Ulkomaalaislupaprosessin läpäisyaikojen hallinta poliisilaitoksella

Tämä diplomityö käsittelee Lappeenrannan teknillisen yliopiston ja Etelä- Karjalan poliisilaitoksen yhteistä kehitysprojektia, jonka tavoitteena oli tuottaa käytäntöön vietävissä oleva kehittämissuunnitelma ulkomaalaislupa-asioiden läpäisyaikojen hallintaan. Kehitysalueita on pyritty löytämään poliisilaitoksen henkilöstön haastatteluilla ja prosessianalyyseilla. Työ sisältää teoriaosion määräaikahallinnasta, yleiskatsauksen ulkomaalaislupa-asioista Suomessa sekä selvityksen ulkomaalaislupa-asioiden tilanteesta Etelä-Karjalan poliisilaitoksella. Työssä muodostettiin kolme kehittämisteemaa: seurannan kehittäminen, prosessien ja työtapojen kehittäminen ja yhteistyön kehittäminen. Tärkein kehittämistoimi oli Excel-pohjaisen seurantajärjestelmän suunnittelu, rakentaminen ja käyttöönottaminen. Seurantajärjestelmää voidaan hyödyntää tehtävien päivittäisessä ohjauksessa, määräaikatavoitteiden toteutumisen seurannassa sekä tilannekuvan ylläpitämisessä. Tavoitetilanteessa seurantajärjestelmän avulla hallitaan tilannetta siten, että hallitsemattomia hakemusruuhkia ei synny. Hankkeesta saatuja kokemuksia tehtäväjonojen hallinnasta ja suorituskyvyn mittaamisesta voidaan ajatella hyödynnettävän myös muissa prosesseissa.

Aging based maintenance and reinvestment scheduling of electric distribution

The maintenance of electric distribution network is a topical question for distribution system operators because of increasing significance of failure costs. In this dissertation the maintenance practices of the distribution system operators are analyzed and a theory for scheduling maintenance activities and reinvestment of distribution components is created. The scheduling is based on the deterioration of components and the increasing failure rates due to aging. The dynamic programming algorithm is used as a solving method to maintenance problem which is caused by the increasing failure rates of the network. The other impacts of network maintenance like environmental and regulation reasons are not included to the scope of this thesis. Further the tree trimming of the corridors and the major disturbance of the network are not included to the problem optimized in this thesis. For optimizing, four dynamic programming models are presented and the models are tested. Programming is made in VBA-language to the computer. For testing two different kinds of test networks are used. Because electric distribution system operators want to operate with bigger component groups, optimal timing for component groups is also analyzed. A maintenance software package is created to apply the presented theories in practice. An overview of the program is presented.

Scheduling of guarded command based models

Formal methods provide a means of reasoning about computer programs in order to prove correctness criteria. One subtype of formal methods is based on the weakest precondition predicate transformer semantics and uses guarded commands as the basic modelling construct. Examples of such formalisms are Action Systems and Event-B. Guarded commands can intuitively be understood as actions that may be triggered when an associated guard condition holds. Guarded commands whose guards hold are nondeterministically chosen for execution, but no further control flow is present by default. Such a modelling approach is convenient for proving correctness, and the Refinement Calculus allows for a stepwise development method. It also has a parallel interpretation facilitating development of concurrent software, and it is suitable for describing event-driven scenarios. However, for many application areas, the execution paradigm traditionally used comprises more explicit control flow, which constitutes an obstacle for using the above mentioned formal methods. In this thesis, we study how guarded command based modelling approaches can be conveniently and efficiently scheduled in different scenarios. We first focus on the modelling of trust for transactions in a social networking setting. Due to the event-based nature of the scenario, the use of guarded commands turns out to be relatively straightforward. We continue by studying modelling of concurrent software, with particular focus on compute-intensive scenarios. We go from theoretical considerations to the feasibility of implementation by evaluating the performance and scalability of executing a case study model in parallel using automatic scheduling performed by a dedicated scheduler. Finally, we propose a more explicit and non-centralised approach in which the flow of each task is controlled by a schedule of its own. The schedules are expressed in a dedicated scheduling language, and patterns assist the developer in proving correctness of the scheduled model with respect to the original one.

