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Major advances over the past decade in the field of ancient DNA are providing access to past paleogenomic diversity, but the diverse functions and biosynthetic capabilities of this growing paleome remain largely elusive. We investigated the dental calculus of 12 Neanderthals and 52 anatomically modern humans ranging from 100,000 years ago to the present and reconstructed 459 bacterial metagenome-assembled genomes. We identified a biosynthetic gene cluster shared by seven Middle and Upper Paleolithic individuals that allows for the heterologous production of a class of previously unknown metabolites that we name “paleofurans.” This paleobiotechnological approach demonstrates that viable biosynthetic machinery can be produced from the preserved genetic material of ancient organisms, allowing access to natural products from the Pleistocene and providing a promising area for natural product exploration.

The increasing importance of integrated fulfillment concepts revitalizes bricks-and-mortar stores and puts them at the center of retail operations. So-called omnichannel (OC) concepts leverage stores to offer seamless and enhanced operations for offline and online shoppers. Stores are used to fulfill online orders, offer shorter lead times to customers, and extend the assortment across channels. The role of the store and the underlying store operations are thus impacted by profound changes. This transformation has not yet been assessed comprehensively from a practical or an Operations Research (OR) lens. This paper identifies cross-cutting store-related planning issues and develops a planning framework for OC operations. We apply industry interviews and a systematic literature analysis to derive five planning issues. Research gaps are revealed by matching the pertinent OR literature with managerial needs. The planning issues network design of fulfillment locations, assignment of customer orders, and assortment and inventory planning have been discussed in several store-related OC publications. Demand forecasting and inventory replenishment have received less coverage, and offer significant research opportunities.

Nowadays the majority of retail customers use multiple channels. We investigate the assortment, space and inventory problem for an omni-channel retailer operating with interconnected bricks-and-mortar stores and an online shop. For this problem it becomes essential to consider customers’ demand interactions across channels. Current literature mainly focuses on single-channel assortments and ignores cross-channel substitution. We contribute the first integrated omni-channel model that determines assortments for the online and bricks-and-mortar channel with stochastic, space-elastic and out-of-assortment and out-of-stock demand both for in-channel substitution and cross-channel substitution. A specialized heuristic is developed that is based on an iterative solution of a binary problem and demand updates. Our approach achieves near-optimal results for small instances and higher objective values as an alternative heuristic for larger instances. With the full integration of channels, omni-channel retailers can realize a profit increase that mainly depends on the magnitude of substitution rates. We further show numerically that in-channel substitution has a stronger impact on profits than cross-channel substitution when costs are equal across channels.

Previous research indicates that the human genetic diversity found in Wallacea - islands in present-day Eastern Indonesia and Timor-Leste that were never part of the Sunda or Sahul continental shelves - has been shaped by complex interactions between migrating Austronesian farmers and indigenous hunter-gatherer communities. Here, we provide new insights into this region’s demographic history based on genome-wide data from 16 ancient individuals (2600-250 yrs BP) from islands of the North Moluccas, Sulawesi, and East Nusa Tenggara. While the ancestry of individuals from the northern islands fit earlier views of contact between groups related to the Austronesian expansion and the first colonization of Sahul, the ancestry of individuals from the southern islands revealed additional contributions from Mainland Southeast Asia, which seems to predate the Austronesian admixture in the region. Admixture time estimates for the oldest individuals of Wallacea are closerto archaeological estimates for the Austronesian arrival into the region than are admixture time estimates for present-day groups. The decreasing trend in admixture times exhibited by younger individuals supports a scenario of multiple or continuous admixture involving Papuan- and Asian-related groups. Our results clarify previously debated times of admixture and suggest that the Neolithic dispersals into Island Southeast Asia are associated with the spread of multiple genetic ancestries.

