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Monitoring and mapping of atmospheric deposition can be achieved by use of chemical transport models, technical sampling devices and/or bio-accumulators such as mosses, lichens, etc. Within the European Moss Survey programme, since 1990 every 5 years mos specimens have been collected and analysed for heavy metals, nitrogen and persistent organic pollutants according a harmonised methodology. Calculation and geostatistical mapping of percentile statistics of element concentration in moss is in the focus of this paper. Thereby, element- and survey-specific as well as heavy metals and surveys integrating statistical evaluations and GIS-mapping were performed. Cr, Sb and Zn show contrary to Fe and Pb, no constant decrease of element concentrations, but an intermediate increase between 2000 and 2005, which did not continue until 2015. Cd, Hg, Pb and N which are of priority according to the Convention on Long-Range Transboundary Air Pollution are in focus in this paper. Additionally, Cr is regarded as representing those elements with an intermediate increase between the years 2000 and 2005 as is also true for Sb and Zn. Surveys integrating percentile statistics depict the spatial patterns of, in most cases, declining element concentrations across time. Survey-specific statistical analyses corroborate that the spatial patterns of element concentrations in moss are changing element-specifically across time. The long-term information on the percentile statistics of bioaccumulation of atmospheric deposition in moss is essential for further scientific evaluation as well as for measurements and reporting of nature protection and environmental management.

Recent developments in the information and communications technology (ICT) sector allow companies to utilise wireless networking devices at comparatively low costs. These technologies facil...

In this paper, we present a modeling framework for hub location problems with a service time limit considering congestion at hubs. Service time is modeled taking the traveling time on the hub network as well as the handling time and the delay caused by congestion at hubs into account. We develop mixed-integer linear programming formulations for the single and multiple allocation versions of this problem. We further extend the multiple allocation model with a possibility of direct shipments. We test our models on the well-known AP data set and analyze the effects of congestion and service time on costs and hub network design. We introduce a measure for the value of modeling congestion and show that not considering the effects of congestion may result in increased costs as well as in building infeasible hub networks.

Energy systems based on renewable energy sources require increasing demand side flexibility. Also, changes in the underlying cost structure, i.e., decreasing variable costs and increasing infrastructure investments as well as varying consumer needs should be reflected in the setup of future markets, including retail markets and tariffs. While various studies focus solely on tariffs with variable energy prices to leverage residential demand side flexibility, we incorporate tariffs with capacity-based price components in our analysis. The latter enable electricity providers to offer more differentiated tariffs, considering individual consumer needs and a balanced cost allocation. To compare the impact of different tariffs on residential demand side flexibility, we develop a bottom-up load model. This model not only simulates but also optimizes residential load profiles in the presence of different tariffs. The model is calibrated based on data from a large-scale field trial. Our results show that tariffs with variable energy prices induce larger demand side flexibility, but the impact of tariffs with variable capacity prices is more predictable and reliable from a suppliers perspective. Potential regulatory adjustments are identified enabling sustainable business models, rewarding demand side flexibility and facilitating the technical implementation.

In this paper, we study properties of a special class of ordered median functions, called block-functions. These are ordered median functions which belong to a generating binary (row)-vector of the form called a block vector. The aim of this paper is to explicitly determine the simplicial complexes and all steepest descent and ascent directions of descent and ascent cones of ordered median block-function.

This study focuses on the development of a modeling framework for multi-period stochastic capacitated multiple allocation hub location problems. We consider a planning horizon divided into several time periods. Uncertainty is assumed for the demands. The decisions to make concern the location of the hubs, their initial capacity, the capacity expansion of existing hubs and the transportation between origin–destination pairs. The goal is to minimize the total expected cost. For the situation in which uncertainty can be captured by a finite set of scenarios each occurring with some estimated probability we derive the extensive form of the deterministic equivalent. The resulting model is compact. However, it includes a set of binary variables that becomes too large for medium and large instances of the problem and thus hardly can it be tackled by a general optimization solver. For this reason, enhancements are proposed to the model making it possible to solve optimally instances that could not be solved using the initial model. This is confirmed by the computational tests performed using the well-known CAB data.

