Solving Irregular Strip Packing Problems With Free Rotations Using Separation Lines

Solving nesting problems or irregular strip packing problems is to position polygons in a fixed width and unlimited length strip, obeying polygon integrity containment constraints and non-overlapping constraints, in order to minimize the used length of the strip. To ensure non-overlapping, we used separation lines. A straight line is a separation line if given two polygons, all vertices of one of the polygons are on one side of the line or on the line, and all vertices of the other polygon are on the other side of the line or on the line. Since we are considering free rotations of the polygons and separation lines, the mathematical model of the studied problem is nonlinear. Therefore, we use the nonlinear programming solver IPOPT (an algorithm of interior points type), which is part of COIN-OR. Computational tests were run using established benchmark instances and the results were compared with the ones obtained with other methodologies in the literature that use free rotation

Machine Learning Approaches for Natural Resource Data

Abstract Real life applications involving efficient management of natural resources are dependent on accurate geographical information. This information is usually obtained by manual on-site data collection, via automatic remote sensing methods, or by the mixture of the two. Natural resource management, besides accurate data collection, also requires detailed analysis of this data, which in the era of data flood can be a cumbersome process. With the rising trend in both computational power and storage capacity, together with lowering hardware prices, data-driven decision analysis has an ever greater role. In this thesis, we examine the predictability of terrain trafficability conditions and forest attributes by using a machine learning approach with geographic information system data. Quantitative measures on the prediction performance of terrain conditions using natural resource data sets are given through five distinct research areas located around Finland. Furthermore, the estimation capability of key forest attributes is inspected with a multitude of modeling and feature selection techniques. The research results provide empirical evidence on whether the used natural resource data is sufficiently accurate enough for practical applications, or if further refinement on the data is needed. The results are important especially to forest industry since even slight improvements to the natural resource data sets utilized in practice can result in high saves in terms of operation time and costs. Model evaluation is also addressed in this thesis by proposing a novel method for estimating the prediction performance of spatial models. Classical model goodness of fit measures usually rely on the assumption of independently and identically distributed data samples, a characteristic which normally is not true in the case of spatial data sets. Spatio-temporal data sets contain an intrinsic property called spatial autocorrelation, which is partly responsible for breaking these assumptions. The proposed cross validation based evaluation method provides model performance estimation where optimistic bias due to spatial autocorrelation is decreased by partitioning the data sets in a suitable way. Keywords: Open natural resource data, machine learning, model evaluationTiivistelmä Käytännön sovellukset, joihin sisältyy luonnonvarojen hallintaa ovat riippuvaisia tarkasta paikkatietoaineistosta. Tämä paikkatietoaineisto kerätään usein manuaalisesti paikan päällä, automaattisilla kaukokartoitusmenetelmillä tai kahden edellisen yhdistelmällä. Luonnonvarojen hallinta vaatii tarkan aineiston keräämisen lisäksi myös sen yksityiskohtaisen analysoinnin, joka tietotulvan aikakautena voi olla vaativa prosessi. Nousevan laskentatehon, tallennustilan sekä alenevien laitteistohintojen myötä datapohjainen päätöksenteko on yhä suuremmassa roolissa. Tämä väitöskirja tutkii maaston kuljettavuuden ja metsäpiirteiden ennustettavuutta käyttäen koneoppimismenetelmiä paikkatietoaineistojen kanssa. Maaston kuljettavuuden ennustamista mitataan kvantitatiivisesti käyttäen kaukokartoitusaineistoa viideltä eri tutkimusalueelta ympäri Suomea. Tarkastelemme lisäksi tärkeimpien metsäpiirteiden ennustettavuutta monilla eri mallintamistekniikoilla ja piirteiden valinnalla. Väitöstyön tulokset tarjoavat empiiristä todistusaineistoa siitä, onko käytetty luonnonvaraaineisto riittävän laadukas käytettäväksi käytännön sovelluksissa vai ei. Tutkimustulokset ovat tärkeitä erityisesti metsäteollisuudelle, koska pienetkin parannukset luonnonvara-aineistoihin käytännön sovelluksissa voivat johtaa suuriin säästöihin niin operaatioiden ajankäyttöön kuin kuluihin. Tässä työssä otetaan kantaa myös mallin evaluointiin esittämällä uuden menetelmän spatiaalisten mallien ennustuskyvyn estimointiin. Klassiset mallinvalintakriteerit nojaavat yleensä riippumattomien ja identtisesti jakautuneiden datanäytteiden oletukseen, joka ei useimmiten pidä paikkaansa spatiaalisilla datajoukoilla. Spatio-temporaaliset datajoukot sisältävät luontaisen ominaisuuden, jota kutsutaan spatiaaliseksi autokorrelaatioksi. Tämä ominaisuus on osittain vastuussa näiden oletusten rikkomisesta. Esitetty ristiinvalidointiin perustuva evaluointimenetelmä tarjoaa mallin ennustuskyvyn mitan, missä spatiaalisen autokorrelaation vaikutusta vähennetään jakamalla datajoukot sopivalla tavalla. Avainsanat: Avoin luonnonvara-aineisto, koneoppiminen, mallin evaluoint

Predicting areas of potential conflicts between bearded vultures (Gypaetus barbatus) and wind turbines in the Swiss Alps

