Maintenance strategy optimization using a continuous-state partially observable semi-Markov decision process

Due to the limitation of current condition monitoring technologies, the estimates of asset health states may contain some uncertainties. A maintenance strategy ignoring this uncertainty of asset health state can cause additional costs or downtime. The partially observable Markov decision process (POMDP) is a commonly used approach to derive optimal maintenance strategies when asset health inspections are imperfect. However, existing applications of the POMDP to maintenance decision-making largely adopt the discrete time and state assumptions. The discrete-time assumption requires the health state transitions and maintenance activities only happen at discrete epochs, which cannot model the failure time accurately and is not cost-effective. The discrete health state assumption, on the other hand, may not be elaborate enough to improve the effectiveness of maintenance. To address these limitations, this paper proposes a continuous state partially observable semi-Markov decision process (POSMDP). An algorithm that combines the Monte Carlo-based density projection method and the policy iteration is developed to solve the POSMDP. Different types of maintenance activities (i.e., inspections, replacement, and imperfect maintenance) are considered in this paper. The next maintenance action and the corresponding waiting durations are optimized jointly to minimize the long-run expected cost per unit time and availability. The result of simulation studies shows that the proposed maintenance optimization approach is more cost-effective than maintenance strategies derived by another two approximate methods, when regular inspection intervals are adopted. The simulation study also shows that the maintenance cost can be further reduced by developing maintenance strategies with state-dependent maintenance intervals using the POSMDP. In addition, during the simulation studies the proposed POSMDP shows the ability to adopt a cost-effective strategy structure when multiple types of maintenance activities are involved.

Autonomous vehicle path planning for persistence monitoring under uncertainty using Gaussian based Markov decision process

One of the main challenges facing online and offline path planners is the uncertainty in the magnitude and direction of the environmental energy because it is dynamic, changeable with time, and hard to forecast. This thesis develops an artificial intelligence for a mobile robot to learn from historical or forecasted data of environmental energy available in the area of interest which will help for a persistence monitoring under uncertainty using the developed algorithm.

Corporate governance and its influence on the investment decision process of equity market's professionals: An inside view of how corporate governance issues influence the decision-making process of sell-side and buy-side professionals when analyzing Brazilian listed companies

Corporate governance has been in the spotlight for the past two decades, being subject of numerous researches all over the world. Governance is pictured as a broad and diverse theme, evolving through different routes to form distinct systems. This scenario together with 2 types of agency problems (investor vs. management and minorities vs. controlling shareholders) produce different definitions for governance. Usually, studies investigate whether corporate governance structures influence firm performance, and company valuation. This approach implies investors can identify those impacts and later take them into consideration when making investment decisions. However, behavioral finance theory shows that not always investors take rational decisions, and therefore the modus operandi of those professionals needs to be understood. So, this research aimed to investigate to what extent Brazilian corporate governance standards and practices influence the investment decision-making process of equity markets' professionals from the sell-side and buy-side. This exploratory study was carried out through qualitative and quantitative approaches. In the qualitative phase, 8 practitioners were interviewed and 3 dimensions emerged: understanding, pertinence and practice. Based on the interviews’ findings, a questionnaire was formulated and distributed to buy-siders and sell-siders that cover Brazilian stocks. 117 respondents from all over the world contributed to the study. The data obtained were analyzed through structural equation modeling and descriptive statistics. The 3 dimensions became 5 constructs: definition (institutionalized governance, informal governance), pertinence (relevance), practice (valuation process, structured governance assessment) The results of this thesis suggest there is no definitive answer, as the extent to which governance will influence an investment decision process will depend on a number of circumstances which compose the context. The only certainty is the need to present a “corporate governance behavior”, rather than simply establishing rules and regulations at firm and country level.

Strategic decision making in international firms: effect of top management team internationalization on international strategic decision process