Metal deposition into terrestrial ecosystems: evaluating data from modelling and biomonitoring In Germany, atmospheric deposition of heavy metals derived by modelling and measurements using biological and technical samplers are used to spatially differentiate respective European data. However, a comparative analysis is still missing. Therefore, this article compares results from deposition modelling derived by LOTOS-EUROS and EMEP with data on heavy metals concentrations in leaves, needles and organic surface soil collected by intensive forest monitoring (ICP Forests Level II) and the Environmental Specimen Bank (ESB). The concentrations in leaves and needles collected during the years 2007–2011 for the specimen bank revealed clear correlations with the modelled values. They were specific for the matrices sampled and, in case of Cd concentrations in one year old spruce needles, for ecosystem types. Thereby, the correlations with LOTOS-EUROS were stronger than those with EMEP. Yet, these differences could not be proved to be significant. Most data retrieved from ICP Forests Level II monitoring showed stronger correlations with EMEP. They are lower than those calculated from specimen bank data. The advantage of IPC Forests Level II data is that it yields concentrations for different age groups of needles. Rather high correlations were found especially between LOTOS-EUROS results and Pb concentrations in two year old needles collected from pine and spruce. Additionally to leaves and needles, Forests Level II data were used to analyze heavy metal concentrations in organic soil layers. Significant correlations were computed for Cd between the modelled total deposition and respective concentrations in OH soil layers and for Pb between modelled total deposition and OL and OH soil layers. The different biomonitoring networks should be connected more tightly to enhance the spatial significance of their common data.

The determination of atmospheric deposition in forests can be accomplished using technical sampling devices (bulk samplers, wet only samplers), biomonitors or modelling. In Europe, since 1990 moss sampled every five years at up to 7300 places in up to 35 countries was used as biomonitor. In the moss specimens, heavy metals (HM), nitrogen (N, since 2005) and persistent organic pollutants (POPs, since 2010) were determined. Germany participated in all surveys with the exception of that in 2010. For the moss survey 2015, the biomonitoring network applied in the 2005 campaign should be reorganized. To this end, a complex statistically based methodology including a decision support system was developed and implemented. Its application yielded a network with a reduction of sample points from 726 to 402. By use of the data collected in 2005 the performance of the reorganized network did not reveal significant loss of statistical validity.

Supply chain networks are globalized and complex systems. Due to unexpected deviations and disruptions, which are subsumed under the notion of supply chain risk, the planning and optimizing of such...

Determination of heavy metal deposition in forests by use of modelling and moss biomonitoring Atmospheric deposition of elements such as heavy metals, to which environmental systems are exposed, can be determined by means of technical samplers, biological samplers such as moss and deposition modelling. This article aims at comparing heavy metal deposition as computed by two chemical transport models with empiric measurements of respective concentrations in moss collected in 2005. For Cd and Pb, the correlations (Spearman) were between 0.31 and 0.81 across Europe and across Germany. Thereby, highest correlations were determined for specific land use classes. For As, Cr, Cu, Ni, V and Zn lower correlations were found. It could be shown that heavy metals emission inventories should be complemented by spatial and ecosystem type-specific biomonitoring with moss and modelling at high spatial resolution. This should apply even if emission and atmospheric deposition of heavy metal are low since heavy metals are persistent and, therefore, accumulate in ecosystems.

Background Implementation Action 5 of the EU Biodiversity Strategy to 2020 includes that Member States map and assess the state of ecosystems and their services in their national territory. A fundamental component of the respective methodology developed in Germany is the classification of semi-natural ecosystems. In this context, this study aims to examine the quality and re-usability of the map of current semi-natural ecosystem types (cEsT) in Germany (1: 500,000; Jenssen et al. in Forschungsvorhaben 3710 83 214, UBA-FB 001834. UBA-Texte 87/2013. Dessau, Textband + 9 Anhänge, 381 S, 2013; Schröder et al. in Sci Total Environ 521–522:108–122, 2015, in Abschlussbericht Forschungsvorhaben UFOPLAN 3713 83 254 im Umweltforschungsplan des Bundesministeriums für Umwelt, Naturschutz, Bau und Reaktorsicherheit, Bd. 1:1–493 + 7 Anhänge, Bd. 2:1–343, Bd. 3:1–303. Dessau, 2018]) as well as the cEsT map of the Kellerwald National Park (1: 25,000). Results Based on DIN EN ISO 19113 and (DDGI in Qualitätsmodell für die Beschreibung von Geodaten (PAS 1071:2007-10), Beuth Verlag, Berlin, 14 S. + Anh, 2007), the positional accuracy (absolute positional accuracy) and thematic accuracy (classification correctness) were quantitatively determined. For this purpose, a comparison was made with geometrical data of well-known positional accuracy such as ATKIS-DLM (Hesse), mapping of biotopes and habitat types (Hesse, Germany), current vegetation surveys from the Kellerwald National Park (Hesse; permanent random sample inspection, own survey) as well as vegetation surveys available Germany-wide after 1990 from the database of the Institute of Forestry Eberswalde (Waldkunde-Institut Eberswalde; W.I.E.). The map of cEsT Germany has an absolute positional accuracy of ± 42.29 m (≈ ± 42 m) and has been classified correctly by about 30%. Another approximately 35% are ecologically similar to the existing forest ecosystem types (together 65%). In a further approximately 15%, the ecosystem types were correctly classified, but only occur as accompanying ecosystem types. About 15% occurred as an ecologically related accompanying ecosystem type (together 30%). 5% of the spatial objects were mapped incorrectly. In the Kellerwald National Park (1: 25,000), about 22% of the cEsT were classified correctly. Misclassifications on both scale levels concerned the assignments to the elevation levels (e.g., Eb-5n-C2 instead of D1-5n-C2) and, respectively, to the humus species (e.g., Eb-5n-D1 instead of Eb-5n-D1a). The main reason for misclassifications can be seen in high thematic differentiation of the ecosystem classification according to Jenssen et al. (2013). The biotope and habitat mappings are, due to their generally lower thematic differentiation, more appropriate for a falsification than for a verification of the cEsT approach. However, the spatial information content is valuable for comparisons with regard to the occurrence of cEsT as the main or concomitant ecosystem type. Conclusions The correctness of the classification can best be verified by vegetation samplings, but only at the site level. Any deviations found could then be used to improve the quality of the cEsT mapping, particularly at the regional level (1:5000 to 1:25,000). In principle, the use of the identification key for forest and forest ecosystem types (Schröder et al. 2018, vol. 3) is recommended for mapping on a regional scale.

