Topology optimization is commonly used to distribute a given amount of material to obtain the stiffest structure, with predefined fixed loads. The present work investigates the result of applying stress constraints to topology optimization, for problems with design-depending loading, such as self-weight and pressure. In order to apply pressure loading, a material boundary identification scheme is proposed, iteratively connecting points of equal density. In previous research, design-dependent loading problems have been limited to compliance minimization. The present study employs a more practical approach by minimizing mass subject to failure constraints, and uses a stress relaxation technique to avoid stress constraint singularities. The results show that these design dependent loading problems may converge to a local minimum when stress constraints are enforced. Comparisons between compliance minimization solutions and stress-constrained solutions are also given. The resulting topologies of these two solutions are usually vastly different, demonstrating the need for stress-constrained topology optimization.

In this thesis, we address the problem of minimizing the average delay of data pack-ets served by a transmitter on a static, point-to-point link. The transmitter dynamically chooses state-dependent admission and transmission rates, while adhering to average throughput and transmission power constraints. The transmitter is modelled as an infinite buffer Markov queue with adjustable arrival and service rates. Data packets arrive at the system according to a Poisson process with rate, Λ, and are admitted at a rate, λnwith 0≤ λn ≤ Λ, depending on the number, n, of data packets present in the system. The packet size is assumed to be exponentially distributed, and the controller chooses a transmission rate, µn, at most equal to a maximum value, M, depending on the current backlog, n, in the system. The objective is to minimize the average delay of data packets in the system subject to a throughput lower bound constraint, while satisfying an upper bound on the average transmission power. This constrained MDP problem is solved using a Lagrange relaxation approach and analysed for the cases with throughput and power constraints that are achievable with equality by appropri-ate values of the Lagrange multipliers. A procedure is developed, based on explicit formulae, using which optimal admission and service rates as a function of the packet queue length are obtained.

In this thesis we study some storage loading problems motivated from several practical contexts, under different types of uncertainty on the items’ data. To have robust stacking solutions against the data uncertainty, we apply the concepts of strict and adjustable robustness. We first give complexity results for various storage loading problems with stacking constraints, and point out some interesting settings in which the adjustable robust problems can be solved more efficiently than the strict ones. Then we propose different solution algorithms for the robust storage loading problems, and figure out which algorithm performs best for which data setting. We also propose a robust optimization framework dealing with storage loading problems under stochastic uncertainty. In this framework, we offer several rule-based ways of scenario generation to derive different uncertainty sets, and analyze the trade-off between cost and robustness of the robust stacking solutions. Additionally, we introduce a novel approach in dealing with stability issues of stacking configurations. Our key idea is to impose a limited payload on each item depending on its weight. We then study a storage loading problem with the interaction of stacking and payload constraints, as well as uncertainty on the weights of items, and propose different solution approaches for the robust problems.

Yes / In this paper, we extend Tobin’s Q model under financial frictions (Hennesy, Levy, and Whited, Journal of Financial Economics (2007)), using a discrete-time version of their model, to include private benefits of control of managers and other stakeholders and soft budget constraints in the form of money injections into the firm. Managers are not viewed to maximise shareholder value, but to maximise the value of their shareholding plus their private benefits of control. Private benefits of control introduce elements of asset stripping into the model. We characterize the optimal investment policy, analyse comparative statics and discuss applications to firms in transitional economies.

Yes / In this paper, we empirically analyze the impact of ownership groups on companies’ investment rates in Ukraine using a new dynamic Tobin’s Q model allowing investment rates to depend on present and lagged Q. We find that the presence of a majority in and increases in state, non-domestic and financial companies’ ownership has a significantly negative impact on investment rates. State and insider ownership are associated with soft budget constraints whereas non-domestic, financial companies’ and financial and industrial groups’ ownership with hard budget constraints. The dynamic model shows persistence in the market-to-book value of equity, the proxy for Q.

Yes / We empirically analyze the impact of different ownership groups on companies’ investment rates in Ukraine allowing investment rates to depend on present and past market-to-book values of equity. We relate the impact to the presence of soft and hard budget constraints, to the free cash flow and the cash constraint hypothesis and discuss over- and under-investment. Several robustness checks, in particular, the potential endogeneity of ownership variables are considered.

