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Data driven modelling has become an important aspect of modern process control and condition monitoring systems. The topic has been extensively studied in academia for several decades and applications in industry are continually increasing. In the past 20 years there has been in increased interest in the use of multivariate statistical techniques in the process industries. This interest has arisen from the need to identify techniques that are able to cope with the highly correlated data sets that are encountered in these industries as levels of instrumentation increase.

Although previous literature has developed and refined Simons' levers of control framework by emphasising the interrelatedness of different control systems (e.g. Henri 2006, Tuomela 2005, Widener 2007), little evidence has been provided on how these control systems are used consistently without creating any disorders or complexities. Therefore, inspired by recent turn in management accounting (Ahrens & Chapman 2005, 2007, Chua 2007, Hansen & Mouritsen 2005) and strategy research (Jarzabkowski 2005, Johnson et al. 2003, Whittington 1996, 2006) to practice based theorising, this study aimed to contribute to 'theoretical refinement' (Keating 1995) of Simons' framework by exploring how Simons' four levers of control work in different capacities in crafting and implementing strategy. This longitudinal field study of a large Japanese supermarket chain was conducted to explore interface between MCS and strategy in the process of < strategising. More specifically, the thesis attempted to uncover the black box of the relationship between strategy and MCS.

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The last decade has seen a shift in emphasis from centralised to decentralised systems to meet the demanding coordination requirements of today's complex computer systems. In such systems, the aim is to achieve effective decentralised control through autonomous software agents that perform local decision-making based on incomplete and imperfect information. Specifically, when the various agents interact, the system behaves as a computational ecology with no single agent coordinating their actions. In this thesis, we focus on one specific type of computational ecology, the Continuous Double Auction (CDA), and investigate market-oriented approaches to decentralised control. In particular, the CDA is a fixed-duration auction mechanism where multiple buyers and sellers compete to buy and sell goods, respectively, in the market, and where transactions can occur at any time whenever an offer to buy and an offer to sell match. Now, in such a market mechanism, the decentralised control is achieved through the decentralised allocation of resources, which, in turn, is an emergent behaviour of buyers and sellers trading in the market. The CDA was chosen, among the plenitude of auction formats available, because it allows efficient resource allocation without the need of a centralised auctioneer. Against this background, we look at both the structure and the behaviour of the CDA in our attempt to build an efficient and robust mechanism for decentralised control. We seek to do this for both stable environments, in which the market demand and supply do not change and dynamic ones in which there are sporadic changes (known as market shocks). While the structure of the CDA defines the agents' interactions in the market, the behaviour of the CDA is determined by what emerges when the buyers and sellers compete to maximise their individual profits. In more detail, on the structural aspect, we first look at how the market protocol of the CDA can be modified to meet desirable properties for the system (such as high market efficiency, fairness of profit distribution among agents and market stability). Second, we use this modified protocol to efficiently solve a complex decentralised task allocation problem with limited-capacity suppliers that have start-up production costs and consumers with inelastic demand. Furthermore, we demonstrate that the structure of this CDA variant is very efficient (an average of 80% and upto 90%) by evaluating the mechanism with very simple agent behaviours. In so doing, we emphasise the effect of the structure, rather than the behaviour, on efficiency. In the behavioural aspect, we first developed a multi-layered framework for designing strategies that autonomous agents can use for trading in various types of market mechanisms. We then use this framework to design a novel Adaptive-Aggressiveness (AA) strategy for the CDA. Specifically, our bidding strategy has both a short and a long-term learning mechanism to adapt its behaviour to changing market conditions and it is designed to be robust in both static and dynamic environments. Furthermore, we also developed a novel framework that uses a two-population evolutionary game theoretic approach to analyse the strategic interactions of buyers and sellers in the CDA. Finally, we develop effective methodologies for evaluating strategies for the CDA in both homogeneous and heterogeneous populations, within static and dynamic environments. We then evaluate the AA bidding strategy against the state of the art using these methodologies. By so doing, we show that, within homogeneous populations, the AA strategy outperformed the benchmarks, in terms of market efficiency, by up to 3.6% in the static case and 2.8% in the dynamic case. Within heterogeneous populations, based on our evolutionary game theoretic framework, we identify that there is a probability above 85% that the AA strategy will eventually be adopted by buyers and sellers in the market (for being more efficient) and, therefore, AA is also better than the benchmarks in heterogeneous populations as well.

Η διπλωµατική εργασία αποτελεί συνέχεια της µελέτης προβληµάτων χρονοπρογραµµατισµού µε αυστηρές προθεσµίες που ξεκίνησε η Αµαλία Στούµπου στην δικιά της διπλωµατική εργασία µε όνοµα "Προβλήµατα Επιτάχυνσης ∆ιεργασιών σε Grid Computing: Αλγόριθµοι και Πολυπλοκότητα". Εξετάζονται προβλήµατα δροµολόγησης διεργασιών σε περισσότερους από έναν, ίδιους µεταξύ τους, επεξεργαστές. ∆ίνονται αλγόριθµοι που λύνουν το πρόβληµα ελαχιστοποίησης του συνολικού χρόνου εκτέλεσης, µε αυστηρές προθεσµίες, αρχικά για 2 και στη συνέχεια για m επεξεργαστές. Οι αλγόριθµοι αυτοί έχουν ψευδοπολυωνυµική πολυπλοκότητα. Στη συνέχεια εξετάζονται προβλήµατα δροµολόγησης και επιτάχυνσης διεργασιών µε ίδιο χρόνο εκτέλεσης, σε περιβάλλοντα µε ίδιους µεταξύ τους επεξεργαστές και δίνονται πολυωνυµικοί αλγόριθµοι που τα λύνουν. Τέλος αναφέρονται συνοπτικά ορισµένα προβλήµατα του ευρύτερου χώρου προϐληµάτων χρονοπρογραµµατισµού που µπορούν να προσεγγιστούν ή να λυθούν µε τεχνικές που εφαρµόστηκαν για τη λύση των προηγούµενων προβληµάτων που αναφέρθηκαν. / This thesis is a continuation of the study of scheduling problems with strict deadlines that begun in the thesis "Προβλήµατα Επιτάχυνσης ∆ιεργασιών σε Grid Computing : Αλγόριθµοι και Πολυπλοκότητα" by Amalia Stoumpou. We study scheduling problems on more than one parallel processors. Algorithms are given that solve the problem of minimizing the makespan with strict deadlines, first for 2 and then for m processors. These algorithms are pseudopolynomial in complexity. We also study problems of scheduling and speedup of processes with the same execution time in parallel processor environments and we give pseudopolynomial algorithms that solve them. Finally, we mention briefly other problems that can be solved or approached using the techniques that we applied to solve the previous problems.