Hierarchical Forest Management Planning: A Bilevel Wood Supply Modelling Approach

Le processus de planification forestière hiérarchique présentement en place sur les terres publiques risque d’échouer à deux niveaux. Au niveau supérieur, le processus en place ne fournit pas une preuve suffisante de la durabilité du niveau de récolte actuel. À un niveau inférieur, le processus en place n’appuie pas la réalisation du plein potentiel de création de valeur de la ressource forestière, contraignant parfois inutilement la planification à court terme de la récolte. Ces échecs sont attribuables à certaines hypothèses implicites au modèle d’optimisation de la possibilité forestière, ce qui pourrait expliquer pourquoi ce problème n’est pas bien documenté dans la littérature. Nous utilisons la théorie de l’agence pour modéliser le processus de planification forestière hiérarchique sur les terres publiques. Nous développons un cadre de simulation itératif en deux étapes pour estimer l’effet à long terme de l’interaction entre l’État et le consommateur de fibre, nous permettant ainsi d’établir certaines conditions pouvant mener à des ruptures de stock. Nous proposons ensuite une formulation améliorée du modèle d’optimisation de la possibilité forestière. La formulation classique du modèle d’optimisation de la possibilité forestière (c.-à-d., maximisation du rendement soutenu en fibre) ne considère pas que le consommateur de fibre industriel souhaite maximiser son profit, mais suppose plutôt la consommation totale de l’offre de fibre à chaque période, peu importe le potentiel de création de valeur de celle-ci. Nous étendons la formulation classique du modèle d’optimisation de la possibilité forestière afin de permettre l’anticipation du comportement du consommateur de fibre, augmentant ainsi la probabilité que l’offre de fibre soit entièrement consommée, rétablissant ainsi la validité de l’hypothèse de consommation totale de l’offre de fibre implicite au modèle d’optimisation. Nous modélisons la relation principal-agent entre le gouvernement et l’industrie à l’aide d’une formulation biniveau du modèle optimisation, où le niveau supérieur représente le processus de détermination de la possibilité forestière (responsabilité du gouvernement), et le niveau inférieur représente le processus de consommation de la fibre (responsabilité de l’industrie). Nous montrons que la formulation biniveau peux atténuer le risque de ruptures de stock, améliorant ainsi la crédibilité du processus de planification forestière hiérarchique. Ensemble, le modèle biniveau d’optimisation de la possibilité forestière et la méthodologie que nous avons développée pour résoudre celui-ci à l’optimalité, représentent une alternative aux méthodes actuellement utilisées. Notre modèle biniveau et le cadre de simulation itérative représentent un pas vers l’avant en matière de technologie de planification forestière axée sur la création de valeur. L’intégration explicite d’objectifs et de contraintes industrielles au processus de planification forestière, dès la détermination de la possibilité forestière, devrait favoriser une collaboration accrue entre les instances gouvernementales et industrielles, permettant ainsi d’exploiter le plein potentiel de création de valeur de la ressource forestière.

Trajectory prediction uncertainty modelling for Air Traffic Management

The anticipated growth of air traffic worldwide requires enhanced Air Traffic Management (ATM) technologies and procedures to increase the system capacity, efficiency, and resilience, while reducing environmental impact and maintaining operational safety. To deal with these challenges, new automation and information exchange capabilities are being developed through different modernisation initiatives toward a new global operational concept called Trajectory Based Operations (TBO), in which aircraft trajectory information becomes the cornerstone of advanced ATM applications. This transformation will lead to higher levels of system complexity requiring enhanced Decision Support Tools (DST) to aid humans in the decision making processes. These will rely on accurate predicted aircraft trajectories, provided by advanced Trajectory Predictors (TP). The trajectory prediction process is subject to stochastic effects that introduce uncertainty into the predictions. Regardless of the assumptions that define the aircraft motion model underpinning the TP, deviations between predicted and actual trajectories are unavoidable. This thesis proposes an innovative method to characterise the uncertainty associated with a trajectory prediction based on the mathematical theory of Polynomial Chaos Expansions (PCE). Assuming univariate PCEs of the trajectory prediction inputs, the method describes how to generate multivariate PCEs of the prediction outputs that quantify their associated uncertainty. Arbitrary PCE (aPCE) was chosen because it allows a higher degree of flexibility to model input uncertainty. The obtained polynomial description can be used in subsequent prediction sensitivity analyses thanks to the relationship between polynomial coefficients and Sobol indices. The Sobol indices enable ranking the input parameters according to their influence on trajectory prediction uncertainty. The applicability of the aPCE-based uncertainty quantification detailed herein is analysed through a study case. This study case represents a typical aircraft trajectory prediction problem in ATM, in which uncertain parameters regarding aircraft performance, aircraft intent description, weather forecast, and initial conditions are considered simultaneously. Numerical results are compared to those obtained from a Monte Carlo simulation, demonstrating the advantages of the proposed method. The thesis includes two examples of DSTs (Demand and Capacity Balancing tool, and Arrival Manager) to illustrate the potential benefits of exploiting the proposed uncertainty quantification method.