In this research project, I have integrated two research streams on international strategic decisions making in international firms: upper echelons or top management teams (TMT) internationalization research and international strategic decision making process research. Both research streams in international business literature have evolved independently, but there is a potential in combining these two streams of research. The first empirical paper “TMT internationalization and international strategic decision making process: a decision level analysis of rationality, speed, and performance” explores the influence of TMT internationalization on strategic decision rationality and speed and, subsequently, their effect on international strategic decision effectiveness (performance). The results show that the internationalization of TMT is positively related to decision effectiveness and this relationship is mediated by decision rationality while the hypotheses regarding the association between TMT internationalization and decision speed, and the mediating effect of speed were not supported. The second paper “TMT internationalization and international strategic decision rationality: the mediating role of international information” of my thesis is a simple but logical extension of first paper. The first paper showed that TMT Internationalization has a significant positive effect on international strategic decision rationality. The second paper explicitly showed that TMT internationalization affect on international strategic decision rationality comes from two sources: international experience (personal international knowledge and information) and international information collected from managerial international contacts. For this research project, I have collected data from international software firms in Pakistan. My research contributes to the literature on upper echelons theory and strategic decision making in context of international business and international firms by explicitly examining the link between TMT internationalization and characteristics of strategic decisions making process (i.e. rationality and speed) in international firms and their possible mediating effect on performance.

An application of decision process modelling to manufacturing system design

As levels of investment in advanced manufacturing systems increase, effective project management becomes ever more critical. This paper demonstrates how the model proposed by Mintzberg, Raisinghani and Theoret in 1976, which structures complicated strategic decision processes, can be applied to the design of new production systems for both descriptive and analytical research purposes. This paper sets a detailed case study concerning the design and development of an advanced manufacturing system within the Mintzberg decision model and so breaks down the decision sequence into constituent parts. It thus shows how a structured model can provide a framework for the researcher who wishes to study decision episodes in the design of manufacturing facilities in greater depth.

Strategic positioning:an integrated decision process for manufacturers

Purpose – This paper describes research that has sought to create a formal and rational process that guides manufacturers through the strategic positioning decision. Design/methodology/approach – The methodology is based on a series of case studies to develop and test the decision process. Findings – A decision process that leads the practitioner through an analytical process to decide which manufacturing activities they should carryout themselves. Practical implications – Strategic positioning is concerned with choosing those production related activities that an organisations should carry out internally, and those that should be external and under the ownership and control of suppliers, partners, distributors and customers. Originality/value – This concept extends traditional decision paradigms, such as those associated with “make versus buy” and “outsourcing”, by looking at the interactions between manufacturing operations and the wider supply chain networks associated with the organisation.

Augmenting competitiveness of cities through tourism planning : the role of collaborative decision support systems

Many cities around the globe are now considering tourism facilities and their remarkable revenues in order to become competitive in the global economy. In many of these cities a great emphasis is given to the cultural tourism as it plays an important role in the establishment of creative and knowledge-base of cities. The literature points out the importance of local community support in cultural tourism. In such context, the use of new approach and technologies in tourism planning in order to increase the community participation and competitiveness of cities’ cultural assets gains a great significance. This paper advocates a new planning approach for tourism planning, particularly for cultural tourism, to increase the competitiveness of cities. As part of this new approach, the paper introduces the joined up planning approach integrated with a collaborative decision support system: ‘the community-oriented decision support system’. This collaborative planning support system is an effective and efficient tool for cultural tourism planning, which provides a platform for local communities’ participation in the development decision process.

Modelling and execution of asset management process via workflow automation

An Asset Management (AM) life-cycle constitutes a set of processes that align with the development, operation and maintenance of assets, in order to meet the desired requirements and objectives of the stake holders of the business. The scope of AM is often broad within an organization due to the interactions between its internal elements such as human resources, finance, technology, engineering operation, information technology and management, as well as external elements such as governance and environment. Due to the complexity of the AM processes, it has been proposed that in order to optimize asset management activities, process modelling initiatives should be adopted. Although organisations adopt AM principles and carry out AM initiatives, most do not document or model their AM processes, let alone enacting their processes (semi-) automatically using a computer-supported system. There is currently a lack of knowledge describing how to model AM processes through a methodical and suitable manner so that the processes are streamlines and optimized and are ready for deployment in a computerised way. This research aims to overcome this deficiency by developing an approach that will aid organisations in constructing AM process models quickly and systematically whilst using the most appropriate techniques, such as workflow technology. Currently, there is a wealth of information within the individual domains of AM and workflow. Both fields are gaining significant popularity in many industries thus fuelling the need for research in exploring the possible benefits of their cross-disciplinary applications. This research is thus inspired to investigate these two domains to exploit the application of workflow to modelling and execution of AM processes. Specifically, it will investigate appropriate methodologies in applying workflow techniques to AM frameworks. One of the benefits of applying workflow models to AM processes is to adapt and enable both ad-hoc and evolutionary changes over time. In addition, this can automate an AM process as well as to support the coordination and collaboration of people that are involved in carrying out the process. A workflow management system (WFMS) can be used to support the design and enactment (i.e. execution) of processes and cope with changes that occur to the process during the enactment. So far few literatures can be found in documenting a systematic approach to modelling the characteristics of AM processes. In order to obtain a workflow model for AM processes commonalities and differences between different AM processes need to be identified. This is the fundamental step in developing a conscientious workflow model for AM processes. Therefore, the first stage of this research focuses on identifying the characteristics of AM processes, especially AM decision making processes. The second stage is to review a number of contemporary workflow techniques and choose a suitable technique for application to AM decision making processes. The third stage is to develop an intermediate ameliorated AM decision process definition that improves the current process description and is ready for modelling using the workflow language selected in the previous stage. All these lead to the fourth stage where a workflow model for an AM decision making process is developed. The process model is then deployed (semi-) automatically in a state-of-the-art WFMS demonstrating the benefits of applying workflow technology to the domain of AM. Given that the information in the AM decision making process is captured at an abstract level within the scope of this work, the deployed process model can be used as an executable guideline for carrying out an AM decision process in practice. Moreover, it can be used as a vanilla system that, once being incorporated with rich information from a specific AM decision making process (e.g. in the case of a building construction or a power plant maintenance), is able to support the automation of such a process in a more elaborated way.

