Supportive and palliative care for lung cancer patients

Lung cancer patients face poor survival and experience co-occurring chronic physical and psychological symptoms. These symptoms can result in significant burden, impaired physical and social function and poor quality of life. This paper provides a review of evidence based interventions that support best practice supportive and palliative care for patients with lung cancer. Specifically, interventions to manage dyspnoea, one of the most common symptoms experienced by this group, are discussed to illustrate the emerging evidence base in the field. The evidence base for the pharmacological management of dyspnoea report systemic opioids have the best available evidence to support their use. In particular, the evidence strongly supports systemic morphine preferably initiated and continued as a once daily sustained release preparation. Evidence supporting the use of a range of other adjunctive non-pharmacological interventions in managing the symptom is also emerging. Interventions to improve breathing efficiency that have been reported to be effective include pursed lip breathing, diaphragmatic breathing, positioning and pacing techniques. Psychosocial interventions seeking to reduce anxiety and distress can also improve the management of breathlessness although further studies are needed. In addition, evidence reviews have concluded that case management approaches and nurse led follow-up programs are effective in reducing breathlessness and psychological distress, providing a useful model for supporting implementation of evidence based symptom management strategies. Optimal outcomes from supportive and palliative care interventions thus require a multilevel approach, involving interventions at the patient, health professional and health service level.

A queuing approach for inventory planning with batch ordering in multi-echelon supply chains

This paper presents stylized models for conducting performance analysis of the manufacturing supply chain network (SCN) in a stochastic setting for batch ordering. We use queueing models to capture the behavior of SCN. The analysis is clubbed with an inventory optimization model, which can be used for designing inventory policies . In the first case, we model one manufacturer with one warehouse, which supplies to various retailers. We determine the optimal inventory level at the warehouse that minimizes total expected cost of carrying inventory, back order cost associated with serving orders in the backlog queue, and ordering cost. In the second model we impose service level constraint in terms of fill rate (probability an order is filled from stock at warehouse), assuming that customers do not balk from the system. We present several numerical examples to illustrate the model and to illustrate its various features. In the third case, we extend the model to a three-echelon inventory model which explicitly considers the logistics process.

China, film coproduction and soft power competition

This thesis investigates China's film internationalism and coproduction strategy based on three cases: Hong Kong and China film coproduction; US and China without any state-level agreements; Australia and China based on an official coproduction treaty. It investigates the evolution of coproduction in the film industry, the process of coproduction, foreign film companies' strategies of adjustment to state policies, and the culture and complexities that hinder coproduction. It surveys the current environment for China film coproduction and investigates the degree to which film coproduction has been - to this stage - a contributor to China's global cultural presence – its soft power.

Inventory Routing - A Strategic Management Accounting Perspective

This dissertation develops a strategic management accounting perspective of inventory routing. The thesis studies the drivers of cost efficiency gains by identifying the role of the underlying cost structure, demand, information sharing, forecasting accuracy, service levels, vehicle fleet, planning horizon and other strategic factors as well as the interaction effects among these factors with respect to performance outcomes. The task is to enhance the knowledge of the strategic situations that favor the implementation of inventory routing systems, understanding cause-and-effect relationships, linkages and gaining a holistic view of the value proposition of inventory routing. The thesis applies an exploratory case study design, which is based on normative quantitative empirical research using optimization, simulation and factor analysis. Data and results are drawn from a real world application to cash supply chains. The first research paper shows that performance gains require a common cost component and cannot be explained by simple linear or affine cost structures. Inventory management and distribution decisions become separable in the absence of a set-dependent cost structure, and neither economies of scope nor coordination problems are present in this case. The second research paper analyzes whether information sharing improves the overall forecasting accuracy. Analysis suggests that the potential for information sharing is limited to coordination of replenishments and that central information do not yield more accurate forecasts based on joint forecasting. The third research paper develops a novel formulation of the stochastic inventory routing model that accounts for minimal service levels and forecasting accuracy. The developed model allows studying the interaction of minimal service levels and forecasting accuracy with the underlying cost structure in inventory routing. Interestingly, results show that the factors minimal service level and forecasting accuracy are not statistically significant, and subsequently not relevant for the strategic decision problem to introduce inventory routing, or in other words, to effectively internalize inventory management and distribution decisions at the supplier. Consequently the main contribution of this thesis is the result that cost benefits of inventory routing are derived from the joint decision model that accounts for the underlying set-dependent cost structure rather than the level of information sharing. This result suggests that the value of information sharing of demand and inventory data is likely to be overstated in prior literature. In other words, cost benefits of inventory routing are primarily determined by the cost structure (i.e. level of fixed costs and transportation costs) rather than the level of information sharing, joint forecasting, forecasting accuracy or service levels.

