Throughout this research the focus has been on unraveling of the factors and relations that link different aspects of collaborative workflow to strategic performance management. However, the same... Show moreThroughout this research the focus has been on unraveling of the factors and relations that link different aspects of collaborative workflow to strategic performance management. However, the same issues that applied to strategic performance management of supply chains also apply to other areas of strategic performance in business. The following (to be - tested) recommendations, organized along the lines of the "expected managerial contributions" therefore apply both to strategic performance management of supply chains and other strategic business processes. a) The SPI (Strategic Performance Inhibitor) Model will enable an integrated approach to (i) Problem structuring (ii) Problem solving and (iii) Learning for managers on potential threats and problem to strategic performance of supply chains. b) The SPI classification will provide a systematic and structured manner of communicating and addressing potential problems and risk to strategic performance of supply chains. Once classified, each class will have its own type of impact on strategic performance of supply chains and consequently the resolution for it. c) Collaborative Workflow will enable inventory (i.e. right quantity at the right time and right place) to be substitutes by Information (i.e. the right information at the right time and the right place) resulting in cost reduction. Show less
Real-world (black-box) optimization problems often involve various types of uncertainties and noise emerging in different parts of the optimization problem. When this is not accounted for,... Show moreReal-world (black-box) optimization problems often involve various types of uncertainties and noise emerging in different parts of the optimization problem. When this is not accounted for, optimization may fail or may yield solutions that are optimal in the classical strict notion of optimality, but fail in practice. Robust optimization is the practice of optimization that actively accounts for uncertainties and/or noise. Evolutionary Algorithms form a class of optimization algorithms that use the principle of evolution to find good solutions to optimization problems. Because uncertainty and noise are indispensable parts of nature, this class of optimization algorithms seems to be a logical choice for robust optimization scenarios. This thesis provides a clear definition of the term robust optimization and a comparison and practical guidelines on how Evolution Strategies, a subclass of Evolutionary Algorithms for real-parameter optimization problems, should be adapted for such scenarios. Show less