Modelling and simulating retail management practices: a first approach

Multi-agent systems offer a new and exciting way of understanding the world of work. We apply agent-based modeling and simulation to investigate a set of problems in a retail context. Specifically, we are working to understand the relationship between people management practices on the shop-floor and retail performance. Despite the fact we are working within a relatively novel and complex domain, it is clear that using an agent-based approach offers great potential for improving organizational capabilities in the future. Our multi-disciplinary research team has worked closely with one of the UK’s top ten retailers to collect data and build an understanding of shop-floor operations and the key actors in a department (customers, staff, and managers). Based on this case study we have built and tested our first version of a retail branch agent-based simulation model where we have focused on how we can simulate the effects of people management practices on customer satisfaction and sales. In our experiments we have looked at employee development and cashier empowerment as two examples of shop floor management practices. In this paper we describe the underlying conceptual ideas and the features of our simulation model. We present a selection of experiments we have conducted in order to validate our simulation model and to show its potential for answering “what-if” questions in a retail context. We also introduce a novel performance measure which we have created to quantify customers’ satisfaction with service, based on their individual shopping experiences.

Modeling control objectives for business process compliance

Business process design is primarily driven by process improvement objectives. However, the role of control objectives stemming from regulations and standards is becoming increasingly important for businesses in light of recent events that led to some of the largest scandals in corporate history. As organizations strive to meet compliance agendas, there is an evident need to provide systematic approaches that assist in the understanding of the interplay between (often conflicting) business and control objectives during business process design. In this paper, our objective is twofold. We will firstly present a research agenda in the space of business process compliance, identifying major technical and organizational challenges. We then tackle a part of the overall problem space, which deals with the effective modeling of control objectives and subsequently their propagation onto business process models. Control objective modeling is proposed through a specialized modal logic based on normative systems theory, and the visualization of control objectives on business process models is achieved procedurally. The proposed approach is demonstrated in the context of a purchase-to-pay scenario.

Progress in integrated assessment and modelling

Environmental processes have been modelled for decades. However. the need for integrated assessment and modeling (IAM) has,town as the extent and severity of environmental problems in the 21st Century worsens. The scale of IAM is not restricted to the global level as in climate change models, but includes local and regional models of environmental problems. This paper discusses various definitions of IAM and identifies five different types of integration that Lire needed for the effective solution of environmental problems. The future is then depicted in the form of two brief scenarios: one optimistic and one pessimistic. The current state of IAM is then briefly reviewed. The issues of complexity and validation in IAM are recognised as more complex than in traditional disciplinary approaches. Communication is identified as a central issue both internally among team members and externally with decision-makers. stakeholders and other scientists. Finally it is concluded that the process of integrated assessment and modelling is considered as important as the product for any particular project. By learning to work together and recognise the contribution of all team members and participants, it is believed that we will have a strong scientific and social basis to address the environmental problems of the 21st Century. (C) 2002 Elsevier Science Ltd. All rights reserved.

Modelling text file evaluation processes

Text file evaluation is an emergent topic in e-learning that responds to the shortcomings of the assessment based on questions with predefined answers. Questions with predefined answers are formalized in languages such as IMS Question & Test Interoperability Specification (QTI) and supported by many e-learning systems. Complex evaluation domains justify the development of specialized evaluators that participate in several business processes. The goal of this paper is to formalize the concept of a text file evaluation in the scope of the E-Framework – a service oriented framework for development of e-learning systems maintained by a community of practice. The contribution includes an abstract service type and a service usage model. The former describes the generic capabilities of a text file evaluation service. The later is a business process involving a set of services such as repositories of learning objects and learning management systems.