Background The aim of the study was a statistical evaluation of the statistical relevance of potentially explanatory variables (atmospheric deposition, meteorology, geology, soil, topography, sampling, vegetation structure, land-use density, population density, potential emission sources) correlated with the content of 12 heavy metals and nitrogen in mosses collected from 400 sites across Germany in 2015. Beyond correlation analysis, regression analysis was performed using two methods: random forest regression and multiple linear regression in connection with commonality analysis. Results The strongest predictor for the content of Cd, Cu, Ni, Pb, Zn and N in mosses was the sampled species. In 2015, the atmospheric deposition showed a lower predictive power compared to earlier campaigns. The mean precipitation (2013–2015) is a significant factor influencing the content of Cd, Pb and Zn in moss samples. Altitude (Cu, Hg and Ni) and slope (Cd) are the strongest topographical predictors. With regard to 14 vegetation structure measures studied, the distance to adjacent tree stands is the strongest predictor (Cd, Cu, Hg, Zn, N), followed by the tree layer height (Cd, Hg, Pb, N), the leaf area index (Cd, N, Zn), and finally the coverage of the tree layer (Ni, Cd, Hg). For forests, the spatial density in radii 100–300 km predominates as significant predictors for Cu, Hg, Ni and N. For the urban areas, there are element-specific different radii between 25 and 300 km (Cd, Cu, Ni, Pb, N) and for agricultural areas usually radii between 50 and 300 km, in which the respective land use is correlated with the element contents. The population density in the 50 and 100 km radius is a variable with high explanatory power for all elements except Hg and N. Conclusions For Europe-wide analyses, the population density and the proportion of different land-use classes up to 300 km around the moss sampling sites are recommended.

We extend the setting of online optimization with look-ahead to online optimization with gradual look-ahead. While look-ahead as considered so far refers to a deterministic outlook on future data, gradual look-ahead only allows for an uncertain outlook on future data which becomes more and more precise as an input element’s release time is approached. After a discussion of related concepts, we formally introduce the class of online optimization problems with gradual look-ahead. Since the course of look-ahead information of a single input element is tied to a corresponding uncertainty set trajectory, we examine how different forecasting methods and different algorithmic approaches for dealing with gradual look-ahead can be instantiated and compared to each other with respect to the optimization output. We exemplify the introduced concepts by numerical experiments for the two applications of lot sizing and vehicle routing under gradual look-ahead.

The collection of atmospheric deposition by technical samplers and validated deposition modelling using chemical transport models is spatially complemented by using mosses as bioindicator: since 1990, the European moss survey has been providing data on element concentrations in moss every 5 years at up to 7300 sampling sites. In the moss specimens, heavy metals (since 1990), nitrogen (since 2005) and persistent organic pollutants (since 2010) were determined. Germany participated in all surveys with the exception of that in 2010. In this study, the spatial structures of element concentrations in moss collected in Germany between 1990 and 2015 were comparatively investigated by using Moran’s I statistics and Variogram analysis and mapped by use of Kriging interpolation. This is the precondition to spatially join the moss survey data with data collected at other locations within different environmental networks and to validate spatial patterns of atmospheric deposition as derived by technical sampling and modelling. The calculated maps reveal a clear and statistically significant decrease of most heavy metals, but not of nitrogen, in moss. Due to decreasing element concentrations and the unchanged application of the element concentration classification for the mapping, the heavy metal maps for the survey 2015 do no longer depict much spatial variation. Therefore, in an upcoming study, this analysis needs to be complemented for the heavy metals by calculating maps that depict the spatial structure of survey-specific percentile statistics 1990, 1995, 2000, 2005 and 2015.

