conrad 2005; Wang, Chaudhury et al. 2008; Winkelvos, Rudolph et al. 2011); 2) the endogenous estimation of a decision maker’s risk attitude using models which otherwise typically assume risk neutrality or an inherent degree of risk aversion (Danielsson 2002; Harrison, Johnson et al. 2003); and 3) the application of structural modelling which allows for the possible combination and weighting between multiple latent models of choice (Harrison and Rutström 2009). The identification, decomposition and tractability of these decisional factors is of crucial importance to understanding the economic trade-offs inherent in security control choice under conditions of both risk and uncertainty, particularly where established psychological decisional biases such as ambiguity aversion (Ellsberg 1961) or loss aversion (Kahneman and Tversky 1984) may be assumed to be endemic to, if not magnified by, the institutional setting in which these decisions take place. Minimally, risk averse managers may simply be overspending on controls, overcompensating for anticipated losses that do not actually occur with the frequency or impact they imagine. On the other hand, risk-seeking managers, where they may exist (practitioners call them ‘cowboys’ – they are a familiar player in equally risky financial markets) may be simply gambling against ultimately losing odds, putting the entire firm at risk of potentially catastrophic security losses. Identifying and correcting for these scenarios would seem to be increasingly important for now universally networked business computing infrastructures. From a research design perspective, the field of behavioural economics has made significant and recent contributions to the empirical evaluation of psychological theories of decision making under uncertainty (Andersen, Harrison et al. 2007) and provides salient examples of lab experiments which can be used to elicit and isolate a range of latent decision-making behaviours for choice under risk and uncertainty within relatively controlled conditions versus those which might be obtainable in the field (Harrison and Rutström 2008). My research builds on recent work in the domain of information security control choice by 1) undertaking a series of lab experiments incorporating a stochastic model of a simulated information management system at risk which supports the generation of observational data derived from a range of security control choice decisions under both risk and uncertainty (Baldwin, Beres et al. 2011); and 2) modeling the resulting decisional biases using structural models of choice under risk and uncertainty (ElGamal and Grether 1995; Harrison and Rutström 2009; Keane 2010). The research contribution consists of the novel integration of a model of stochastic system risk and domain relevant structural utility modeling using a mixed model specification for estimation of the latent decision making behaviour. It is anticipated that the research results can be applied to the real world problem of ‘tuning’ quantitative information security risk management models to the decisional biases and characteristics of the decision maker (Abdellaoui and Munier 1998