Customer Lifetime Value: a framework for application in the insurance industry - Building a Business Process to Generate and Maintain an Automatic Estimation Agent

Research Project submited as partial fulfilment for the Master Degree in Statistics and Information Management

Modelling lean and green supply chain

Dissertação para obtenção do Grau de Doutor em Engenharia Industrial

Conceptual Foundations of Modelling of Innovative Production Projects

At the moment there is a lack of methodological approaches to formalization of management of innovative projects relating to production systems, as well as to adaptation and practical use of the existing approaches. This article is about one potential approach to the management of innovative projects, which makes the building of innovative process models possible based on objective approach. It outlines the frameworks for the building of innovative project models, and describes the method of transition from conceptual modelling to innovative project management. In this case, the model alone and together with parameters used for evaluation of the project may be unique and depends on the special features of the project, preferences of decision-making person, and production and economic system in which it is to be implemented. Unlike existing approaches, this concept does not place any restrictions on types of models and makes it possible to take into account the specificities of economic and production systems. Principles embodied in the model allow its usage as a basis for simulation model to be used in one of specialized simulation systems, as well as for information system providing information support of decision-making process in production and economic systems both newly developed by the company (enterprise) and designed on the basis of available information systems that interact through the exchange of data. In addition, this article shows that the development of conceptual foundations of innovative project management in the economic and production systems is inseparable from the development of the theory of industrial control systems, and their comprehensive study may be reduced to a set of elements represented as certain algorithms, models and evaluations. Thus, the study of innovative process may be conducted in both directions: from general to particular, and vice versa.

Evaluating sampling strategies and logistic regression methods for modelling complex land cover changes

The role of land cover change as a significant component of global change has become increasingly recognized in recent decades. Large databases measuring land cover change, and the data which can potentially be used to explain the observed changes, are also becoming more commonly available. When developing statistical models to investigate observed changes, it is important to be aware that the chosen sampling strategy and modelling techniques can influence results. We present a comparison of three sampling strategies and two forms of grouped logistic regression models (multinomial and ordinal) in the investigation of patterns of successional change after agricultural land abandonment in Switzerland. Results indicated that both ordinal and nominal transitional change occurs in the landscape and that the use of different sampling regimes and modelling techniques as investigative tools yield different results. Synthesis and applications. Our multimodel inference identified successfully a set of consistently selected indicators of land cover change, which can be used to predict further change, including annual average temperature, the number of already overgrown neighbouring areas of land and distance to historically destructive avalanche sites. This allows for more reliable decision making and planning with respect to landscape management. Although both model approaches gave similar results, ordinal regression yielded more parsimonious models that identified the important predictors of land cover change more efficiently. Thus, this approach is favourable where land cover change pattern can be interpreted as an ordinal process. Otherwise, multinomial logistic regression is a viable alternative.