Efficiently managing retail space is critical as the increase in product variety is in conflict with limited shelf space and instore replenishment constraints. This paper develops a general framework for retail space management and presents a decision support model with the related problems within the framework of optimizing assortment, shelf space assignment and replenishment. An integrative approach to these planning problems becomes particularly relevant for fast-moving consumer goods and groceries, where stores are regularly replenished from distribution centers. The planning problem at hand is a multi-product shelf space allocation problem where demand is a composite function of the shelf space allocated and assortment-related demand substitution, and actual replenishment practices from retail are incorporated. The model developed extends existing models of shelf space management by jointly considering space-elastic demand and assortment-based substitution and integrating restocking constraints. For the latter, we consider real-world replenishment processes of retailers that distinguish between period-based and ad-hoc replenishment from the backroom. We develop three solution approaches that are based on efficient pre-processing and a nonlinear binary integer programming formulation of the problem. The computation tests based on retail data show the efficiency of the solution approaches in terms of computation time and solution quality. We reveal the improvement in profit levels that can be achieved from integrating assortments, shelf space planning and replenishment where challenges arise in obtaining feasible solutions with limited shelf space and replenishment constraints. We also use sensitivity analyses to demonstrate the high impact of replenishment constraints on profits and solution structures.

Retail shelves are adjustable by varying the number of shelf boards as well as the height and depth of each shelf board. Shelf planners adjust the boards accordingly at regular intervals when they create the shelf plans and allocate products. Current shelf planning models assume given shelf configurations and allocate only products. However, the dimensioning of a shelf segment and product allocation are interdependent. For instance, the height of one segment may be reduced if only small products are allocated or products cannot be stacked. This paper proposes the first integrated approach for shelf segment dimensioning and product allocation. It jointly determines the number of facings for each product, the shelf quantity and the size and number of shelf segments. We also identify and consider several restrictions for the shelf structure (e.g., technical options), allocation rules (e.g., maximum inventory reach) and allocation- and shelf-layout-dependent demand. We formulate the decision problem at hand which is an Integer Non-linear Program and apply a solution algorithm based on the application of bounds that are obtained by transferring constraints to a preprocessing stage. Doing so, we can reformulate the problem as Binary Integer Program, provide an exact approach and generate practical applicable and optimal solutions in a time-efficient manner. We show that integrating shelf dimensioning into product allocation results in up to 5% higher profits than benchmarks available in literature. By means of a case study we show how planning can be improved, and that the retailer’s profit margin can be improved by up to 7%.

Omnichannel retailing and digitalization result in considerable challenges for the management and optimization of retail operations. The continued demand of quantitative insights, their practical need, and the growing availability of data motivates an increasing number of scientists and practitioners to intensify research on demand and supply-related issues in retailing. This featured cluster provides the state-of-the art literature on forecasting and digitalization technologies, channel structures and delivery concepts as well as logistics in omnichannel and online retailing. The featured cluster contains 17 articles that deal with such topics.

The retail shelf space planning problem has long been addressed by Marketing and Operations Research (OR) professionals and researchers, with the first empirical studies tracing back to the 1960s and the first modelling approaches back to the 1970s. Due to this long history, this field presents a wide range of different mathematical modelling approaches that deal with the decisions surrounding a set of products and not only define their space assignment and related quantity, but also their vertical and horizontal positioning within a retail shelf. These decisions affect customer demand, namely in the form of space- and position-dependent demand and replenishment requirements. Current literature provides either more comprehensive decision models with a wide range of demand effects but limited practical applicability, or more simplistic model formulations with greater practical application but limited consideration of the associated demand. Despite the recent progress seen in this research area, no work has yet systematised published research with a clear focus on shelf space planning. As a result, there is neither any up-to-date structured literature nor a unique model approach, and no benchmark sets are available. This paper provides a description and a state-of-the-art literature review of this problem, focusing on optimisation models. Based on this review, a classification framework is proposed to systematise the research into a set of sub-problems, followed by a unified approach with a univocal notation of model classes. Future lines of research point to the most promising open questions in this field.