A spatial explicit approach coping with potential modifications in ecological structures and functions of forests due to climate change and atmospheric deposition was developed for Germany (Jenssen et al. 2013; Schroder et al. in Sci Total Environ 521–522:108–122, 2015). As a result, two maps of current semi-natural forest ecosystem types of Germany (1991–2010) and the Kellerwald National Park (2001–2010) are presented. The data provided (Nickel et al. 2018) are accessible at https://doi.org/10.1594/PANGAEA.887305 and comprise 120 ecosystem types (89 near-natural ecosystems, 31 cultivated ecosystems) covering 27% of Germany’s territory and, respectively, 17 forest types (12 near-natural, 5 cultivated) covering 86% of the National Park area. The data are in particular of relevance for spatial ecology and conservation purposes. Associated metadata is available at https://agroenvgeo.data.inra.fr/geonetwork/srv/fre/catalog.search#/metadata/b5e5d76f-6176-4d6d-9089-a607bc8a4076

Location decisions play a key role in strategic logistics and supply chain management. In this chapter, we place the emphasis on the interaction of logistics activities and long-term supply chain decision-making on location logistics models. We cover modeling formulations of logistics core activities related to different industrial supply chain settings. In particular, we relate current challenges in supply chain management and their implications on relevant logistics activities. Finally, new research directions and areas of interest are provided.

Healthcare is one of the most important disciplines to ensure life quality for human beings, especially in unpredictable scenarios such as accidents, natural disasters, or terrorism. In these cases, Emergency Medical Services (EMS) face significant challenges due to the complex nature of pre-hospital events, coordinating several resources according to the collected information in a short time. In this context, both agent-based and discrete event simulation seem to be excellent approaches to find new strategies for facing this complexity. However, there are some challenges presented in the modeling process seeking an accurate representation of the reality. In this work, we introduce a general process of pre-hospital emergency services, a hybrid simulation model based on the general process, and a machine learning approach for key simulation parameters. Our general process benefits from an intensive exchange with a multidisciplinary group of experts from medicine, including practitioners, paramedics, coordination center managers, and stakeholders, among others. Furthermore, we validated our simulation model using real-world data from an emergency coordination center from North Germany. In addition, we introduce a machine learning approach for travel speed forecasting, using seven parameters for more accuracy. Finally, we present an application of our model to analyze critical crew capacity in the coordination center.

A key feature of dynamic problems which offer degrees of freedom to the decision maker is the necessity for a goal-oriented decision making routine which is employed every time the logic of the system requires a decision. In this paper, we look at optimization procedures which appear as subroutines in dynamic problems and show how discrete event simulation can be used to assess the quality of algorithms: after establishing a general link between online optimization and discrete event systems, we address performance measurement in dynamic settings and derive a corresponding tool kit. We then analyze several control strategies using the methodologies discussed previously in two real world examples of discrete event simulation models: a manual order picking system and a pickup and delivery service.

A vast number of real world problems are coined by an information release over time and the related need for repetitive decision making over time. Optimization problems arising in this context are called online since decisions have to be made although not all data is known. Due to technological advances, algorithms may also resort to a limited preview (lookahead) on future events. We first embed the paradigm of online optimization with lookahead into the theory of optimization and develop a concise understanding of lookahead. We further find that the effect of lookahead can be decomposed into an informational and a processual component. Based on analogies to discrete event systems, we then formulate a generic modeling framework for online optimization with lookahead and derive a classification scheme which facilitates a thorough categorization of different lookahead concepts. After an assessment of performance measurement approaches with relevance to practical needs, we conduct a series of computational experiments which illustrate how the general concept of lookahead applies to specific instantiations and how a knowledge pool on lookahead effects in applications can be built up using the general classification scheme.

This paper discusses a multi-period service scheduling problem. In this problem, a set of customers is given who periodically require service over a finite time horizon. To satisfy the service demands, a set of operators is given, each with a fixed capacity in terms of the number of customers an operator can serve per period. The task is to determine for each customer the periods in which he will be visited by an operator such that the periodic service requests of the customers are adhered to and the total number of operators used over the time horizon is minimal. Two alternative policies for scheduling customer visits are considered. In the first one, a customer is visited just on time, i.e., in the period where he or she has a demand for service. The second policy allows service visits ahead of time. The rationale behind this policy is that allowing irregular visits may reduce the overall number of operators needed throughout the time horizon. To solve the problem, integer linear programming formulations are proposed for both policies and numerical experiments are presented that show the reduction in the number of operators used when visits ahead of time are allowed. As only small instances can be solved optimally, a heuristic algorithm is introduced in order to obtain good quality solutions and shorter computing times.

