This study originally emanated from the debate on value creation through projects, which has become more prevalent in project management literature in recent times. Earned value analysis, which is widely used to measure and report the performance of project value, does not include the occurrence of value leaks in its calculations and reporting. Although there is a scarcity of literature on the issue of value leaks during project deployment, it is considered to be a big issue which can make or break a project’s value success. This lack of research is more pronounced in network expansion projects in the Ghanaian telecommunication industry, however, considering the level of investment by these network operators, and their contribution to economic growth, the occurrence of value leaks can be suspected. Therefore, the overall aim of the study was to develop a diagnostic model that aids in the easy identification of value leaks, so that they can be controlled, and remedied to minimise the forgone unrealised project value. In view of this, the study adopted an exploratory sequential mixed research design. The qualitative phase employed a multiple-case study approach to explore the concept of value leaks and the extent to which it becomes problematic in delivering overall project value. The quantitative phase, through a survey study, adopted factor analysis to test and validate the findings from the case study, and analyses were also performed to test the conceptual model fit to the retained dataset. The findings culminated in the development of the “Value Leaks-Flashlight”, with an add-on called the “Tolerable Nut” to theorise the concept of value leaks. This practical establishment of the value leak concept cemented the development of the value leaks diagnostic model through the application of the “CIIR” acronym, which fulfils the overall aim of the study. The study contributes to the contemporary literature in the field of project management, as the concept of project value leaks is still gaining prominence, and only a few empirical studies have thus far been conducted. The concept of value leaks enlightens the perspective of project management practitioners in their quest to achieve value through projects. / Business Management / D. Phil. (Management Studies)

The increasing trend in frequency of natural disasters in tandem with globalization of business makes the agricultural supply chain significantly vulnerable to disruption. This thesis presents a pragmatic approach for creating a Business Continuity Model that can notify supply chain planners when there is an increase in risk of agriculture supply chain disruption due to natural disasters. The methodology presented in this thesis applied big data analytics and machine learning algorithms along with agriculture product related exponential decay function to create a regionalized composite risk score, that incorporated both direct and indirect risk associated with the Agriculture Fresh Supply Chain. This model will aid supply chain planners in creating and implementing contingency plans, at the right time per given food production location. This risk score can help food manufacturing organizations to have a Business Continuity Plan that alleviate agriculture business supply chain interruptions. An example application of this model is illustrated with a melon packaging industry.

Worldwide globalization has made supply chains more vulnerable to risk factors, increasing the associated costs of outsourcing goods. Outsourcing is highly beneficial for any company that values building upon its core competencies, but the emergence of the COVID-19 pandemic and other crises have exposed significant vulnerabilities within supply chains. These disruptions forced a shift in the production of goods from outsourcing to domestic methods. This paper considers a multi-echelon supply chain model with global and domestic raw material suppliers, manufacturing plants, warehouses, and markets. All levels within the supply chain network are evaluated from a holistic perspective, calculating a total cost for all levels with embedded risk. We formulate the problem as a mixed-integer linear model programmed in Excel Solver linear to solve smaller optimization problems. Then, we create a Tabu Search algorithm that solves problems of any size. Excel Solver considers three small-scale supply chain networks of varying sizes, one of which maximizes the decision variables the software can handle. In comparison, the Tabu Search program, programmed in Python, solves an additional ten larger-scaled supply chain networks. Tabu Search’s capabilities illustrate its scalability and replicability. A quadratic multi-regression analysis interprets the input parameters (iterations, neighbors, and tabu list size) associated with total supply chain cost and run time. The analysis shows iterations and neighbors to minimize total supply chain cost, while the interaction between iterations x neighbors increases the run time exponentially. Therefore, increasing the number of iterations and neighbors will increase run time but provide a more optimal result for total supply chain cost. Tabu Search’s input parameters should be set high in almost every practical case to achieve the most optimal result. This work is the first to incorporate risk and outsourcing into a multi-echelon supply chain, solved using an exact (Excel Solver) and metaheuristic (Tabu Search) solution methodology. From a practical case, managers can visualize supply chain networks of any size and variation to estimate the total supply chain cost in a relatively short time. Supply chain managers can identify suppliers and pick specific suppliers based on cost or risk. Lastly, they can adjust for risk according to external or internal risk factors. Future research directions include expanding or simplifying the supply chain network design, considering multiple parts, and considering scrap or defective products. In addition, one could incorporate a multi-product dynamic planning horizon supply chain. Overall, considering a hybrid method combining Tabu Search with genetic algorithms, particle swarm optimization, simulated annealing, CPLEX, GUROBI, or LINGO, could provide better results in a faster computational time.