This paper addresses the shelf-space and replenishment problem where a retailer must decide how much shelf space to assign to items and how frequently to replenish retail shelves. Since space is limited, these two decisions are interdependent. Existing optimization models approximate shelf sizes by reducing the shelf to a one-dimensional space. However, in practice shelf sizes and their shelf segments vary in width, height and depth. Without considering the actual shelf dimensions, results from optimization models cannot be transferred to actual shelves. To account for these constraints, we propose the first shelf-space model capable of modeling three-dimensional shelf characteristics. Our model determines optimal shelf quantities, an item’s optimal shelf segment, as well as optimal replenishment frequencies. To do this we include a space-elastic function that impacts the customer demand for an item depending on its allocated shelf space. We also consider different shelf segments in different positions. To solve the non-linear integer problem (NLIP), we develop an optimal solution approach based on precalculations to transfer the model into a binary-integer problem that considers logical item-specific bounds to significantly reduce computation time. We conduct various numerical analyses. First, we show that results are generated time-efficiently, even for large-scale data instances. We then analyze the extent to which the stylized assumption of a one-dimensional space results in non-feasible solutions in practice, thus underlining the necessity of our multi-dimensional model. Finally, the results of a field test in which model solutions were actually applied in practice show how one of the largest German grocery retailers was able to increase its profits across different categories and stores by 9.1% on average compared to a control group by using the approach.

The bacterial pathogen Yersinia pestis gave rise to devastating outbreaks throughout human history, and ancient DNA evidence has shown it afflicted human populations as far back as the Neolithic. Y. pestis genomes recovered from the Eurasian Late Neolithic/Early Bronze Age (LNBA) period have uncovered key evolutionary steps that led to its emergence from a Yersinia pseudotuberculosis-like progenitor; however, the number of reconstructed LNBA genomes are too few to explore its diversity during this critical period of development. Here, we present 17 Y. pestis genomes dating to 5,000 to 2,500 y BP from a wide geographic expanse across Eurasia. This increased dataset enabled us to explore correlations between temporal, geographical, and genetic distance. Our results suggest a nonflea-adapted and potentially extinct single lineage that persisted over millennia without significant parallel diversification, accompanied by rapid dispersal across continents throughout this period, a trend not observed in other pathogens for which ancient genomes are available. A stepwise pattern of gene loss provides further clues on its early evolution and potential adaptation. We also discover the presence of the flea-adapted form of Y. pestis in Bronze Age Iberia, previously only identified in in the Caucasus and the Volga regions, suggesting a much wider geographic spread of this form of Y. pestis. Together, these data reveal the dynamic nature of plague’s formative years in terms of its early evolution and ecology.