It’s a workflow engine. It’s working with linked data. It’s repository! – Supporting electronic thesis related processes in University of Helsinki library

Presentation at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014

SYLI - an external workflow system for DSpace

Presentation at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014

Exposing usage and workflow in a DSpace repository

Poster at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014

Scheduling dynamic dataflow graphs with model checking

With the shift towards many-core computer architectures, dataflow programming has been proposed as one potential solution for producing software that scales to a varying number of processor cores. Programming for parallel architectures is considered difficult as the current popular programming languages are inherently sequential and introducing parallelism is typically up to the programmer. Dataflow, however, is inherently parallel, describing an application as a directed graph, where nodes represent calculations and edges represent a data dependency in form of a queue. These queues are the only allowed communication between the nodes, making the dependencies between the nodes explicit and thereby also the parallelism. Once a node have the su cient inputs available, the node can, independently of any other node, perform calculations, consume inputs, and produce outputs. Data ow models have existed for several decades and have become popular for describing signal processing applications as the graph representation is a very natural representation within this eld. Digital lters are typically described with boxes and arrows also in textbooks. Data ow is also becoming more interesting in other domains, and in principle, any application working on an information stream ts the dataflow paradigm. Such applications are, among others, network protocols, cryptography, and multimedia applications. As an example, the MPEG group standardized a dataflow language called RVC-CAL to be use within reconfigurable video coding. Describing a video coder as a data ow network instead of with conventional programming languages, makes the coder more readable as it describes how the video dataflows through the different coding tools. While dataflow provides an intuitive representation for many applications, it also introduces some new problems that need to be solved in order for data ow to be more widely used. The explicit parallelism of a dataflow program is descriptive and enables an improved utilization of available processing units, however, the independent nodes also implies that some kind of scheduling is required. The need for efficient scheduling becomes even more evident when the number of nodes is larger than the number of processing units and several nodes are running concurrently on one processor core. There exist several data ow models of computation, with different trade-offs between expressiveness and analyzability. These vary from rather restricted but statically schedulable, with minimal scheduling overhead, to dynamic where each ring requires a ring rule to evaluated. The model used in this work, namely RVC-CAL, is a very expressive language, and in the general case it requires dynamic scheduling, however, the strong encapsulation of dataflow nodes enables analysis and the scheduling overhead can be reduced by using quasi-static, or piecewise static, scheduling techniques. The scheduling problem is concerned with nding the few scheduling decisions that must be run-time, while most decisions are pre-calculated. The result is then an, as small as possible, set of static schedules that are dynamically scheduled. To identify these dynamic decisions and to find the concrete schedules, this thesis shows how quasi-static scheduling can be represented as a model checking problem. This involves identifying the relevant information to generate a minimal but complete model to be used for model checking. The model must describe everything that may affect scheduling of the application while omitting everything else in order to avoid state space explosion. This kind of simplification is necessary to make the state space analysis feasible. For the model checker to nd the actual schedules, a set of scheduling strategies are de ned which are able to produce quasi-static schedulers for a wide range of applications. The results of this work show that actor composition with quasi-static scheduling can be used to transform data ow programs to t many different computer architecture with different type and number of cores. This in turn, enables dataflow to provide a more platform independent representation as one application can be fitted to a specific processor architecture without changing the actual program representation. Instead, the program representation is in the context of design space exploration optimized by the development tools to fit the target platform. This work focuses on representing the dataflow scheduling problem as a model checking problem and is implemented as part of a compiler infrastructure. The thesis also presents experimental results as evidence of the usefulness of the approach.

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.

Developing purchase invoice processing through workflow automation

The aim of this master’s thesis is to research and analyze how purchase invoice processing can be automated and streamlined in a system renewal project. The impacts of workflow automation on invoice handling are studied by means of time, cost and quality aspects. Purchase invoice processing has a lot of potential for automation because of its labor-intensive and repetitive nature. As a case study combining both qualitative and quantitative methods, the topic is approached from a business process management point of view. The current process was first explored through interviews and workshop meetings to create a holistic understanding of the process at hand. Requirements for process streamlining were then researched focusing on specified vendors and their purchase invoices, which helped to identify the critical factors for successful invoice automation. To optimize the flow from invoice receipt to approval for payment, the invoice receiving process was outsourced and the automation functionalities of the new system utilized in invoice handling. The quality of invoice data and the need of simple structured purchase order (PO) invoices were emphasized in the system testing phase. Hence, consolidated invoices containing references to multiple PO or blanket release numbers should be simplified in order to use automated PO matching. With non-PO invoices, it is important to receive the buyer reference details in an applicable invoice data field so that automation rules could be created to route invoices to a review and approval flow. In the beginning of the project, invoice processing was seen ineffective both time- and cost-wise, and it required a lot of manual labor to carry out all tasks. In accordance with testing results, it was estimated that over half of the invoices could be automated within a year after system implementation. Processing times could be reduced remarkably, which would then result savings up to 40 % in annual processing costs. Due to several advancements in the purchase invoice process, business process quality could also be perceived as improved.

