Production planning and optimization of a pulp mill: introduction of the optimisation based scheduling software for pulp industry

The objective of this project was to introduce a new software product to pulp industry, a new market for case company. An optimization based scheduling tool has been developed to allow pulp operations to better control their production processes and improve both production efficiency and stability. Both the work here and earlier research indicates that there is a potential for savings around 1-5%. All the supporting data is available today coming from distributed control systems, data historians and other existing sources. The pulp mill model together with the scheduler, allows what-if analyses of the impacts and timely feasibility of various external actions such as planned maintenance of any particular mill operation. The visibility gained from the model proves also to be a real benefit. The aim is to satisfy demand and gain extra profit, while achieving the required customer service level. Research effort has been put both in understanding the minimum features needed to satisfy the scheduling requirements in the industry and the overall existence of the market. A qualitative study was constructed to both identify competitive situation and the requirements vs. gaps on the market. It becomes clear that there is no such system on the marketplace today and also that there is room to improve target market overall process efficiency through such planning tool. This thesis also provides better overall understanding of the different processes in this particular industry for the case company.

Efficient Optimization Algorithms for Nonlinear Data Analysis

Identification of low-dimensional structures and main sources of variation from multivariate data are fundamental tasks in data analysis. Many methods aimed at these tasks involve solution of an optimization problem. Thus, the objective of this thesis is to develop computationally efficient and theoretically justified methods for solving such problems. Most of the thesis is based on a statistical model, where ridges of the density estimated from the data are considered as relevant features. Finding ridges, that are generalized maxima, necessitates development of advanced optimization methods. An efficient and convergent trust region Newton method for projecting a point onto a ridge of the underlying density is developed for this purpose. The method is utilized in a differential equation-based approach for tracing ridges and computing projection coordinates along them. The density estimation is done nonparametrically by using Gaussian kernels. This allows application of ridge-based methods with only mild assumptions on the underlying structure of the data. The statistical model and the ridge finding methods are adapted to two different applications. The first one is extraction of curvilinear structures from noisy data mixed with background clutter. The second one is a novel nonlinear generalization of principal component analysis (PCA) and its extension to time series data. The methods have a wide range of potential applications, where most of the earlier approaches are inadequate. Examples include identification of faults from seismic data and identification of filaments from cosmological data. Applicability of the nonlinear PCA to climate analysis and reconstruction of periodic patterns from noisy time series data are also demonstrated. Other contributions of the thesis include development of an efficient semidefinite optimization method for embedding graphs into the Euclidean space. The method produces structure-preserving embeddings that maximize interpoint distances. It is primarily developed for dimensionality reduction, but has also potential applications in graph theory and various areas of physics, chemistry and engineering. Asymptotic behaviour of ridges and maxima of Gaussian kernel densities is also investigated when the kernel bandwidth approaches infinity. The results are applied to the nonlinear PCA and to finding significant maxima of such densities, which is a typical problem in visual object tracking.

Distribution network design and optimization

The objective of this thesis is to examine distribution network designs and modeling practices and create a framework to identify best possible distribution network structure for the case company. The main research question therefore is: How to optimize case company’s distribution network in terms of customer needs and costs? Theory chapters introduce the basic building blocks of the distribution network design and needed calculation methods and models. Framework for the distribution network projects was created based on the theory and the case study was carried out by following the defined framework. Distribution network calculations were based on the company’s sales plan for the years 2014 - 2020. Main conclusions and recommendations were that the new Asian business strategy requires high investments in logistics and the first step is to open new satellite DC in China as soon as possible to support sales and second possible step is to open regional DC in Asia within 2 - 4 years.

Optimization and Measuring Techniques for Collect-and-Place Machines in Printed Circuit Board Industry

This thesis considers optimization problems arising in printed circuit board assembly. Especially, the case in which the electronic components of a single circuit board are placed using a single placement machine is studied. Although there is a large number of different placement machines, the use of collect-and-place -type gantry machines is discussed because of their flexibility and increasing popularity in the industry. Instead of solving the entire control optimization problem of a collect-andplace machine with a single application, the problem is divided into multiple subproblems because of its hard combinatorial nature. This dividing technique is called hierarchical decomposition. All the subproblems of the one PCB - one machine -context are described, classified and reviewed. The derived subproblems are then either solved with exact methods or new heuristic algorithms are developed and applied. The exact methods include, for example, a greedy algorithm and a solution based on dynamic programming. Some of the proposed heuristics contain constructive parts while others utilize local search or are based on frequency calculations. For the heuristics, it is made sure with comprehensive experimental tests that they are applicable and feasible. A number of quality functions will be proposed for evaluation and applied to the subproblems. In the experimental tests, artificially generated data from Markov-models and data from real-world PCB production are used. The thesis consists of an introduction and of five publications where the developed and used solution methods are described in their full detail. For all the problems stated in this thesis, the methods proposed are efficient enough to be used in the PCB assembly production in practice and are readily applicable in the PCB manufacturing industry.

Innovation process development and business process integration - a multiple case study

The main objective of this study was to find out the bases for innovation model formulation in an existing organization based on cases. Innovation processes can be analyzed based on their needs and based on their emphasis on the business model development or R&D. The research was conducted in energy sector within one company by utilizing its projects as cases for the study. It is typical for the field of business that development is slow, although the case company has put emphasis on its innovation efforts. Analysis was done by identifying the cases’ needs and comparing them. The results were that because of the variances in the needs of the cases, the applicability of innovation process models varies. It was discovered that by dividing the process into two phases, a uniform model could be composed. This model would fulfill the needs of the cases and potential future projects as well.