Resource Usage Monitoring in Clouds

Monitoring of infrastructural resources in clouds plays a crucial role in providing application guarantees like performance, availability, and security. Monitoring is crucial from two perspectives - the cloud-user and the service provider. The cloud user’s interest is in doing an analysis to arrive at appropriate Service-level agreement (SLA) demands and the cloud provider’s interest is to assess if the demand can be met. To support this, a monitoring framework is necessary particularly since cloud hosts are subject to varying load conditions. To illustrate the importance of such a framework, we choose the example of performance being the Quality of Service (QoS) requirement and show how inappropriate provisioning of resources may lead to unexpected performance bottlenecks. We evaluate existing monitoring frameworks to bring out the motivation for building much more powerful monitoring frameworks. We then propose a distributed monitoring framework, which enables fine grained monitoring for applications and demonstrate with a prototype system implementation for typical use cases.

Elastic Resources Framework in IaaS, preserving performance SLAs

Elasticity in cloud systems provides the flexibility to acquire and relinquish computing resources on demand. However, in current virtualized systems resource allocation is mostly static. Resources are allocated during VM instantiation and any change in workload leading to significant increase or decrease in resources is handled by VM migration. Hence, cloud users tend to characterize their workloads at a coarse grained level which potentially leads to under-utilized VM resources or under performing application. A more flexible and adaptive resource allocation mechanism would benefit variable workloads, such as those characterized by web servers. In this paper, we present an elastic resources framework for IaaS cloud layer that addresses this need. The framework provisions for application workload forecasting engine, that predicts at run-time the expected demand, which is input to the resource manager to modulate resource allocation based on the predicted demand. Based on the prediction errors, resources can be over-allocated or under-allocated as compared to the actual demand made by the application. Over-allocation leads to unused resources and under allocation could cause under performance. To strike a good trade-off between over-allocation and under-performance we derive an excess cost model. In this model excess resources allocated are captured as over-allocation cost and under-allocation is captured as a penalty cost for violating application service level agreement (SLA). Confidence interval for predicted workload is used to minimize this excess cost with minimal effect on SLA violations. An example case-study for an academic institute web server workload is presented. Using the confidence interval to minimize excess cost, we achieve significant reduction in resource allocation requirement while restricting application SLA violations to below 2-3%.

Real-Time and Data-Driven Operation Optimization and Knowledge Discovery for an Enterprise Information System

An enterprise information system (EIS) is an integrated data-applications platform characterized by diverse, heterogeneous, and distributed data sources. For many enterprises, a number of business processes still depend heavily on static rule-based methods and extensive human expertise. Enterprises are faced with the need for optimizing operation scheduling, improving resource utilization, discovering useful knowledge, and making data-driven decisions.

This thesis research is focused on real-time optimization and knowledge discovery that addresses workflow optimization, resource allocation, as well as data-driven predictions of process-execution times, order fulfillment, and enterprise service-level performance. In contrast to prior work on data analytics techniques for enterprise performance optimization, the emphasis here is on realizing scalable and real-time enterprise intelligence based on a combination of heterogeneous system simulation, combinatorial optimization, machine-learning algorithms, and statistical methods.

On-demand digital-print service is a representative enterprise requiring a powerful EIS.We use real-life data from Reischling Press, Inc. (RPI), a digit-print-service provider (PSP), to evaluate our optimization algorithms.

In order to handle the increase in volume and diversity of demands, we first present a high-performance, scalable, and real-time production scheduling algorithm for production automation based on an incremental genetic algorithm (IGA). The objective of this algorithm is to optimize the order dispatching sequence and balance resource utilization. Compared to prior work, this solution is scalable for a high volume of orders and it provides fast scheduling solutions for orders that require complex fulfillment procedures. Experimental results highlight its potential benefit in reducing production inefficiencies and enhancing the productivity of an enterprise.

We next discuss analysis and prediction of different attributes involved in hierarchical components of an enterprise. We start from a study of the fundamental processes related to real-time prediction. Our process-execution time and process status prediction models integrate statistical methods with machine-learning algorithms. In addition to improved prediction accuracy compared to stand-alone machine-learning algorithms, it also performs a probabilistic estimation of the predicted status. An order generally consists of multiple series and parallel processes. We next introduce an order-fulfillment prediction model that combines advantages of multiple classification models by incorporating flexible decision-integration mechanisms. Experimental results show that adopting due dates recommended by the model can significantly reduce enterprise late-delivery ratio. Finally, we investigate service-level attributes that reflect the overall performance of an enterprise. We analyze and decompose time-series data into different components according to their hierarchical periodic nature, perform correlation analysis,

and develop univariate prediction models for each component as well as multivariate models for correlated components. Predictions for the original time series are aggregated from the predictions of its components. In addition to a significant increase in mid-term prediction accuracy, this distributed modeling strategy also improves short-term time-series prediction accuracy.