Viability of part cargoes in tramp shipping company’s routing and scheduling decisions

Operational excellence of individual tramp shipping companies is important in today’s market, where competition is intense, freight revenues are modest and capital costs high due to global financial crisis, and tighter regulatory framework is generating additional costs and challenges to the industry. This thesis concentrates on tramp shipping, where a tramp operator in a form of an individual case company, specialized in short-sea shipping activities in the Baltic Sea region, is searching ways to map their current fleet operations and better understand potential ways to improve the overall routing and scheduling decisions. The research problem is related to tramp fleet planning where several cargoes are carried on board at the same time, which are here systematically referred to as part cargoes. The purpose is to determine the pivotal dimensions and characteristics of these part cargo operations in tramp shipping, and offer both the individual case company and wider research community better understanding of potential risks and benefits related to utilization of part cargo operations. A mixed method research approach is utilized in this research, as the objectives are related to complex, real-life business practices in the field of supply chain management and more specifically, maritime logistics. A quantitative analysis of different voyage scenarios is executed, including alternative voyage legs with varying cost structure and customer involvement. An on-line-based questionnaire designed and prepared by case company’s decision group again provides desired data of predominant attitudes and views of most important industrial customers regarding the part cargo-related operations and potential future utilization of this business model. The results gained from these quantitative methods are complied with qualitative data collection tools, along with suitable secondary data sources. Based on results and logical analysis of different data sources, a framework for characterizing the different aspects of part cargo operations is developed, utilizing both existing research and empirical investigation of the phenomenon. As conclusions, part cargoes have the ability to be part of viable fleet operations, and even increase flexibility among the fleet to a certain extent. Naturally, several hinderers for this development is recognized as well, such as potential issues with information gathering and sharing, inefficient port activities, and increased transit times.

Irrigation scheduling for Sovereign Coronation grapevine based upon evapotranspiration calculations and crop coefficients /

Several irrigation treatments were evaluated on Sovereign Coronation table grapes at two sites over a 3-year period in the cool humid Niagara Peninsula of Ontario. Trials were conducted in the Hippie (Beamsville, ON) and the Lambert Vineyards (Niagara-on-the-Lake, ON) in 2003 to 2005 with the objective of assessing the usefulness of the modified Penman-Monteith equation to accurately schedule vine irrigation needs. Data (relative humidity, windspeed, solar radiation, and temperature) required to precisely calculate evapotranspiration (ETq) were downloaded from the Ontario Weather Network. One of two ETq values (either 100 or 150%) were used in combination with one of two crop coefficients (Kc; either fixed at 0.75 or 0.2 to 0.8 based upon increasing canopy volume) to calculate the amount of irrigation water required. Five irrigation treatments were: un irrigated control; (lOOET) X Kc =0.75; 150ET X Kc =0.75; lOOET X Kc =0.2-0.8; 150ET X Kc =0.2-0.8. Transpiration, water potential (v|/), and soil moisture data were collected each growing seasons. Yield component data was collected and berries from each treatment were analyzed for soluble solids (Brix), pH, titratable acidity (TA), anthocyanins, methyl anthranilate (MA), and total volatile esters (TVE). Irrigation showed a substantial positive effect on transpiration rate and soil moisture; the control treatment showed consistently lower transpiration and soil moisture over the 3 seasons. Transpiration appeared accurately reflect Sovereign Coronation grapevines water status. Soil moisture also accurately reflected level of irrigation. Moreover, irrigation showed impact of leaf \|/, which was more negative throughout the 3 seasons for vines that were not irrigated. Irrigation had a substantial positive effect on yield (kg/vine) and its various components (clusters/vine, cluster weight, and berries/cluster) in 2003 and 2005. Berry weights were higher under the irrigated treatments at both sites. Berry weight consistently appeared to be the main factor leading to these increased yields, as inconsistent responses were noted for some yield variables. Soluble solids was highest under the ET150 and ET100 treatments both with Kc at 0.75. Both pH and TA were highest under control treatments in 2003 and 2004, but highest under irrigated treatments in 2005. Anthocyanins and phenols were highest under the control treatments in 2003 and 2004, but highest under irrigated treatments in 2005. MA and TVE were highest under the ET150 treatments. Vine and soil water status measurements (soil moisture, leaf \|/, and transpiration) confirmed that irrigation was required for the summers of 2003 and 2005 due to dry weather in those years. They also partially supported the hypothesis that the Penman-Monteith equation is useful for calculating vineyard water needs. Both ET treatments gave clear evidence that irrigation could be effective in reducing water stress and for improving vine performance, yield and fruit composition. Use of properly scheduled irrigation was beneficial for Sovereign Coronation table grapes in the Niagara region. Findings herein should give growers some strong guidehnes on when, how and how much to irrigate their vineyards.