Driver scheduling problem modelling

The Drivers Scheduling Problem (DSP) consists of selecting a set of duties for vehicle drivers, for example buses, trains, plane or boat drivers or pilots, for the transportation of passengers or goods. This is a complex problem because it involves several constraints related to labour and company rules and can also present different evaluation criteria and objectives. Being able to develop an adequate model for this problem that can represent the real problem as close as possible is an important research area.The main objective of this research work is to present new mathematical models to the DSP problem that represent all the complexity of the drivers scheduling problem, and also demonstrate that the solutions of these models can be easily implemented in real situations. This issue has been recognized by several authors and as important problem in Public Transportation. The most well-known and general formulation for the DSP is a Set Partition/Set Covering Model (SPP/SCP). However, to a large extend these models simplify some of the specific business aspects and issues of real problems. This makes it difficult to use these models as automatic planning systems because the schedules obtained must be modified manually to be implemented in real situations. Based on extensive passenger transportation experience in bus companies in Portugal, we propose new alternative models to formulate the DSP problem. These models are also based on Set Partitioning/Covering Models; however, they take into account the bus operator issues and the perspective opinions and environment of the user.We follow the steps of the Operations Research Methodology which consist of: Identify the Problem; Understand the System; Formulate a Mathematical Model; Verify the Model; Select the Best Alternative; Present the Results of theAnalysis and Implement and Evaluate. All the processes are done with close participation and involvement of the final users from different transportation companies. The planner s opinion and main criticisms are used to improve the proposed model in a continuous enrichment process. The final objective is to have a model that can be incorporated into an information system to be used as an automatic tool to produce driver schedules. Therefore, the criteria for evaluating the models is the capacity to generate real and useful schedules that can be implemented without many manual adjustments or modifications. We have considered the following as measures of the quality of the model: simplicity, solution quality and applicability. We tested the alternative models with a set of real data obtained from several different transportation companies and analyzed the optimal schedules obtained with respect to the applicability of the solution to the real situation. To do this, the schedules were analyzed by the planners to determine their quality and applicability. The main result of this work is the proposition of new mathematical models for the DSP that better represent the realities of the passenger transportation operators and lead to better schedules that can be implemented directly in real situations.

Modelling nitrogen and phosphorus loads in a Mediterranean river catchment (La Tordera, NE Spain)

Human activities have resulted in increased nutrient levels in many rivers all over Europe. Sustainable management of river basins demands an assessment of the causes and consequences of human alteration of nutrient flows, together with an evaluation of management options. In the context of an integrated and interdisciplinary environmental assessment (IEA) of nutrient flows, we present and discuss the application of the nutrient emission model MONERIS (MOdelling Nutrient Emissions into River Systems) to the Catalan river basin, La Tordera (north-east Spain), for the period 1996–2002. After a successful calibration and verification process (Nash-Sutcliffe efficiencies E=0.85 for phosphorus and E=0.86 for nitrogen), the application of the model MONERIS proved to be useful in estimating nutrient loads. Crucial for model calibration, in-stream retention was estimated to be about 50 % of nutrient emissions on an annual basis. Through this process, we identified the importance of point sources for phosphorus emissions (about 94% for 1996–2002), and diffuse sources, especially inputs via groundwater, for nitrogen emissions (about 31% for 1996–2002). Despite hurdles related to model structure, observed loads, and input data encountered during the modelling process, MONERIS provided a good representation of the major interannual and spatial patterns in nutrient emissions. An analysis of the model uncertainty and sensitivity to input data indicates that the model MONERIS, even in data-starved Mediterranean catchments, may be profitably used by water managers for evaluating quantitative nutrient emission scenarios for the purpose of managing river basins. As an example of scenario modelling, an analysis of the changes in nutrient emissions through two different future scenarios allowed the identification of a set of relevant measures to reduce nutrient loads.

Effect of hydrodynamics on modelling, monitoring and control of crystallization

Crystallization is a purification method used to obtain crystalline product of a certain crystal size. It is one of the oldest industrial unit processes and commonly used in modern industry due to its good purification capability from rather impure solutions with reasonably low energy consumption. However, the process is extremely challenging to model and control because it involves inhomogeneous mixing and many simultaneous phenomena such as nucleation, crystal growth and agglomeration. All these phenomena are dependent on supersaturation, i.e. the difference between actual liquid phase concentration and solubility. Homogeneous mass and heat transfer in the crystallizer would greatly simplify modelling and control of crystallization processes, such conditions are, however, not the reality, especially in industrial scale processes. Consequently, the hydrodynamics of crystallizers, i.e. the combination of mixing, feed and product removal flows, and recycling of the suspension, needs to be thoroughly investigated. Understanding of hydrodynamics is important in crystallization, especially inlargerscale equipment where uniform flow conditions are difficult to attain. It is also important to understand different size scales of mixing; micro-, meso- and macromixing. Fast processes, like nucleation and chemical reactions, are typically highly dependent on micro- and mesomixing but macromixing, which equalizes the concentrations of all the species within the entire crystallizer, cannot be disregarded. This study investigates the influence of hydrodynamics on crystallization processes. Modelling of crystallizers with the mixed suspension mixed product removal (MSMPR) theory (ideal mixing), computational fluid dynamics (CFD), and a compartmental multiblock model is compared. The importance of proper verification of CFD and multiblock models is demonstrated. In addition, the influence of different hydrodynamic conditions on reactive crystallization process control is studied. Finally, the effect of extreme local supersaturation is studied using power ultrasound to initiate nucleation. The present work shows that mixing and chemical feeding conditions clearly affect induction time and cluster formation, nucleation, growth kinetics, and agglomeration. Consequently, the properties of crystalline end products, e.g. crystal size and crystal habit, can be influenced by management of mixing and feeding conditions. Impurities may have varying impacts on crystallization processes. As an example, manganese ions were shown to replace magnesium ions in the crystal lattice of magnesium sulphate heptahydrate, increasing the crystal growth rate significantly, whereas sodium ions showed no interaction at all. Modelling of continuous crystallization based on MSMPR theory showed that the model is feasible in a small laboratoryscale crystallizer, whereas in larger pilot- and industrial-scale crystallizers hydrodynamic effects should be taken into account. For that reason, CFD and multiblock modelling are shown to be effective tools for modelling crystallization with inhomogeneous mixing. The present work shows also that selection of the measurement point, or points in the case of multiprobe systems, is crucial when process analytical technology (PAT) is used to control larger scale crystallization. The thesis concludes by describing how control of local supersaturation by highly localized ultrasound was successfully applied to induce nucleation and to control polymorphism in reactive crystallization of L-glutamic acid.