Background The importance of patient participation and involvement is now widely acknowledged; in the past, few systematic health-care institution policies existed to establish sustainable co-operation. In 2004, in Germany, the initiative ‘Self-Help Friendliness (SHF) and Patient-Centeredness in Health Care’ was launched to establish and implement quality criteria related to collaboration with patient groups. Objectives The objective of this study was to describe (i) how patients were involved in the development of SHF by summarizing a number of studies and (ii) a new survey on the importance and feasibility of SHF. Setting and participants In a series of participative studies, SHF was shaped, tested and implemented in 40 health-care institutions in Germany. Representatives from 157 self-help groups (SHGs), 50 self-help organizations and 17 self-help clearing houses were actively involved. The second objective was reached through a survey of 74 of the 115 member associations of the biggest self-help umbrella organization at federal level (response rate: 64 %). Results Patient involvement included the following: identification of the needs and wishes of SHGs regarding co-operation, their involvement in the definition of quality criteria of co-operation, having a crucial role during the implementation of SHF and accrediting health-care institutions as self-help friendly. The ten criteria in total were positively valued and perceived as moderately practicable. Conclusions Through the intensive involvement of self-help representatives, it was feasible to develop SHF as a systematic approach to closer collaboration of professionals and SHGs. Some challenges have to be taken into account involving patients and the limitations of our empirical study.

The monthly nurse rostering is one of the most time consuming planning tasks of our project partner, a leading German home care provider. The planning is currently done manually by experienced senior nurses. However, the planning setup is very complex due to requirements such as legal working time restrictions, availabilities of full and part time nurses, and patient requests with differing requirements on qualification and frequency. The objective is to assign as few different nurses as possible to each patient. Although this continuity is important for quality of care, it is hardly addressed in literature. In this paper, we elaborate a mid-term home care rostering problem (HCR) that fits the requirements of our partner. It implies the assignment of nurses to weekly recurring patient-visit-tours under consideration of the aforementioned continuity of care. We propose different measures of continuity, which are used in five novel MIP formulations, each incorporating all hard planning constraints. We evaluate the models by means of 45 generated instances with realistic features. Further experiments on a real-world instance show that the approach improves continuity of care considerably compared to the manual solution.

In this paper, we investigate the problem of choosing the location and number of ambulances and their bases in a certain region. The goal is to minimize the total cost for installing (and maintaining) these facilities but assuring a minimum coverage level. Demand is assumed to be stochastic. A scenario-indexed formulation is considered for the problem. By using a small illustrative example we show that even when demand can be captured by a finite number of scenarios, rapidly the number of scenarios becomes too large, thus preventing the effective use of the model. We also show that the possibility of considering just one sample of scenarios can lead to a totally misleading solution. These insights motivate the development of a sampling approach in which we draw several samples of scenarios and solve the restricted model associated with each of them. The sample of optimal values obtained by this procedure can be combined in order to estimate the optimal value of the original problem. The same happens with the sample of optimal solutions obtained, which can be combined heuristically to produce a feasible solution to the whole problem. We test the new approach using a set of random instances.

Atmospheric deposition of heavy metals (HM) can be determined by use of numeric models, technical devices and biomonitors. Mainly focussing on Germany, this paper aims at evaluating data from deposition modelling and biomonitoring programmes. The model LOTOS-EUROS (LE) yielded data on HM deposition at a spatial resolution of 25 km by 25 km throughout Europe. The European Monitoring and Evaluation Programme (EMEP) provided model calculations on 50 km by 50 km grids. Corresponding data on HM concentration in moss, leaves and needles and soil were derived from the European Moss Survey (EMS), the German Environmental Specimen Bank (ESB) and the International Co-operative Programme on Assessment and Monitoring of Air Pollution Effects on Forests (iCP Forests). The modelled HM deposition and respective concentrations in moss (EMS), leaves and needles (ESB, iCP Forests) and soil (iCP Forests) were investigated for their statistical relationships. Regression equations were applied on geostatistical surface estimations of HM concentration in moss and then the residuals were interpolated by use of kriging interpolation. Both maps were summed up to a map of cadmium (Cd) and lead (Pb) deposition across Germany. Biomonitoring data were strongly correlated to LE than to EMEP. For HM concentrations in moss, the highest correlations were found for the association between geostatistical surface estimations of HM concentration in moss and deposition (LE).