[Significance] The bacterium Yersinia pestis has caused numerous historically documented outbreaks of plague and research using ancient DNA could demonstrate that it already affected human populations during the Neolithic. However, the pathogen’s genetic diversity, geographic spread, and transmission dynamics during this early period of Y. pestis evolution are largely unexplored. Here, we describe a set of ancient plague genomes up to 5,000 y old from across Eurasia. Our data demonstrate that two genetically distinct forms of Y. pestis evolved in parallel and were both distributed across vast geographic distances, potentially occupying different ecological niches. Interpreted within the archeological context, our results suggest that the spread of plague during this period was linked to increased human mobility and intensification of animal husbandry., The bacterial pathogen Yersinia pestis gave rise to devastating outbreaks throughout human history, and ancient DNA evidence has shown it afflicted human populations as far back as the Neolithic. Y. pestis genomes recovered from the Eurasian Late Neolithic/Early Bronze Age (LNBA) period have uncovered key evolutionary steps that led to its emergence from a Yersinia pseudotuberculosis-like progenitor; however, the number of reconstructed LNBA genomes are too few to explore its diversity during this critical period of development. Here, we present 17 Y. pestis genomes dating to 5,000 to 2,500 y BP from a wide geographic expanse across Eurasia. This increased dataset enabled us to explore correlations between temporal, geographical, and genetic distance. Our results suggest a nonflea-adapted and potentially extinct single lineage that persisted over millennia without significant parallel diversification, accompanied by rapid dispersal across continents throughout this period, a trend not observed in other pathogens for which ancient genomes are available. A stepwise pattern of gene loss provides further clues on its early evolution and potential adaptation. We also discover the presence of the flea-adapted form of Y. pestis in Bronze Age Iberia, previously only identified in in the Caucasus and the Volga regions, suggesting a much wider geographic spread of this form of Y. pestis. Together, these data reveal the dynamic nature of plague’s formative years in terms of its early evolution and ecology., This study was funded by the Max Planck Society, Max Planck Harvard Research Center for the Archaeoscience of the Ancient Mediterranean and the European Research Council under the European Union’s Horizon 2020 research and innovation program under Grant Agreement 771234 – PALEoRIDER (to W.H.), 856453 – HistoGenes (to J.K.), and 834616 – ARCHCAUCASUS (to S.H.). The Heidelberg Academy of Science financed the genetic and archeological research on human individuals from the Augsburg region within the project WIN Kolleg: “Times of Upheaval: Changes of Society and Landscape at the Beginning of the Bronze Age. M.E. was supported by the award “Praemium Academiae” of the Czech Academy of Sciences. M.D. was supported by the project RVO 67985912 of the Institute of Archaeology of the Czech Academy of Sciences, Prague. I.O. was supported by the Ramón y Cajal grant from Ministerio de Ciencia e Innovación, Spanish Government (RYC2019-027909-I). A. H€ubner was supported by the Deutsche Forschungsgemeinschaft under Germany’s Excellence Strategy (EXC 2051 – Project-ID 390713860). J.F.-E. and J.A.M.-A. were supported by the Diputación Foral de Alava, IT 1223-19, Gobierno Vasco. A. Buzhilova was supported by the Center of Information Technologies and Systems (CITIS), Moscow, Russia 121041500329-0. L. M., L.B.D., and E. Khussainova were supported by the Grant AP08856654, Ministry of Education and Science of the Republic of Kazakhstan. A. Beisenov was supported by the Grant AP08857177, Ministry of Education and Science of the Republic of Kazakhstan.

Previous research indicates that the human genetic diversity found in Wallacea - islands in present-day Eastern Indonesia and Timor-Leste that were never part of the Sunda or Sahul continental shelves - has been shaped by complex interactions between migrating Austronesian farmers and indigenous hunter-gatherer communities. Here, we provide new insights into this region’s demographic history based on genome-wide data from 16 ancient individuals (2600-250 yrs BP) from islands of the North Moluccas, Sulawesi, and East Nusa Tenggara. While the ancestry of individuals from the northern islands fit earlier views of contact between groups related to the Austronesian expansion and the first colonization of Sahul, the ancestry of individuals from the southern islands revealed additional contributions from Mainland Southeast Asia, which seems to predate the Austronesian admixture in the region. Admixture time estimates for the oldest individuals of Wallacea are closer to archaeological estimates for the Austronesian arrival into the region than are admixture time estimates for present-day groups. The decreasing trend in admixture times exhibited by younger individuals supports a scenario of multiple or continuous admixture involving Papuan- and Asian-related groups. Our results clarify previously debated times of admixture and suggest that the Neolithic dispersals into Island Southeast Asia are associated with the spread of multiple genetic ancestries.

Besides fuel and waste distribution, one core application of multi-compartment vehicles (MCVs) is the distribution of groceries, as they enable retailers to jointly transport products with different temperature requirements, thus reducing the number of visits to a store. Grocery stores usually define preferable time windows that depend on the temperature of products (for example, fresh products in the morning) to indicate when deliveries should occur to better plan their in-store operations. Distribution planning therefore needs to take these preferences into consideration to obtain consistent delivery times. This work extends the research on multi-compartment vehicle routing problems (MCVRPs) by tackling a multi-period setting with a product-oriented time window assignment. In this problem, a fleet of MCVs is used for distribution and a unique time window for the delivery of each product segment to each store is defined consistently throughout the planning horizon. An ALNS is proposed to solve the product-oriented time window assignment for MCVRP. Daily and weekly operators are developed respectively focusing on the improvement of routing aspects of the problem on each day and aligning the time window assignment consistently throughout the planning horizon. The approach is tested on benchmark instances from the literature to demonstrate its effectiveness. We also use direct information from retail practice and enhance this with simulated data to further generalize our findings. The numerical experiments demonstrate that planning consistent MCV distribution leads to better overall solutions than the ex-post time window assignment of daily plans, facilitating more on-time deliveries.