<p>Efficient global dynamic optimization (GDO) using spatial branch-and-bound (SBB) requires the ability to construct tight bounds for the dynamic model. This thesis works toward efficient GDO by developing effective convex relaxation techniques for models with ordinary differential equations (ODEs). In particular, a novel algorithm, based upon a verified interval ODE method and the McCormick relaxation technique, is developed for constructing convex and concave relaxations of solutions of nonlinear parametric ODEs. In addition to better convergence properties, the relaxations so obtained are guaranteed to be no looser than their underlying interval bounds, and are typically tighter in practice. Moreover, they are rigorous in the sense of accounting for truncation errors. Nonetheless, the tightness of the relaxations is affected by the overestimation from the dependency problem of interval arithmetic that is not addressed systematically in the underlying interval ODE method. To handle this issue, the relaxation algorithm is extended to a Taylor model ODE method, which can provide generally tighter enclosures with better convergence properties than the interval ODE method. This way, an improved version of the algorithm is achieved where the relaxations are generally tighter than those computed with the interval ODE method, and offer better convergence. Moreover, they are guaranteed to be no looser than the interval bounds obtained from Taylor models, and are usually tighter in practice. However, the nonlinearity and (potentially) nonsmoothness of the relaxations impedes their fast and reliable solution. Therefore, the algorithm is finally modified by incorporating polyhedral relaxations in order to generate relatively tight and computationally cheap linear relaxations for the dynamic model. The resulting relaxation algorithm along with a SBB procedure is implemented in the MC++ software package. GDO utilizing the proposed relaxation algorithm is demonstrated to have significantly reduced computational expense, up to orders of magnitude, compared to existing GDO methods.</p> / Doctor of Philosophy (PhD)

HACCP-based food safety programs have been widely acclaimed, accepted and implemented as an effective means of managing food safety risks. While HACCP training is a cornerstone of managing HACCP programs, there is little information about the effectiveness of HACCP training and the durability of HACCP knowledge. Findings reveal a link between involvement level in HACCP activities and the accuracy of HACCP knowledge over time. Opportunities for peer training in HACCP, irrespective of overall experience in the food industry provide favorable circumstances for maintaining accuracy of HACCP knowledge. The optimal window for engaging employees in HACCP is directly following the completion of training for achieving the minimal depletion level of content knowledge. This study further reveals that refresher training in HACCP is necessary within three years. Furthermore, training standardization organizations likely need a formal process of monitoring and maintaining HACCP trainer and trainee qualifications to ensure uniformity in HACCP programming.

Le souhait d’augmenter la part des énergies renouvelables dans le mix énergétique entraine une augmentation des parts des énergies volatiles et non pilotables, et rend donc l’équilibre offre-demande difficile à satisfaire. Une façon d’intégrer ces énergies dans le réseau électrique actuel est d’utiliser de petits moyens de production, de consommation et de stockage répartis sur tout le territoire pour compenser les sous ou sur productions. Afin que ces procédés puissent être intégrés dans le processus d’équilibre offre-demande, ils sont regroupés au sein d’une centrale virtuelle d’agrégation de flexibilité, qui est vue alors comme une centrale virtuelle. Comme pour tout autre moyen de production du réseau, il est nécessaire de déterminer son plan de production. Nous proposons dans un premier temps dans cette thèse une architecture et un mode de gestion pour une centrale d’agrégation composée de n’importe quel type de procédés. Dans un second temps, nous présentons des algorithmes permettant de calculer le plan de production des différents types de procédés respectant toutes leurs contraintes de fonctionnement. Et enfin, nous proposons des approches pour calculer le plan de production de la centrale d’agrégation dans le but de maximiser son gain financier en respectant les contraintes réseau. / The desire to increase the share of renewable energies in the energy mix leads to an increase inshare of volatile and non-controllable energy and makes it difficult to meet the supply-demand balance. A solution to manage anyway theses energies in the current electrical grid is to deploy new energy storage and demand response systems across the country to counter balance under or over production. In order to integrate all these energies systems to the supply and demand balance process, there are gathered together within a virtual flexibility aggregation power plant which is then seen as a virtual power plant. As for any other power plant, it is necessary to compute its production plan. Firstly, we propose in this PhD thesis an architecture and management method for an aggregation power plant composed of any type of energies systems. Then, we propose algorithms to compute the production plan of any types of energy systems satisfying all theirs constraints. Finally, we propose an approach to compute the production plan of the aggregation power plant in order to maximize its financial profit while complying with all the constraints of the grid.