This study proposes a multi-objective and multi-period mathematical programming model to support planning decisions in the Long-Term Care (LTC) sector. The model provides information to help planning the delivery of institutional LTC services in the medium-term, both in terms of location selection and capacity planning, when it is intended a move towards an equitable provision of care. For this purpose, three equity-related objectives are accounted for—equity of access, geographical equity and socioeconomic equity. Cost considerations are also taken into account. The model objective function incorporates multiple objectives which are scaled into a common unit through weights that are built with the Measuring Attractiveness by a Category-Based Evaluation Technique. The applicability of the model is illustrated through the resolution of a case study in the Great Lisbon region in Portugal.

While methods for optimization under uncertainty have been studied intensely over the past decades, the explicit consideration of the interplay between uncertainty and time has gained increasing attention rather recently. Problems requiring a sequence of decisions in reaction to uncertainty realizations are of crucial relevance in real-world applications, e.g., supply chain planning, scheduling, or finance. Several methods emphasizing varying aspects of these problems have been developed, mainly triggered by a particular application. Although these methods all intend to solve a similar underlying problem, they differ strongly with respect to the uncertainty representation, the prescriptive solution information they provide and the means of performance evaluation. The result is a fragmented picture of uncertain multi-stage problems – both from a methodological and an application-oriented perspective. It fails to interconnect results from different disciplines or even comparing strengths and weaknesses of individual methods in particular applications. This review aims at integrating the different methods for solving uncertainty inflicted multi-stage optimization problems into a broader picture, thereby paving the way for more comprehensive approaches to sequential decision making under uncertainty. For this purpose, a description of the methods along with their historic development is given first. Secondly, an overview on their main areas of application is provided. We conclude that decoupling uncertainty models from solution methods and developing standardized performance measures represent key steps for organizing multi-stage optimization under uncertainty and for eliciting further potentials of yet unexplored combinations of uncertainty models and solution methods.

We present a methodology for an artificial neural network (ANN) based metamodeling of simulation models in the special case when online decision making routines are invoked repetitively by the simulation model throughout the simulation run. For a practitioner, the benefit of such a simulation metamodel lies in the possibility to compare different decision making routines (operational control strategies) without excessive computational time for running multiple simulation configurations with different control strategies. Contrasting to the conventional setting of ANN based simulation metamodeling, in this paper ANNs have to take as input not only numerical parameters, but also different control strategies. The methodology is finally put into practice in a case study of an order picking system. Results show that on average the relative error of the ANN metamodel is fairly acceptable and allows for a first assessment of system parameters and control strategies. However, also large outliers in the relative error are encountered. Hence, for the analysis of different parameters and control strategies in applications, the use of ANN-based simulation metamodels requires an accompanying statistical assessment of ANN-based performance prediction accuracies.

Under the objective of improving intra-organisational logistics processes, this paper investigates the use of collaborative IT applications-based on track and trace technology. We report on company-wide logistics improvements attained through the introduction of a collaborative track and trace IT solution. In a chemical company, we considered the dependent processes of truck arrivals at the main gate and loading operations at stations inside the site. Without track and trace assistance, uncertainties in processing steps and driving times as well as space limitations lead to uncontrolled truck inflow to stations. To achieve a controllable situation, we developed an online IT application where at gate and station real-time data is collected. Based on the data, we employed a site entry control ensuring smooth station operations. Logistics performance indicators such as the average and maximum truck number at the station and site sojourn times of trucks were reduced significantly.

Background This paper aims to investigate the correlations between the concentrations of nine heavy metals in moss and atmospheric deposition within ecological land classes covering Europe. Additionally, it is examined to what extent the statistical relations are affected by the land use around the moss sampling sites. Based on moss data collected in 2010/2011 throughout Europe and data on total atmospheric deposition modelled by two chemical transport models (EMEP MSC-E, LOTOS-EUROS), correlation coefficients between concentrations of heavy metals in moss and in modelled atmospheric deposition were specified for spatial subsamples defined by ecological land classes of Europe (ELCE) as a spatial reference system. Linear discriminant analysis (LDA) and logistic regression (LR) were then used to separate moss sampling sites regarding their contribution to the strength of correlation considering the areal percentage of urban, agricultural and forestry land use around the sampling location. After verification LDA models by LR, LDA models were used to transform spatial information on the land use to maps of potential correlation levels, applicable for future network planning in the European Moss Survey. Results Correlations between concentrations of heavy metals in moss and in modelled atmospheric deposition were found to be specific for elements and ELCE units. Land use around the sampling sites mainly influences the correlation level. Small radiuses around the sampling sites examined (5 km) are more relevant for Cd, Cu, Ni, and Zn, while the areal percentage of urban and agricultural land use within large radiuses (75–100 km) is more relevant for As, Cr, Hg, Pb, and V. Most valid LDA models pattern with error rates of