The alarming increase in global temperature observed over the last hundred years, driven by the use of fossil fuels, has prompted a shift towards “greener” energy production. An extensive expansion of wind power exploitation is expected in the coming years, which makes its effect on vulnerable species an issue of growing conservation concern. Among the wildlife affected by wind turbines, vultures are probably the most vulnerable avian ecological guild. They have experienced a sharp decline during the last decades and their survival in many areas is the result of targeted recovery and conservation actions. The bearded vulture (Gypaetus barbatus) represents an emblematic example. After having been extirpated from the European Alps, the species once again inhabits its former habitat, thanks to the massive long-lasting effort of a dedicated reintroduction programme. There are concerns, however, that the sprawl of wind turbines in the Alpine massif will jeopardise this successful population recovery. The main goal of this PhD thesis was therefore to predict areas in the Swiss Alps where conflicts between bearded vulture conservation and wind energy development are likely to occur, thus allowing for a more biodiversity-friendly spatial planning of wind turbines. Using a spatially explicit modelling framework with combined information of casual observations and GPS data, I predicted species’ potential distribution as well as its flight behaviour in relation to landscape, wind, and foraging conditions. First, I investigated the species ecological requirements in relation to season and age and translated these into distribution maps covering the whole Swiss Alpine arc. Here the focus was on evaluating the ability of the models to predict the possible future expansion of the species, a crucial point for anticipating potential conflicts arising from the spread of wind energy. During this process, I secondly had to delve into methodological challenges, especially with regard to taking objective decisions for model tuning. Based on the example of modelling the distribution of the bearded vulture, I introduced a new genetic algorithm for hyperparameters tuning, which drastically reduces computation time while achieving a model performance comparable or equal to that obtained with standard methods. Moreover, I generalised the developed routines so as to make them applicable to the most common species distribution modelling techniques and compiled the solutions in an R package now available to the scientific community. Thirdly, I explored the flight height patterns of bearded vultures to identify key factors driving low-height flight activity and delineated areas where the species is likely to fly within the critical height range that is typically swept by the blades of modern wind turbines. Overall, I found that food availability is an important driver of both distribution and low-height flight activity of bearded vultures. Habitat selection differed between seasons and between age classes during the cold season. While food availability and geological substrates were the main drivers of the distribution during the warm season, I observed a shift in the requirement of adult birds in the cold season, where habitat selection was mainly influenced by climatic conditions. This suggests that adult birds may be constrained by favourable winter conditions for the selection of breeding territories. Combining the ecological requirements of both age classes and seasons I found that 40% of the Swiss Alps offers suitable habitat for the species. The model trained with species data collected between 2004 and 2014 was able to accurately predict new breeding territories established in 2015 – 2019, and thus adequately delineated areas where the spreading population will likely to occur in the future and where conflicts with wind energy development might arise. The flight-height analysis of the GPS-tagged birds revealed that bearded vultures mainly fly within the critical height range swept by the turbine blades (77.5% of GPS locations), which poses the species at high risk of collision. Flying at low heights most frequently occurred along south exposed mountainsides and in areas with a high probability of ibex (Capra ibex) presence, a key food source for bearded vulture. Synthesising the information on bearded vulture distribution with the flight height behaviour allowed identifying and mapping areas where the species is likely to fly at risky height within its habitat. This high resolution, spatially explicit information represents a valuable tool for planners involved in wind energy development as well as a first basis for detailed impact assessments, while the methodological framework I developed represents a transferable approach for scientists studying potential conflicts between the development of aerial infrastructure and other target organisms

A new mixed-integer programming model for irregular strip packing based on vertical slices with a reproducible survey

The irregular strip-packing problem, also known as nesting or marker making, is defined as the automatic computation of a non-overlapping placement of a set of non-convex polygons onto a rectangular strip of fixed width and unbounded length, such that the strip length is minimized. Nesting methods based on heuristics are a mature technology, and currently, the only practical solution to this problem. However, recent performance gains of the Mixed-Integer Programming (MIP) solvers, together with the known limitations of the heuristics methods, have encouraged the exploration of exact optimization models for nesting during the last decade. Despite the research effort, the current family of exact MIP models for nesting cannot efficiently solve both large problem instances and instances containing polygons with complex geometries. In order to improve the efficiency of the current MIP models, this work introduces a new family of continuous MIP models based on a novel formulation of the NoFit-Polygon Covering Model (NFP-CM), called NFP-CM based on Vertical Slices (NFP-CM-VS). Our new family of MIP models is based on a new convex decomposition of the feasible space of relative placements between pieces into vertical slices, together with a new family of valid inequalities, symmetry breakings, and variable eliminations derived from the former convex decomposition. Our experiments show that our new NFP-CM-VS models outperform the current state-of-the-art MIP models. Finally, we provide a detailed reproducibility protocol and dataset based on our Java software library as supplementary material to allow the exact replication of our models, experiments, and results

Operational research IO 2021—analytics for a better world. XXI Congress of APDIO, Figueira da Foz, Portugal, November 7–8, 2021

This book provides the current status of research on the application of OR methods to solve emerging and relevant operations management problems. Each chapter is a selected contribution of the IO2021 - XXI Congress of APDIO, the Portuguese Association of Operational Research, held in Figueira da Foz from 7 to 8 November 2021. Under the theme of analytics for a better world, the book presents interesting results and applications of OR cutting-edge methods and techniques to various real-world problems. Of particular importance are works applying nonlinear, multi-objective optimization, hybrid heuristics, multicriteria decision analysis, data envelopment analysis, simulation, clustering techniques and decision support systems, in different areas such as supply chain management, production planning and scheduling, logistics, energy, telecommunications, finance and health. All chapters were carefully reviewed by the members of the scientific program committee.info:eu-repo/semantics/publishedVersio

Shipping Configuration Optimization with Topology-Based Guided Local Search for Irregular Shaped Shipments

Manufacturer that uses containers to ship products always works to optimize the space inside the containers. Container loading problems (CLP) are widely encountered in forms of raw material flow and handling, product shipments, warehouse management, facility floor planning, as well as strip-packing nesting problems.Investigations and research conducted two decades ago were logistic orientated, on the basis of the empirical approaches