DNA de novo assembly can be used to reconstruct longer stretches of DNA (contigs), including genes and even genomes, from short DNA sequencing reads. Applying this technique to metagenomic data derived from archaeological remains, such as paleofeces and dental calculus, we can investigate past microbiome functional diversity that may be absent or underrepresented in the modern microbiome gene catalogue. However, compared to modern samples, ancient samples are often burdened with environmental contamination, resulting in metagenomic datasets that represent mixtures of ancient and modern DNA. The ability to rapidly and reliably establish the authenticity and integrity of ancient samples is essential for ancient DNA studies, and the ability to distinguish between ancient and modern sequences is particularly important for ancient microbiome studies. Characteristic patterns of ancient DNA damage, namely DNA fragmentation and cytosine deamination (observed as C-to-T transitions) are typically used to authenticate ancient samples and sequences, but existing tools for inspecting and filtering aDNA damage either compute it at the read level, which leads to high data loss and lower quality when used in combination with de novo assembly, or require manual inspection, which is impractical for ancient assemblies that typically contain tens to hundreds of thousands of contigs. To address these challenges, we designed PyDamage, a robust, automated approach for aDNA damage estimation and authentication of de novo assembled aDNA. PyDamage uses a likelihood ratio based approach to discriminate between truly ancient contigs and contigs originating from modern contamination. We test PyDamage on both on simulated aDNA data and archaeological paleofeces, and we demonstrate its ability to reliably and automatically identify contigs bearing DNA damage characteristic of aDNA. Coupled with aDNA de novo assembly, Pydamage opens up new doors to explore functional diversity in ancient metagenomic datasets.

Modern humans appeared in Europe by at least 45,000 years ago1–5, but the extent of their interactions with Neanderthals, who disappeared by about 40,000 years ago6, and their relationship to the broader expansion of modern humans outside Africa are poorly understood. Here we present genome-wide data from three individuals dated to between 45,930 and 42,580 years ago from Bacho Kiro Cave, Bulgaria1,2. They are the earliest Late Pleistocene modern humans known to have been recovered in Europe so far, and were found in association with an Initial Upper Palaeolithic artefact assemblage. Unlike two previously studied individuals of similar ages from Romania7 and Siberia8 who did not contribute detectably to later populations, these individuals are more closely related to present-day and ancient populations in East Asia and the Americas than to later west Eurasian populations. This indicates that they belonged to a modern human migration into Europe that was not previously known from the genetic record, and provides evidence that there was at least some continuity between the earliest modern humans in Europe and later people in Eurasia. Moreover, we find that all three individuals had Neanderthal ancestors a few generations back in their family history, confirming that the first European modern humans mixed with Neanderthals and suggesting that such mixing could have been common., Genome-wide data for the three oldest known modern human remains in Europe, dated to around 45,000 years ago, shed light on early human migrations in Europe and suggest that mixing with Neanderthals was more common than is often assumed.