Made available in DSpace on 2016-12-23T14:05:52Z (GMT). No. of bitstreams: 1 Dissertacao_ARLISS_2006.pdf: 6503141 bytes, checksum: 35cb76273269987a726a1cb13c65f385 (MD5) Previous issue date: 2006-12-12 / Este trabalho buscou desenvolver um modelo matemático e um procedimento metodológico que contribuam para o estudo de localização de indústrias de reciclagem na cadeia logística reversa do coco verde. Os resíduos do coco verde jogados nos lixões ou espalhados pelas praias causam um grande impacto ao meio ambiente. Estes resíduos podem ganhar um novo destino, mais qualificado e rentável, a partir da transformação de sua casca em fibra e pó, e da fabricação de produtos derivados destes dois componentes, criando ao mesmo tempo condições para gerar emprego e renda. É de grande importância o estudo de localização, tema abordado no presente estudo usando os seguintes passos metodológicos: caracterização da cadeia logística reversa do coco verde; seleção dos locais candidatos à instalação das indústrias de reciclagem; estimativa das quantidades de resíduos do coco verde gerados, distâncias e custos; modelagem do problema proposto, utilizando-se a programação linear mista e não-linear; e escolha das ferramentas para solução do modelo. Estima-se a quantidade de indústrias de reciclagem da casca do coco verde, suas capacidades e as melhores localizações, com a devida distribuição dos resíduos, que minimizem os custos de instalação, operação, estoque e transporte, utilizando um modelo matemático. Foram criados dez cenários relacionados ao problema proposto para o Estado do Espírito Santo, combinando-se diferentes quantidades de indústrias, capacidades e regiões de instalação, e variando-se os custos envolvidos, a quantidade de resíduos e o tempo de vida das indústrias. O modelo matemático mostrou ser bastante útil como instrumento auxiliar de análise de problemas de localização, tanto pela simplicidade de operação quanto pela aplicação das ferramentas Solver e What´sBest! disponíveis no mercado. Os resultados foram coerentes com o esperado, face à realidade atual do Estado do Espírito Santo no que tange à localização do aterro sanitário existente no município de Cariacica, hoje utilizado como local de destinação final dos resíduos do coco verde gerados na Região da Grande Vitória, sendo o mesmo escolhido nos dez cenários utilizados. / This work aims at developing a mathematical model and a methodological approach for the location of recycling industries of green coconut residuals. The coconut hard cover can cause environmental impacts when discharged along the beaches or in land farming. These residuals can be recycled and transformed into fibres, a qualified and profitable use, that can also generate jobs and revenues. It is of great importance the location study, theme approached in the present study using the following methodological steps: the characterization of the reverse chain logistics of the green coconut; selection of the places that are potential candidates to settle the recycling industries; estimation of the residual amount that is produced, travel distances and costs; development of a model of mixed linear and no linear programming; and choice of the tools for solution of the model. The modeling takes into account the amount of recycling industries, its capacities, the best locations and the corresponding residual distribution, which minimize the costs of installation, operation, stock and transport. Ten scenarios related to the problem were created for Espírito Santo State combining the numbers of industries, capacities and locations, and varying the involved costs, the amount of residues and the life time of the industries. The mathematical model showed to be a quite useful tool for analysis of location problems, due to its operational simplicity as well as the availability in the market of Solver and What´sBest!. As expected, the results agree with the reality of Espírito Santo State concerning the landfill location in the Cariacica district, actual place of destination of the green coconut residuals generated in the Grande Vitória region, being the same chosen in the ten used sceneries.