Background Since 1990, every 5 years, moss sampling is conducted within the European moss monitoring programme to assess the atmospheric deposition of airborne pollutants. Besides many other countries, Germany takes regularly part at these evaluations. Within the European moss monitoring 2015, more than 400 moss samples across Germany were taken according to a harmonized methodology for the assessment heavy metal and nitrogen input. In a pilot programme, eight of these sites were chosen for additional investigations on a broad range of organic contaminants to evaluate their accumulation in moss and thereby their presence in atmospheric deposition in Germany. Target compound classes comprised polycyclic aromatic hydrocarbons (PAH), polychlorinated dibenzodioxins and –furans (PCDD/F), dioxin-like and non-dioxin-like polychlorinated biphenyls (dl-PCB, ndl-PCB), polyfluorinated alkyl substances, classical flame retardants as well as emerging chlorinated and brominated flame retardants. In total, 120 target compounds were analysed. For some analytes, comparisons of accumulation in moss and tree leave samples were possible. Results Except for certain flame retardants, PFAS, and ndl-PCB, substances of all other compound classes could be quantified in moss samples of all sites. Concentrations were highest for PAH (40–268 ng g−1) followed by emerging flame retardants (0.5–7.7 ng g−1), polybrominated diphenyl ethers (PBDE; 0.3–3.7 ng g−1), hexabromocyclododecane (HBCD; 0.3–1.2 ng g−1), dl-PCB (0.04–0.4 ng g−1) and PCDD/F (0.008–0.06 ng g−1). Conclusions Results show the widespread atmospheric distribution and deposition of organic contaminants across Germany as well as the suitability of moss as bioaccumulation monitor for most of these compound classes. Compared to nearby tree leaf samples, accumulation potential of moss appeared to be higher for pollutants of high octanol–air partition coefficient (KOA) and octanol–water partition coefficient (KOW). Electronic supplementary material The online version of this article (10.1186/s12302-018-0172-y) contains supplementary material, which is available to authorized users.

In this study, the canopy drip effect on the exposure of forests to atmospheric deposition of potentially toxic metals and nitrogen (N) and element accumulation was investigated. Thereby, the respective element concentrations of metals and N in moss specimens were investigated by example of North-Western Germany. To this end, on the one hand, the concentrations of Al, As, Cd, Cr, Cu, Fe, Hg, Pb, Ni, Sb, V, Zn, and N in mosses sampled under, outside, and at the edge of forest canopies were examined for statistical significant differences. On the other hand, vegetation structures parameterizing the canopy drip effect were quantified by use of information collected, in addition to the element data, at each moss sampling site. The statistical relations between ratios of vegetation parameters and ratios of element concentrations were modeled by regression analysis, and the respective element concentration in moss was geostatistically estimated and mapped for unsampled locations throughout Germany. This article tackles regression models with R2 > 0.5 (Cu, Hg, Pb, Sb, and N) to adapt the element concentrations measured at the 400 sites of the European Moss Survey (EMS) to three different features of hypothetical vegetation structures. To this end, the continuum of vegetation structures were represented as follows: open land (meadows) described by a leaf area index (LAI) value of 2.96 and under canopy sites in coniferous forests represented by a LAI value of 11. The arithmetic mean of LAI values at 400 EMS sites throughout Germany amounts to 5.1. The element concentrations for these target LAIs representing three site categories were calculated and mapped. Then, these LAI-dependent element concentration maps were compared with the maps depicting the spatial patterns of “pure” element concentrations. Spatial differences were evaluated and supposed to be of great value for the validation of atmospheric deposition modeling.

This chapter presents our innovative approach to teach Industrial Engineering and Management Master students how to apply Operations Research to health care problems. We, the Chair of Discrete Optimization and Logistics (DOL) at the Karlsruhe Institute of Technology, offer the course “Operations Research in Health Care Management” where we give an introduction to the German health care system and cover a variety of Operations Research (OR) methods and their application to health care planning problems arising for example in hospitals, emergency medical services systems or for home health care providers. In our practical seminar “Health Care Management” students work in groups to solve real world problems from hospitals or other health care facilities using OR methods. During their project, they work in close collaboration with representatives from the project partners and are supervised by DOL research assistants. Additionally, students can deepen their skills in software tools by attending lectures on the IBM ILOG Optimization studio or on the simulation software AnyLogic. These skills are especially useful for our practical seminar. Finally, the seminar “Hospital Management” taught by a former hospital director gives additional insight into the hospital processes as well as regulations and best practices. The courses are supposed to increase the students’ interest in working in the health care domain. Our aim is to prepare motivated students for management positions in hospitals or other health care institutions.