The impact of scheduling on the implementation of the new Ontario curriculum: teachers' perceptions /

This qualitative study explored secondary teachers' perceptions of scheduling in relation to pedagogy, curriculum, and observation of student learning. Its objective was to determine the best way to organize the scheduling for the delivery of Ontario's new 4-year curriculum. Six participants were chosen. Two were teaching in a semestered timetable, 1 in a traditional timetable, and 3 had experience in both schedules. Participants related a pressure cooker "lived experience" with weaker students in the semester system experiencing a particularly harsh environment. The inadequate amount of time for review in content-heavy courses, gap scheduling problems, catch-up difficulties for students missing classes, and the fast pace of semestering are identified as factors negatively impacting on these students. Government testing adds to the pressure by shifting teachers' time and attention in the classroom from deeper learning to a superficial coverage of material, from curriculum as lived to curriculum as text to be covered. Scheduling choice should be available in public education to accommodate the needs of all students. Curriculum guidelines need to be revamped to reflect the content that teachers believe is necessary for a successful course delivery. Applied level courses need to be developed for students who are not academically inferior but learn differently.

Reinforcement Learning Approaches to Power System Scheduling

One major component of power system operation is generation scheduling. The objective of the work is to develop efficient control strategies to the power scheduling problems through Reinforcement Learning approaches. The three important active power scheduling problems are Unit Commitment, Economic Dispatch and Automatic Generation Control. Numerical solution methods proposed for solution of power scheduling are insufficient in handling large and complex systems. Soft Computing methods like Simulated Annealing, Evolutionary Programming etc., are efficient in handling complex cost functions, but find limitation in handling stochastic data existing in a practical system. Also the learning steps are to be repeated for each load demand which increases the computation time.Reinforcement Learning (RL) is a method of learning through interactions with environment. The main advantage of this approach is it does not require a precise mathematical formulation. It can learn either by interacting with the environment or interacting with a simulation model. Several optimization and control problems have been solved through Reinforcement Learning approach. The application of Reinforcement Learning in the field of Power system has been a few. The objective is to introduce and extend Reinforcement Learning approaches for the active power scheduling problems in an implementable manner. The main objectives can be enumerated as:(i) Evolve Reinforcement Learning based solutions to the Unit Commitment Problem.(ii) Find suitable solution strategies through Reinforcement Learning approach for Economic Dispatch. (iii) Extend the Reinforcement Learning solution to Automatic Generation Control with a different perspective. (iv) Check the suitability of the scheduling solutions to one of the existing power systems.First part of the thesis is concerned with the Reinforcement Learning approach to Unit Commitment problem. Unit Commitment Problem is formulated as a multi stage decision process. Q learning solution is developed to obtain the optimwn commitment schedule. Method of state aggregation is used to formulate an efficient solution considering the minimwn up time I down time constraints. The performance of the algorithms are evaluated for different systems and compared with other stochastic methods like Genetic Algorithm.Second stage of the work is concerned with solving Economic Dispatch problem. A simple and straight forward decision making strategy is first proposed in the Learning Automata algorithm. Then to solve the scheduling task of systems with large number of generating units, the problem is formulated as a multi stage decision making task. The solution obtained is extended in order to incorporate the transmission losses in the system. To make the Reinforcement Learning solution more efficient and to handle continuous state space, a fimction approximation strategy is proposed. The performance of the developed algorithms are tested for several standard test cases. Proposed method is compared with other recent methods like Partition Approach Algorithm, Simulated Annealing etc.As the final step of implementing the active power control loops in power system, Automatic Generation Control is also taken into consideration.Reinforcement Learning has already been applied to solve Automatic Generation Control loop. The RL solution is extended to take up the approach of common frequency for all the interconnected areas, more similar to practical systems. Performance of the RL controller is also compared with that of the conventional integral controller.In order to prove the suitability of the proposed methods to practical systems, second plant ofNeyveli Thennal Power Station (NTPS IT) is taken for case study. The perfonnance of the Reinforcement Learning solution is found to be better than the other existing methods, which provide the promising step towards RL based control schemes for practical power industry.Reinforcement Learning is applied to solve the scheduling problems in the power industry and found to give satisfactory perfonnance. Proposed solution provides a scope for getting more profit as the economic schedule is obtained instantaneously. Since Reinforcement Learning method can take the stochastic cost data obtained time to time from a plant, it gives an implementable method. As a further step, with suitable methods to interface with on line data, economic scheduling can be achieved instantaneously in a generation control center. Also power scheduling of systems with different sources such as hydro, thermal etc. can be looked into and Reinforcement Learning solutions can be achieved.