The primary objective of this research is to formulate a methodology of assessing the maximum impact loading condition that will incur onto an aircraft’s landing gear system via Finite Element Analysis (FEA) and appropriately determining its corresponding structural and impact responses to minimize potential design failures during hard landing (abnormal impact) and shock absorption testing. Both static and dynamic loading condition were closely analyzed, compared, and derived through the Federal Aviation Administration’s (FAA) airworthiness regulations and empirical testing data. In this research, a nonlinear transient dynamic analysis is developed and established via NASTRAN advanced nonlinear finite element model (FEM) to simulate the worst-case loading condition. Under the appropriate loading analysis, the eye-bar and contact patch region theory were then utilized to simulate the tire and nose wheel interface more accurately. The open geometry of the nose landing gear was also optimized to minimize the effect of stress concentration. The result of this research is conformed to the FAA’s regulations and bound to have an impact on the design and development of small and large aircraft’s landing gear for both near and distant future.

Air traffic flow management is a critical component of air transport operations because at some point in time, often very frequently, one of more of the critical resources in the air transportation network has significantly reduced capacity, resulting in congestion and delay for airlines and other entities and individuals who use the network. Typically, these “bottlenecks” are noticed at a given airport or terminal area, but they also occur in en route airspace. The two-stage combinatorial optimization framework for air traffic flow management under constrained capacity that is presented in this thesis, represents a important step towards the full consideration of the combinatorial nature of air traffic flow management decision that is often ignored or dealt with via priority-based schemes. It also illustrates the similarities between two traffic flow management problems that heretofore were considered to be quite distinct. The runway systems at major airports are highly constrained resources. From the perspective of arrivals, unnecessary delays and emissions may occur during peak periods when one or more runways at an airport are in great demand while other runways at the same airport are operating under their capacity. The primary cause of this imbalance in runway utilization is that the traffic flow into and out of the terminal areas is asymmetric (as a result of airline scheduling practices), and arrivals are typically assigned to the runway nearest the fix through which they enter the terminal areas. From the perspective of departures, delays and emissions occur because arrivals take precedence over departures with regard to the utilization of runways (despite the absence of binding safety constraints), and because arrival trajectories often include level segments that ensure “procedural separation” from arriving traffic while planes are not allowed to climb unrestricted along the most direct path to their destination. Similar to the runway systems, the terminal radar approach control facilities (TRACON) boundary fixes are also constrained resources of the terminal airspace. Because some arrival traffic from different airports merges at an arrival fix, a queue for the terminal areas generally starts to form at the arrival fix, which are caused by delays due to heavy arriving traffic streams. The arrivals must then absorb these delays by path stretching and adjusting their speed, resulting in unplanned fuel consumption. However, these delays are often not distributed evenly. As a result, some arrival fixes experience severe delays while, similar to the runway systems, the other arrival fixes might experience no delays at all. The goal of this thesis is to develop a combined optimization approach for terminal airspace flow management that assigns a TRACON boundary fix and a runway to each flight while minimizing the required fuel burn and emissions. The approach lessens the severity of terminal capacity shortage caused by and imbalance of traffic demand by shunting flights from current positions to alternate runways. This is done by considering every possible path combination. To attempt to solve the congestion of the terminal airspace at both runways and arrival fixes, this research focuses on two sequential optimizations. The fix assignments are dealt with by considering, simultaneously, the capacity constraints of fixes and runways as well as the fuel consumption and emissions of each flight. The research also develops runway assignments with runway scheduling such that the total emissions produced in the terminal area and on the airport surface are minimized. The two-stage sequential framework is also extended to en route airspace. When en route airspace loses its capacity for any reason, e.g. severe weather condition, air traffic controllers and flight operators plan flight schedules together based on the given capacity limit, thereby maximizing en route throughput and minimizing flight operators' costs. However, the current methods have limitations due to the lacks of consideration of the combinatorial nature of air traffic flow management decision. One of the initial attempts to overcome these limitations is the Collaborative Trajectory Options Program (CTOP), which will be initiated soon by the Federal Aviation Administration (FAA). The developed two-stage combinatorial optimization framework fits this CTOP perfectly from the flight operator's perspective. The first stage is used to find an optimal slot allocation for flights under satisfying the ration by schedule (RBS) algorithm of the FAA. To solve the formulated first stage problem efficiently, two different solution methodologies, a heuristic algorithm and a modified branch and bound algorithm, are presented. Then, flights are assigned to the resulting optimized slots in the second stage so as to minimize the flight operator's costs.