In summary, this thesis research has led to a set of characterization, optimization, and prediction tools for an EIS to derive insightful knowledge from data and use them as guidance for production management. It is expected to provide solutions for enterprises to increase reconfigurability, accomplish more automated procedures, and obtain data-driven recommendations or effective decisions.

SLA-driven flexible bandwidth reservation negotiation schemes for QoS aware IP networks

We present a generic Service Level Agreement (SLA)-driven service provisioning architecture, which enables dynamic and flexible bandwidth reservation schemes on a per-user or a per-application basis. Various session level SLA negotiation schemes involving bandwidth allocation, service start time and service duration parameters are introduced and analysed. The results show that these negotiation schemes can be utilised for the benefits of both end user and network provide such as getting the highest individual SLA optimisation in terms of Quality of Service (QoS) and price. A prototype based on an industrial agent platform has also been built to demonstrate the negotiation scenario and this is presented and discussed.

The role of operations management in public sector policy and practice alignment: a local government analysis

In responding to the demand for change and improvement, local government has applied a plethora of operations management-based methods, tools and techniques. This article explores how these methods, specifically in the form of performance management models, are used to improve alignment between central government policy and local government practice, an area which has thus far been neglected in the literature. Using multiple case studies from Environmental Waste Management Services, this research reports that models derived in the private sector are often directly ‘implanted’ into the public sector. This has challenged the efficacy of all performance management models. However, those organisations which used models most effectively did so by embedding (contextualisation) and extending (reconceptualisation) them beyond their original scope. Moreover, success with these models created a cumulative effect whereby other operations management approaches were probed, adapted and used.

Time-lapse monitoring of climate effects on earthworks using surface waves

The UK’s transportation network is supported by critical geotechnical assets (cuttings/embankments/dams) that require sustainable, cost-effective management, while maintaining an appropriate service level to meet social, economic, and environmental needs. Recent effects of extreme weather on these geotechnical assets have highlighted their vulnerability to climate variations. We have assessed the potential of surface wave data to portray the climate-related variations in mechanical properties of a clay-filled railway embankment. Seismic data were acquired bimonthly from July 2013 to November 2014 along the crest of a heritage railway embankment in southwest England. For each acquisition, the collected data were first processed to obtain a set of Rayleigh-wave dispersion and attenuation curves, referenced to the same spatial locations. These data were then analyzed to identify a coherent trend in their spatial and temporal variability. The relevance of the observed temporal variations was also verified with respect to the experimental data uncertainties. Finally, the surface wave dispersion data sets were inverted to reconstruct a time-lapse model of S-wave velocity for the embankment structure, using a least-squares laterally constrained inversion scheme. A key point of the inversion process was constituted by the estimation of a suitable initial model and the selection of adequate levels of spatial regularization. The initial model and the strength of spatial smoothing were then kept constant throughout the processing of all available data sets to ensure homogeneity of the procedure and comparability among the obtained VS sections. A continuous and coherent temporal pattern of surface wave data, and consequently of the reconstructed VS models, was identified. This pattern is related to the seasonal distribution of precipitation and soil water content measured on site.

