Data-driven warehouse optimization

Batching orders and routing order pickers is a commonly studied problem in many picker-to-parts warehouses. The impact of individual differences in picking skills on performance has received little attention. In this paper, we show that taking into account differences in the skills of individual pickers when assigning work has a substantial effect on total batch execution time and picker productivity. We demonstrate this for the case of a Finnish retailer. First, using time-stamped picking data, multilevel modeling is used to forecast batch execution times for individual pickers by modeling individual skills of pickers. Next, these forecasts are used to minimize total batch execution time, by assigning the right picker to the right order batch. We formulate the problem as a joint order batching and generalized assignment model, and solve it with an Adaptive Large Neighborhood Search algorithm. For the sample company, we are able to improve state-of-the-art batching and routing methods by almost 10% taking skill differences among pickers into account and minimizing the sum of total order processing time. Compared to assigning order batches to pickers only based on individual picker productivity, savings of 6% in total time are achieved

Localization in highly dynamic environments using dual-timescale NDT-MCL

Industrial environments are rarely static and often their configuration is continuously changing due to the material transfer flow. This is a major challenge for infrastructure free localization systems. In this paper we address this challenge by introducing a localization approach that uses a dual- timescale approach. The proposed approach - Dual-Timescale Normal Distributions Transform Monte Carlo Localization (DT- NDT-MCL) - is a particle filter based localization method, which simultaneously keeps track of the pose using an apriori known static map and a short-term map. The short-term map is continuously updated and uses Normal Distributions Transform Occupancy maps to maintain the current state of the environment. A key novelty of this approach is that it does not have to select an entire timescale map but rather use the best timescale locally. The approach has real-time performance and is evaluated using three datasets with increasing levels of dynamics. We compare our approach against previously pro- posed NDT-MCL and commonly used SLAM algorithms and show that DT-NDT-MCL outperforms competing algorithms with regards to accuracy in all three test cases.This work has been supported by the European Commission under contract number FP7-ICT-600877 (SPENCER) and by the Spanish Ministry of Economy and Competitiveness under Project DPI-2011-27510 and the EU Project CargoAnts FP7-605598.Peer Reviewe

A 1500-year multiproxy record of coastal hypoxia from the northern Baltic Sea indicates unprecedented deoxygenation over the 20th century

The anthropogenically forced expansion of coastal hypoxia is a major environmental problem affecting coastal ecosystems and biogeochemical cycles throughout the world. The Baltic Sea is a semi-enclosed shelf sea whose central deep basins have been highly prone to deoxygenation during its Holocene history, as shown previously by numerous paleoenvironmental studies. However, long-term data on past fluctuations in the intensity of hypoxia in the coastal zone of the Baltic Sea are largely lacking, despite the significant role of these areas in retaining nutrients derived from the catchment. Here we present a 1500-year multiproxy record of near-bottom water redox changes from the coastal zone of the northern Baltic Sea, encompassing the climatic phases of the Medieval Climate Anomaly (MCA), the Little Ice Age (LIA), and the Modern Warm Period (MoWP). Our reconstruction shows that although multicentennial climate variability has modulated the depositional conditions and delivery of organic matter (OM) to the basin the modern aggravation of coastal hypoxia is unprecedented and, in addition to gradual changes in the basin configuration, it must have been forced by excess human-induced nutrient loading. Alongside the anthropogenic nutrient input, the progressive deoxygenation since the beginning of the 1900s was fueled by the combined effects of gradual shoaling of the basin and warming climate, which amplified sediment focusing and increased the vulnerability to hypoxia. Importantly, the eutrophication of coastal waters in our study area began decades earlier than previously thought, leading to a marked aggravation of hypoxia in the 1950s. We find no evidence of similar anthropogenic forcing during the MCA. These results have implications for the assessment of reference conditions for coastal water quality. Furthermore, this study highlights the need for combined use of sedimentological, ichnological, and geochemical proxies in order to robustly reconstruct subtle redox shifts especially in dynamic, non-euxinic coastal settings with strong seasonal contrasts in the bottom water quality.Peer reviewe

A 1500-year multiproxy record of coastal hypoxia from the northern Baltic Sea indicates unprecedented deoxygenation over the 20th century