À travers une approche de contrôle optimal, cette thèse de doctorat propose une étude des modèles et des techniques de résolution dans un domaine d'application propre à la gestion du trafic aérien. Motivés par la croissance des flux aériens d'une part, et les développements en théorie du contrôle optimal d'autre part, ces travaux portent sur l'analyse du problème d'évitement de conflits aériens. Cette étude permet le développement de nouvelles approches et algorithmes en vue d'aider les contrôleurs aériens dans leur tâche. Ainsi, dans le cadre du trafic aérien, afin de préserver des distances minimales de sécurité entre avions, lors de phases tactiques et de configurations des vols en-route, notre recherche se focalise sur une stratégie de régulation subliminale en vitesse (variations très réduites), pour assurer la séparation entre avions, tout en conservant leur trajectoire prédéfinie. D'une part, une méthode de résolution numérique en contrôle optimal telle que la méthode directe de tir, impliquant une discrétisation totale ou partielle du problème, transforme le problème initial en un problème en programmation non linéaire de grande taille. Ce type de méthodes peut générer des problèmes d'optimisation de grande taille numériquement di_ciles à résoudre. Suivant le nombre de variables du problème, elles peuvent s'avérer trop coûteuse en termes de temps de calculs. D'autre part, les contraintes sur les variables d'états du problème posent des di_cultés de résolution, par exemple, pour l'usage d'une méthode numérique indirecte de tir. Développant les informations caractéristiques des conflits aériens, une détection et une détermination a priori des zones de conflits permettent alors la décomposition du problème présenté de contrôle optimal en sous-problèmes plus aisés à résoudre. La résolution des sous-problèmes hors-zones peut être abordée en utilisant les conditions du principe du maximum de Pontryagin, ce qui en permet une résolution e_cace. Une combinaison de méthodes numériques directes de tir et d'application des conditions du principe du maximum de Pontryagin est proposée, et des implémentations numériques valident ce type d'approche. / The purpose of this doctoral thesis is to study models and solution techniques based on optimal control approaches to address air tra_c management problems. Motivated by the growth of air tra_c volume, and by the advances in optimal control theory, this research works focus on analysing aircraft conflict avoidance problem. This study allows development of new approaches and algorithms to help air tra_c controllers. In the framework of air tra_c management, to ensure the minimum safety distances between aircraft, in tactical phases and en-route flight configurations, this thesis focusses on a subliminal velocity regulation strategy to perform the separation, while preserving the aircraft predefined trajectories. A numerical optimal control solution approach as the direct shooting method, wherein involves a total or partial discretization of the problem, transforms the initial problem into a large scale nonlinear programming problem. This kind of methods could generate large-size optimization problems which are numerically di_cult to solve. Depending on the number of variables which involved, this approaches could be too expensive in terms of computation time. Moreover, the state-variables constraints of the problem lead to numerical di_culties, e.g., considering the indirect numerical shooting method. Tailored on aircraft conflict avoidance problems, a detection and a determination of a priori conflict zones allow the decomposition of the optimal control problem into sub-problems, easier to solve than the original one. Solving the o_-zones sub-problems can be addressed using the Pontryagin maximum principle, which allows in this case directly the solution. A combination of direct numerical shooting method and application of conditions of Pontryagin's maximum principle is proposed, and numerical experiments validate this approach.