Asset life prediction and maintenance decision-making using a non-linear non-Gaussian state space model

Estimating and predicting degradation processes of engineering assets is crucial for reducing the cost and insuring the productivity of enterprises. Assisted by modern condition monitoring (CM) technologies, most asset degradation processes can be revealed by various degradation indicators extracted from CM data. Maintenance strategies developed using these degradation indicators (i.e. condition-based maintenance) are more cost-effective, because unnecessary maintenance activities are avoided when an asset is still in a decent health state. A practical difficulty in condition-based maintenance (CBM) is that degradation indicators extracted from CM data can only partially reveal asset health states in most situations. Underestimating this uncertainty in relationships between degradation indicators and health states can cause excessive false alarms or failures without pre-alarms. The state space model provides an efficient approach to describe a degradation process using these indicators that can only partially reveal health states. However, existing state space models that describe asset degradation processes largely depend on assumptions such as, discrete time, discrete state, linearity, and Gaussianity. The discrete time assumption requires that failures and inspections only happen at fixed intervals. The discrete state assumption entails discretising continuous degradation indicators, which requires expert knowledge and often introduces additional errors. The linear and Gaussian assumptions are not consistent with nonlinear and irreversible degradation processes in most engineering assets. This research proposes a Gamma-based state space model that does not have discrete time, discrete state, linear and Gaussian assumptions to model partially observable degradation processes. Monte Carlo-based algorithms are developed to estimate model parameters and asset remaining useful lives. In addition, this research also develops a continuous state partially observable semi-Markov decision process (POSMDP) to model a degradation process that follows the Gamma-based state space model and is under various maintenance strategies. Optimal maintenance strategies are obtained by solving the POSMDP. Simulation studies through the MATLAB are performed; case studies using the data from an accelerated life test of a gearbox and a liquefied natural gas industry are also conducted. The results show that the proposed Monte Carlo-based EM algorithm can estimate model parameters accurately. The results also show that the proposed Gamma-based state space model have better fitness result than linear and Gaussian state space models when used to process monotonically increasing degradation data in the accelerated life test of a gear box. Furthermore, both simulation studies and case studies show that the prediction algorithm based on the Gamma-based state space model can identify the mean value and confidence interval of asset remaining useful lives accurately. In addition, the simulation study shows that the proposed maintenance strategy optimisation method based on the POSMDP is more flexible than that assumes a predetermined strategy structure and uses the renewal theory. Moreover, the simulation study also shows that the proposed maintenance optimisation method can obtain more cost-effective strategies than a recently published maintenance strategy optimisation method by optimising the next maintenance activity and the waiting time till the next maintenance activity simultaneously.

Bounded parameter Markov Decision Processes with average reward criterion

Bounded parameter Markov Decision Processes (BMDPs) address the issue of dealing with uncertainty in the parameters of a Markov Decision Process (MDP). Unlike the case of an MDP, the notion of an optimal policy for a BMDP is not entirely straightforward. We consider two notions of optimality based on optimistic and pessimistic criteria. These have been analyzed for discounted BMDPs. Here we provide results for average reward BMDPs. We establish a fundamental relationship between the discounted and the average reward problems, prove the existence of Blackwell optimal policies and, for both notions of optimality, derive algorithms that converge to the optimal value function.