Multi-objective assembly job shop scheduling using genetic algorithm and tabu search

Assembly job shop scheduling problem (AJSP) is one of the most complicated combinatorial optimization problem that involves simultaneously scheduling the processing and assembly operations of complex structured products. The problem becomes even more complicated if a combination of two or more optimization criteria is considered. This thesis addresses an assembly job shop scheduling problem with multiple objectives. The objectives considered are to simultaneously minimizing makespan and total tardiness. In this thesis, two approaches viz., weighted approach and Pareto approach are used for solving the problem. However, it is quite difficult to achieve an optimal solution to this problem with traditional optimization approaches owing to the high computational complexity. Two metaheuristic techniques namely, genetic algorithm and tabu search are investigated in this thesis for solving the multiobjective assembly job shop scheduling problems. Three algorithms based on the two metaheuristic techniques for weighted approach and Pareto approach are proposed for the multi-objective assembly job shop scheduling problem (MOAJSP). A new pairing mechanism is developed for crossover operation in genetic algorithm which leads to improved solutions and faster convergence. The performances of the proposed algorithms are evaluated through a set of test problems and the results are reported. The results reveal that the proposed algorithms based on weighted approach are feasible and effective for solving MOAJSP instances according to the weight assigned to each objective criterion and the proposed algorithms based on Pareto approach are capable of producing a number of good Pareto optimal scheduling plans for MOAJSP instances.

Thread Scheduling Mechanisms for Multiple-Context Parallel Processors

Scheduling tasks to efficiently use the available processor resources is crucial to minimizing the runtime of applications on shared-memory parallel processors. One factor that contributes to poor processor utilization is the idle time caused by long latency operations, such as remote memory references or processor synchronization operations. One way of tolerating this latency is to use a processor with multiple hardware contexts that can rapidly switch to executing another thread of computation whenever a long latency operation occurs, thus increasing processor utilization by overlapping computation with communication. Although multiple contexts are effective for tolerating latency, this effectiveness can be limited by memory and network bandwidth, by cache interference effects among the multiple contexts, and by critical tasks sharing processor resources with less critical tasks. This thesis presents techniques that increase the effectiveness of multiple contexts by intelligently scheduling threads to make more efficient use of processor pipeline, bandwidth, and cache resources. This thesis proposes thread prioritization as a fundamental mechanism for directing the thread schedule on a multiple-context processor. A priority is assigned to each thread either statically or dynamically and is used by the thread scheduler to decide which threads to load in the contexts, and to decide which context to switch to on a context switch. We develop a multiple-context model that integrates both cache and network effects, and shows how thread prioritization can both maintain high processor utilization, and limit increases in critical path runtime caused by multithreading. The model also shows that in order to be effective in bandwidth limited applications, thread prioritization must be extended to prioritize memory requests. We show how simple hardware can prioritize the running of threads in the multiple contexts, and the issuing of requests to both the local memory and the network. Simulation experiments show how thread prioritization is used in a variety of applications. Thread prioritization can improve the performance of synchronization primitives by minimizing the number of processor cycles wasted in spinning and devoting more cycles to critical threads. Thread prioritization can be used in combination with other techniques to improve cache performance and minimize cache interference between different working sets in the cache. For applications that are critical path limited, thread prioritization can improve performance by allowing processor resources to be devoted preferentially to critical threads. These experimental results show that thread prioritization is a mechanism that can be used to implement a wide range of scheduling policies.