Benefits and challenges of supply chain finance business process automation : A case study in ING Bank

Supply chain finance, a financial product provided by the bank, has gained increasing attention and popularity over the last few years. Supply chain finance helps the corporate clients to optimize their financial flows along the supply chain. One characteristic of supply chain finance is that it aims to provide automated solutions. Therefore, the business process automation of supply chain finance is a very interesting and important topic for study. In this study, the business process automation of supply chain finance within the case organization, ING, is analysed. The purpose is to: (1) Identify the benefits to understand the importance to automate supply chain finance business process; (2) Find out the existing automation degree in the supply chain finance business process within the case bank to see what’s the situation now and how to improve in the future; (3) Discover the challenges in the further automation of supply chain finance business process. Firstly, the study finds out that supply chain finance business process automation can bring many benefits to the bank. Automation can improve productivity by using less time and human labour in the business process, and by providing scalable solutions. Automation can also improve quality of the service by reducing the human errors. Last but not least, automation can improve internal governance by providing enhanced visibility of the business process. Because of these potential benefits, many banks are actively seeking solutions to automate their supply chain finance business process. Then, the current automation situation with the case bank is analysed with the help of business process modelling. The supply chain finance business process within the case bank can be further divided into several sub processes: daily transaction, buyer sales and setup, supplier onboarding, contract management, customer services and supports, and contract termination. The study finds out that the daily transaction process is already a highly automated, which is carried out through the web-based trading platform. However, for other business the automation degree is relatively low. Among these business processes, supplier onboarding is most needed for further automation. Then, some solutions are also suggested to automate the supplier onboarding business process. In the end, the study also foresees some challenges during the further automation of supply chain finance business process in the case bank. Some suggestions are also given to deal with these challenges.

Modelling the exothermic heat and the demand of cooling in paper and pulp industry biological wastewater treatment

This master thesis presents a study on the requisite cooling of an activated sludge process in paper and pulp industry. The energy consumption of paper and pulp industry and it’s wastewater treatment plant in particular is relatively high. It is therefore useful to understand the wastewater treatment process of such industries. The activated sludge process is a biological mechanism which degrades carbonaceous compounds that are present in waste. The modified activated sludge model constructed here aims to imitate the bio-kinetics of an activated sludge process. However, due to the complicated non-linear behavior of the biological process, modelling this system is laborious and intriguing. We attempt to find a system solution first using steady-state modelling of Activated Sludge Model number 1 (ASM1), approached by Euler’s method and an ordinary differential equation solver. Furthermore, an enthalpy study of paper and pulp industry’s vital pollutants was carried out and applied to revise the temperature shift over a period of time to formulate the operation of cooling water. This finding will lead to a forecast of the plant process execution in a cost-effective manner and management of effluent efficiency. The final stage of the thesis was achieved by optimizing the steady state of ASM1.