Considering key uncertainties and health policy options in the reorganization of a long-term care (LTC) network is crucial. This study proposes a stochastic mixed integer linear programming model for planning the delivery of LTC services within a network of care where such aspects are modeled in an integrated manner. The model assists health care planners on how to plan the delivery of the entire range of LTC services – institutional, home-based and ambulatory services – when the main policy objective is the minimization of expected costs and while respecting satisficing levels of equity. These equity levels are modeled as constraints, ensuring the attainment of equity of access, equity of utilization, socioeconomic equity and geographical equity. The proposed model provides planners with key information on: when and where to locate services and with which capacity, how to distribute this capacity across services and patient groups, and which changes to the network of care are needed over time. Model outputs take into account the uncertainty surrounding LTC demand, and consider strategic health policy options adopted by governments. The applicability of the model is demonstrated through the resolution of a case study in the Great Lisbon region in Portugal with estimates on the equity-cost trade-off for several equity dimensions being provided.

Zusammenfassung Fragestellung: In einem interdisziplinaren Projekt haben Psychoanalytiker und Historiker an der Universitat Hamburg die langfristigen psychischen Folgen bei Uberlebenden des "Hamburger Feuersturms von 1943" untersucht, eines grosen Bombenangriffs auf Hamburg im Zweiten Weltkrieg. In diesem Rahmen fragt die Arbeit nach Zusammenhangen des damaligen Erlebens und den verschiedenen Modalitaten der Verarbeitung mit den Folgen heute. Methode: Auswertung vollstandig transkribierter lebensgeschichtlicher Interviews mit 60 Zeitzeugen (31 Frauen, zum Zeitpunkt des Feuersturms zwischen drei und 27 Jahren alt) mit dem Verfahren der "Systematisierten Diagnostischen Eindrucksbildung". Diese Befunde wurden in einer Faktorenanalyse ausgewertet. Nach einer Bildung von Skalen wurden die Interdependenzen der Befunde durch Korrelationsberechnungen ermittelt. Ergebnisse: Im Vordergrund des Erlebens stand die Ausbombung, bei vielen Zeitzeugen mit dem vollstandigen Verlust aller personlichen Habe. Besonders der A...

Changes of forests in Germany due to climate change and nitrogen deposition According to the principles of the German Strategy for Adaptation to Climate Change nature conservation programmes and instruments should take more account of the findings of climate research, including the relevant uncertainties. In this context, a method for mapping climate-induced changes of ecosystem types is presented. It is built on a classification system for forest ecosystem types in Germany. The modelling approach is based on Classification and Regression Trees (CART) and geographic information on climate, soil and vegetation features available with blanket coverage of Germany. This method allows users to produce nationwide maps in a coarse temporal resolution (here: 1961–1990, 1991–2010, 2011–2040, 2041–2070). It is demonstrated how the allocation of the ecosystem types to Habitats Directive Annex I habitat types can be used to estimate changes of habitat types as a result of climate warming.

Economic systems are increasingly prone to complexity and uncertainty. Therefore, making well-informed decisions requires risk analysis, control and mitigation. In some areas such as finance, insurance, crisis management and health care, the importance of considering risk is largely acknowledged and well-elaborated, yet rather heterogeneous concepts and approaches for risk management have been developed. The increased frequency and the severe consequences of past supply chain disruptions have resulted in an increasing interest in risk. This development has led to the adoption of the risk concepts, terminologies and methods from related fields. In this paper, existing approaches for quantitative supply chain risk management are reviewed by setting the focus on the definition of supply chain risk and related concepts.

Many transport service providers operate on hub-and-spoke network structures. Major operators may have several dedicated hub facilities that are leased for a time horizon rather than being owned or constructed. For a given discrete planning horizon, service providers must decide on the location of the hub ports (i.e. terminals), the period when the lease contract starts, the period when the existing contracts must be terminated and the flow routing over the entire planning horizon so as to minimize the total operational cost. Thus, we propose a mathematical model for a Multi-period Uncapacitated Multiple Allocation Hub Location Problem with Budget Constraint. The proposed model incorporates several features of practice, particularly from maritime and land transport practices. We also propose a meta-heuristic solution algorithm that produces high-quality solutions in a reasonable amount of time. By exploiting the decomposable structure of the model, we extended a Benders decomposition approach by proposing several improvements. Extensive computational experiments confirm the efficiency of the proposed methods and also show its limitations.

In this paper, a modeling framework is proposed for multi-period hub location. The problems to be studied are extensions of classical hub location problems to the situation in which the hub network can be progressively built and its capacity gradually expanded over time. Both the single allocation and the multiple allocation cases are considered. For each case, a mixed-integer linear programming formulation is proposed and a set of valid inequalities is derived for enhancing the corresponding model. The results of a set of computational tests performed using the formulations proposed and their enhancements are reported. The value of the multi-period solution is discussed as a measure for evaluating the relevance of considering a multi-period model instead of a static counterpart.