Market anomalies and tactical asset allocation: Utilising market anomalies in multiple asset class portfolios with the Black−Litterman model

This thesis discusses the basic problem of the modern portfolio theory about how to optimise the perfect allocation for an investment portfolio. The theory provides a solution for an efficient portfolio, which minimises the risk of the portfolio with respect to the expected return. A central feature for all the portfolios on the efficient frontier is that the investor needs to provide the expected return for each asset. Market anomalies are persistent patterns seen in the financial markets, which cannot be explained with the current asset pricing theory. The goal of this thesis is to study whether these anomalies can be observed among different asset classes. Finally, if persistent patterns are found, it is investigated whether the anomalies hold valuable information for determining the expected returns used in the portfolio optimization Market anomalies and investment strategies based on them are studied with a rolling estimation window, where the return for the following period is always based on historical information. This is also crucial when rebalancing the portfolio. The anomalies investigated within this thesis are value, momentum, reversal, and idiosyncratic volatility. The research data includes price series of country level stock indices, government bonds, currencies, and commodities. The modern portfolio theory and the views given by the anomalies are combined by utilising the Black-Litterman model. This makes it possible to optimise the portfolio so that investor’s views are taken into account. When constructing the portfolios, the goal is to maximise the Sharpe ratio. Significance of the results is studied by assessing if the strategy yields excess returns in a relation to those explained by the threefactormodel. The most outstanding finding is that anomaly based factors include valuable information to enhance efficient portfolio diversification. When the highest Sharpe ratios for each asset class are picked from the test factors and applied to the Black−Litterman model, the final portfolio results in superior riskreturn combination. The highest Sharpe ratios are provided by momentum strategy for stocks and long-term reversal for the rest of the asset classes. Additionally, a strategy based on the value effect was highly appealing, and it basically performs as well as the previously mentioned Sharpe strategy. When studying the anomalies, it is found, that 12-month momentum is the strongest effect, especially for stock indices. In addition, a high idiosyncratic volatility seems to be positively correlated with country indices on stocks.

Distributed energy resources in an electricity retailer’s short-term profit optimization

Liberalization of electricity markets has resulted in a competed Nordic electricity market, in which electricity retailers play a key role as electricity suppliers, market intermediaries, and service providers. Although these roles may remain unchanged in the near future, the retailers’ operation may change fundamentally as a result of the emerging smart grid environment. Especially the increasing amount of distributed energy resources (DER), and improving opportunities for their control, are reshaping the operating environment of the retailers. This requires that the retailers’ operation models are developed to match the operating environment, in which the active use of DER plays a major role. Electricity retailers have a clientele, and they operate actively in the electricity markets, which makes them a natural market party to offer new services for end-users aiming at an efficient and market-based use of DER. From the retailer’s point of view, the active use of DER can provide means to adapt the operation to meet the challenges posed by the smart grid environment, and to pursue the ultimate objective of the retailer, which is to maximize the profit of operation. This doctoral dissertation introduces a methodology for the comprehensive use of DER in an electricity retailer’s short-term profit optimization that covers operation in a variety of marketplaces including day-ahead, intra-day, and reserve markets. The analysis results provide data of the key profit-making opportunities and the risks associated with different types of DER use. Therefore, the methodology may serve as an efficient tool for an experienced operator in the planning of the optimal market-based DER use. The key contributions of this doctoral dissertation lie in the analysis and development of the model that allows the retailer to benefit from profit-making opportunities brought by the use of DER in different marketplaces, but also to manage the major risks involved in the active use of DER. In addition, the dissertation introduces an analysis of the economic potential of DER control actions in different marketplaces including the day-ahead Elspot market, balancing power market, and the hourly market of Frequency Containment Reserve for Disturbances (FCR-D).

GIS-based approach for optimization of onshore wind park infrastructure alignment in Finland

Wind power is a rapidly developing, low-emission form of energy production. In Fin-land, the official objective is to increase wind power capacity from the current 1 005 MW up to 3 500–4 000 MW by 2025. By the end of April 2015, the total capacity of all wind power project being planned in Finland had surpassed 11 000 MW. As the amount of projects in Finland is record high, an increasing amount of infrastructure is also being planned and constructed. Traditionally, these planning operations are conducted using manual and labor-intensive work methods that are prone to subjectivity. This study introduces a GIS-based methodology for determining optimal paths to sup-port the planning of onshore wind park infrastructure alignment in Nordanå-Lövböle wind park located on the island of Kemiönsaari in Southwest Finland. The presented methodology utilizes a least-cost path (LCP) algorithm for searching of optimal paths within a high resolution real-world terrain dataset derived from airborne lidar scannings. In addition, planning data is used to provide a realistic planning framework for the anal-ysis. In order to produce realistic results, the physiographic and planning datasets are standardized and weighted according to qualitative suitability assessments by utilizing methods and practices offered by multi-criteria evaluation (MCE). The results are pre-sented as scenarios to correspond various different planning objectives. Finally, the methodology is documented by using tools of Business Process Management (BPM). The results show that the presented methodology can be effectively used to search and identify extensive, 20 to 35 kilometers long networks of paths that correspond to certain optimization objectives in the study area. The utilization of high-resolution terrain data produces a more objective and more detailed path alignment plan. This study demon-strates that the presented methodology can be practically applied to support a wind power infrastructure alignment planning process. The six-phase structure of the method-ology allows straightforward incorporation of different optimization objectives. The methodology responds well to combining quantitative and qualitative data. Additional-ly, the careful documentation presents an example of how the methodology can be eval-uated and developed as a business process. This thesis also shows that more emphasis on the research of algorithm-based, more objective methods for the planning of infrastruc-ture alignment is desirable, as technological development has only recently started to realize the potential of these computational methods.