Essays on supply chain's flexibility: a real options approach

A presente tese investiga o processo de tomada de decisão na gestão de cadeias de abastecimento, utilizando um quadro de análise de opções reais. Especificamente, estudamos tópicos como o nível de inventário ideal para protecção contra a incerteza da procura, o momento para implementação de capacidade flexível em mercados onde existe complexidade no mix de produtos, o tempo para o reforço do factor trabalho visando requisitos de serviço ao mercado, e as decisões entre integração e outsourcing num ambiente de incerteza. Foram usadas metodologias de tempo discreto e contínuo para identificar o valor ideal e o calendário das opções a adoptar, quando a procura é estocástica. Além disso, foram considerados os efeitos dos requisitos dos mercados, como a complexidade na oferta de produtos e o nível de serviço. A procura é representada recorrendo a diferentes processos estocásticos, o impacto de saltos inesperados também é explorado, reforçando a generalização dos modelos a diferentes condições de negócio. A aplicabilidade dos modelos que apresentamos permite a diversificação e o enriquecimento da literatura sobre a abordagem de opções reais, no âmbito das cadeias de abastecimento. Níveis de inventário flexíveis e capacidades flexíveis são característicos das cadeias de abastecimento e podem ser usados como resposta à incerteza do mercado. Esta tese é constituída por ensaios que suportam a aplicação dos modelos, e consiste num capítulo introdutório (designado por ensaio I) e mais seis ensaios sobre factores que discutem o uso de medidas de flexibilidade nas cadeias de abastecimento, em ambientes de incerteza, e um último ensaio sobre a extensão do conceito de flexibilidade ao tratamento da avaliação de planos de negócio. O segundo ensaio que apresentamos é sobre o valor do inventário num único estádio, enquanto medida de flexibilidade, sujeita ao crescente condicionalismo dos custos com posse de activos. Introduzimos uma nova classificação de artigos para suportar o indicador designado por overstock. No terceiro e quarto ensaio ampliamos a exploração do conceito de overstock, promovendo a interacção e o balanceamento entre vários estádios de uma cadeia de abastecimento, como forma de melhorar o desempenho global. Para sustentar a aplicação prática das abordagens, adaptamos o ensaio número três à gestão do desempenho, para suportar o estabelecimento de metas coordenadas e alinhadas; e adaptamos o quarto ensaio à coordenação das cadeias de abastecimento, como auxiliar ao planeamento integrado e sequencial dos níveis de inventário. No ensaio cinco analisamos o factor de produção “tecnologia”, em relação directa com a oferta de produtos de uma empresa, explorando o conceito de investimento, como medida de flexibilidade nas componentes de volume da procura e gama de produtos. Dedicamos o ensaio número seis à análise do factor de produção “Mão-de-Obra”, explorando as condicionantes para aumento do número de turnos na perspectiva económica e determinando o ponto crítico para a tomada de decisão em ambientes de incerteza. No ensaio número sete exploramos o conceito de internalização de operações, demarcando a nossa análise das demais pela definição do momento crítico que suporta a tomada de decisão em ambientes dinâmicos. Complementamos a análise com a introdução de factores temporais de perturbação, nomeadamente, o estádio de preparação necessário e anterior a uma eventual alteração de estratégia. Finalmente, no último ensaio, estendemos a análise da flexibilidade em ambientes de incerteza ao conceito de planos de negócio. Em concreto, exploramos a influência do número de pontos de decisão na flexibilidade de um plano, como resposta à crescente incerteza dos mercados. A título de exemplo, usamos o mecanismo de gestão sequencial do orçamento para suportar o nosso modelo. A crescente incerteza da procura obrigou a um aumento da agilidade e da flexibilidade das cadeias de abastecimento, limitando o uso de muitas das técnicas tradicionais de suporte à gestão, pela incapacidade de incorporarem os efeitos da incerteza. A flexibilidade é claramente uma vantagem competitiva das empresas que deve, por isso, ser quantificada. Com os modelos apresentados e com base nos resultados analisados, pretendemos demonstrar a utilidade da consideração da incerteza nos instrumentos de gestão, usando exemplos numéricos para suportar a aplicação dos modelos, o que claramente promove a aproximação dos desenvolvimentos aqui apresentados às práticas de negócio.

Otimização da atividade de picking num armazém de peças – um caso de estudo

Mestrado em Engenharia Mecânica – Gestão Industrial

Desenvolvimento de um modelo para a gestão de stocks no grupo Nors

Hoje em dia, um dos grandes objetivos das empresas é conseguirem uma gestão eficiente. Em particular, empresas que lidam com grandes volumes de stocks têm a necessidade de otimizar as quantidades dos seus produtos armazenados, com o objetivo, de entre outros, reduzir os seus custos associados. O trabalho documentado descreve um novo modelo, desenvolvido para a gestão de encomendas de uma empresa líder em soluções de transporte. A eficiência do modelo foi alcançada com a utilização de vários métodos matemáticos de previsão. Salientam-se os métodos de Croston, Teunter e de Syntetos e Boylan adequados para artigos com procuras intermitentes e a utilização de métodos mais tradicionais, tais como médias móveis ou alisamento exponencial. Os conceitos de lead time, stock de segurança, ponto de encomenda e quantidade económica a encomendar foram explorados e serviram de suporte ao modelo desenvolvido. O stock de segurança recebeu especial atenção. Foi estabelecida uma nova fórmula de cálculo em conformidade com as necessidades reais da empresa. A eficiência do modelo foi testada com o acompanhamento da evolução do stock real. Para além de uma redução significativa do valor dos stocks armazenados, a viabilidade do modelo é reflectida pelo nível de serviço alcançado.

Reframing reverse logistics, reducing costs

A Work Project, presented as part of the requirements for the Award of a Masters Degree in Management from the NOVA – School of Business and Economics