In this study, we consider the Operational Fixed Job Scheduling Problem on identical parallel machines. The problem is to select a subset of jobs for processing among a set of available jobs with fixed arrival times and deadlines, so as to maximize the total weight. We analyze the problem under three environments: Working time constraints, Spread time constraints, and Machine dependent job weights. We show that machine eligibility constraints appear as a special case of the last environment. We settle the complexity status of all problems, and show that they are NP-hard in the strong sense and have several polynomially solvable special structures. For all problems, we propose branch and bound algorithms that employ powerful reduction mechanisms and efficient lower and upper bounds. The results of our computational runs reveal that, the algorithms return optimal solutions for problem instances with up to 100 jobs in reasonable solution times.

Constraint programming (CP) is a technology in which a combinatorial problem is modelled as a conjunction of constraints on variables ranging over given initial domains, and optionally an objective function on the variables. Such a model is given to a general-purpose solver performing systematic search to find constraint-satisfying domain values for the variables, giving an optimal value to the objective function. A constraint predicate (also known as a global constraint) does two things: from the modelling perspective, it allows a modeller to express a commonly occurring combinatorial substructure, for example that a set of variables must take distinct values; from the solving perspective, it comes with a propagation algorithm, called a propagator, which removes some but not necessarily all impossible values from the current domains of its variables when invoked during search. Although modern CP solvers have many constraint predicates, often a predicate one would like to use is not available. In the past, the choices were either to reformulate the model or to write one's own propagator. In this dissertation, we contribute to the automatic design of propagators for new predicates. Integer time series are often subject to constraints on the aggregation of the features of all maximal occurrences of some pattern. For example, the minimum width of the peaks may be constrained. Automata allow many constraint predicates for variable sequences, and in particular many time-series predicates, to be described in a high-level way. Our first contribution is an algorithm for generating an automaton-based predicate description from a pattern, a feature, and an aggregator. It has previously been shown how to decompose an automaton-described constraint on a variable sequence into a conjunction of constraints whose predicates have existing propagators. This conjunction provides the propagation, but it is unknown how to propagate it efficiently. Our second contribution is a tool for deriving, in an off-line process, implied constraints for automaton-induced constraint decompositions to improve propagation. Further, when a constraint predicate functionally determines a result variable that is unchanged under reversal of a variable sequence, we provide as our third contribution an algorithm for deriving an implied constraint between the result variables for a variable sequence, a prefix thereof, and the corresponding suffix.

No description available.

The fundamental objective of this study was to determine the impact of PLAS Land Reform Projects on the livelihood (financial, human, physical, natural and social capitals) of beneficiaries. The population of the study included all beneficiaries (97) of PLAS projects within Dr. Kenneth Kaunda District Municipality. Fifty four beneficiaries were randomly selected from all 36 projects and interviewed using a structured questionnaire. Data collected was sorted, coded and analysed using version 21 of the Statistical Package for Social Sciences (SPSS). Frequency count and percentage were used to summarize the data. The identified major constraints affecting PLAS projects were found to be: lack of resources (77.9%), lack of finance (77.8%), lack of water (77.8%), lack of incentives (66.7%), lack/poor infrastructure (64.8%) and high inputs cost (59.3%). The respondents indicated that lack of finance was caused by both lack of government support and inability to access credit from financial institutions, Jack of assets and land ownership which could be used as security to secure loans. Binary Logit Regression Model was used to determine factors influencing the impact of PLAS projects on the livelihood of beneficiaries. Five explanatory variables found to be statistically significant were: size of projects (Z=1-.905: P<0.05), purchase price of projects (Z=-2.258: P>0.01), sufficient funding (Z=1.657: P<0.01), established market (Z=2.552: P<0.01) and age of farmers' (Z=-2.697: P>0.05). Wilcoxon Sign-rank Sum Test was used to determine the "before" and "after" impact of PLAS Land Redistribution projects on the livelihood (financial, human, physical, natural and social capitals) of beneficiaries. The findings showed that significant difference existed on the livelihood (social, financial, physical, natural and human capital) before and after participating in PLAS projects. The result indicated an inverse relationship in terms of the impact of PLAS projects on the livelihood of beneficiaries implying that discontinuation or no participation in PLAS projects could reduce livelihood capitals or negatively affect beneficiaries' livelihood. / Thesis (M.Sc.(Agric Economics) North-West University, Mafikeng Campus, 2013