The anthropogenically forced expansion of coastal hypoxia is a major environmental problem affecting coastal ecosystems and biogeochemical cycles throughout the world. The Baltic Sea is a semi-enclosed shelf sea whose central deep basins have been highly prone to deoxygenation during its Holocene history, as shown previously by numerous paleoenvironmental studies. However, long-term data on past fluctuations in the intensity of hypoxia in the coastal zone of the Baltic Sea are largely lacking, despite the significant role of these areas in retaining nutrients derived from the catchment. Here we present a 1500-year multiproxy record of near-bottom water redox changes from the coastal zone of the northern Baltic Sea, encompassing the climatic phases of the Medieval Climate Anomaly (MCA), the Little Ice Age (LIA), and the Modern Warm Period (MoWP). Our reconstruction shows that although multicentennial climate variability has modulated the depositional conditions and delivery of organic matter (OM) to the basin the modern aggravation of coastal hypoxia is unprecedented and, in addition to gradual changes in the basin configuration, it must have been forced by excess human-induced nutrient loading. Alongside the anthropogenic nutrient input, the progressive deoxygenation since the beginning of the 1900s was fueled by the combined effects of gradual shoaling of the basin and warming climate, which amplified sediment focusing and increased the vulnerability to hypoxia. Importantly, the eutrophication of coastal waters in our study area began decades earlier than previously thought, leading to a marked aggravation of hypoxia in the 1950s. We find no evidence of similar anthropogenic forcing during the MCA. These results have implications for the assessment of reference conditions for coastal water quality. Furthermore, this study highlights the need for combined use of sedimentological, ichnological, and geochemical proxies in order to robustly reconstruct subtle redox shifts especially in dynamic, non-euxinic coastal settings with strong seasonal contrasts in the bottom water quality.</p

The N-glycome of human embryonic stem cells

<p>Abstract</p> <p>Background</p> <p>Complex carbohydrate structures, glycans, are essential components of glycoproteins, glycolipids, and proteoglycans. While individual glycan structures including the SSEA and Tra antigens are already used to define undifferentiated human embryonic stem cells (hESC), the whole spectrum of stem cell glycans has remained unknown. We undertook a global study of the asparagine-linked glycoprotein glycans (N-glycans) of hESC and their differentiated progeny using MALDI-TOF mass spectrometric and NMR spectroscopic profiling. Structural analyses were performed by specific glycosidase enzymes and mass spectrometric fragmentation analyses.</p> <p>Results</p> <p>The data demonstrated that hESC have a characteristic N-glycome which consists of both a constant part and a variable part that changes during hESC differentiation. hESC-associated N-glycans were downregulated and new structures emerged in the differentiated cells. Previously mouse embryonic stem cells have been associated with complex fucosylation by use of SSEA-1 antibody. In the present study we found that complex fucosylation was the most characteristic glycosylation feature also in undifferentiated hESC. The most abundant complex fucosylated structures were Le^xand H type 2 antennae in sialylated complex-type N-glycans.</p> <p>Conclusion</p> <p>The N-glycan phenotype of hESC was shown to reflect their differentiation stage. During differentiation, hESC-associated N-glycan features were replaced by differentiated cell-associated structures. The results indicated that hESC differentiation stage can be determined by direct analysis of the N-glycan profile. These results provide the first overview of the N-glycan profile of hESC and form the basis for future strategies to target stem cell glycans.</p

Independent Markov Chain Occupancy Grid Maps for Representation of Dynamic Environments

Abstract — In this paper we propose a new grid based approach to model a dynamic environment. Each grid cell is assumed to be an independent Markov chain (iMac) with two states. The state transition parameters are learned online and modeled as two Poisson processes. As a result, our representation not only encodes the expected occupancy of the cell, but also models the expected dynamics within the cell. The paper also presents a strategy based on recency weighting to learn the model parameters from observations that is able to deal with non-stationary cell dynamics. Moreover, an interpretation of the model parameters with discussion about the convergence rates of the cells is presented. The proposed model is experimentally validated using offline data recorded with a Laser Guided Vehicle (LGV) system running in production use. I

Conditional Transition Maps: Learning Motion Patterns in Dynamic Environments

Abstract — In this paper we introduce a method for learning motion patterns in dynamic environments. Representations of dynamic environments have recently received an increasing amount of attention in the research community. Understanding dynamic environments is seen as one of the key challenges in order to enable autonomous navigation in real-world scenarios. However, representing the temporal dimension in a representative manner, is a challenge yet to be solved. In this paper we introduce a spatial representation, which encapsulates the statistical dynamic behavior observed in the environment. The proposed Conditional Transition Map (CTMap) is a grid-based representation that associates a probability distribution for an object exiting the cell, given its entry direction. The transition parameters are learned from a temporal signal of occupancy on cells by using a local-neighborhood cross-correlation method. In this paper, we introduce the CTMap, the learning approach and present a proof-of-concept method for estimating future paths of dynamic objects, called Conditional Probability Propagation Tree (CPPTree). The evaluation is done using a real-world dataset collected at a busy roundabout. I