This paper develops a multi-objective decision support model for solving the patient bed assignment problem. Assigning inpatients to hospital beds impacts patient satisfaction and the workload of nurses and doctors. The assignment is subject to unknown patient arrivals and lengths of stay, in particular for emergency patients. Hospitals therefore need to deal with uncertainty on actual bed requirements and potential shortage situations as bed capacities are limited. This paper contributes by improving the anticipation of emergency patients using machine learning (ML) approaches, incorporating weather data, time and dates, important local and regional events, as well as current and historical occupancy levels. Drawing on real-life data from a large case hospital, we were able to improve forecasting accuracy for emergency inpatient arrivals. We achieved an up to 17% better root mean square error when using ML methods compared to a baseline approach relying on averages for historical arrival rates. Second, we develop a new hyper-heuristic for solving real-life problem instances based on the pilot method and a specialized greedy look-ahead heuristic. When applying the hyper-heuristic in test sets we were able to increase the objective function by up to 3% in a single problem instance and up to 4% in a time series analysis compared to current approaches in literature. We achieved an improvement of up to 2.2% compared to a baseline approach from literature by combining the emergency patient admission forecasting and the hyper-heuristic on real-life situations.

Among the many extensions of the classical capacitated vehicle routing problem, multi-compartment vehicle routing problems have been studied extensively only in recent years. Vehicles with multiple compartments enable the joint delivery or collection of goods with differing characteristics in separate compartments that would otherwise need separate transportation with single-compartment vehicles. This enables greater flexibility in routing decisions and order assignment to tours. The versatile use of these vehicles is leading to increasing relevance in both research and industry, and consequently in an increasing number of related publications. The available studies, however, consider substantially different problem variants. As no survey on multi-compartment vehicle routing problems is available so far, the identification of common problem features and research opportunities has been difficult. This paper aims at overcoming this difficulty by proposing an extended typology for multi-compartment vehicle routing problems and extensively reviewing the existing literature. Although only few identical problems can be identified, common attributes among similar applications (regarding compartment flexibility, for example) are observed. Suggestions for future research directions are also proposed.

Loss of gut microbial diversity1–6 in industrial populations is associated with chronic diseases7, underscoring the importance of studying our ancestral gut microbiome. However, relatively little is known about the composition of pre-industrial gut microbiomes. Here we performed a large-scale de novo assembly of microbial genomes from palaeofaeces. From eight authenticated human palaeofaeces samples (1,000–2,000 years old) with well-preserved DNA from southwestern USA and Mexico, we reconstructed 498 medium- and high-quality microbial genomes. Among the 181 genomes with the strongest evidence of being ancient and of human gut origin, 39% represent previously undescribed species-level genome bins. Tip dating suggests an approximate diversification timeline for the key human symbiont Methanobrevibacter smithii. In comparison to 789 present-day human gut microbiome samples from eight countries, the palaeofaeces samples are more similar to non-industrialized than industrialized human gut microbiomes. Functional profiling of the palaeofaeces samples reveals a markedly lower abundance of antibiotic-resistance and mucin-degrading genes, as well as enrichment of mobile genetic elements relative to industrial gut microbiomes. This study facilitates the discovery and characterization of previously undescribed gut microorganisms from ancient microbiomes and the investigation of the evolutionary history of the human gut microbiota through genome reconstruction from palaeofaeces., Ancient microbiomes from palaeofaeces are more similar to non-industrialized than industrialized human gut microbiomes regardless of geography, but 39% of their de novo reconstructed genomes represent previously undescribed microbial species.

Innovative last-mile logistics solutions are needed to reduce delivery costs, traffic congestion, and pollution in cities. A promising concept in this context are truck-and-robot systems, as they enable significant cost and traffic reduction compared to classic truck deliveries. The system relies on small autonomous delivery robots to cover the last meters to a customer. Existing truck-and-robot concepts to date consider home deliveries by robots, while trucks are only used to transport robots and not for deliveries. This assumption disregards the fact that regular truck deliveries are still needed for some delivery requests, such as for the delivery of bulky items, or for customers who do not accept robots. Our research addresses this issue and proposes a mixed truck and robot delivery concept in which both robots and the delivery truck can visit customers. Our tailored solution approach is based on a General Variable Neighborhood Search that efficiently solves the routing problem and outperforms existing truck-and-robot routing algorithms. The numerical experiments show that this approach enables cost reductions of up to 43% compared to classical truck deliveries and up to 22% compared to a truck-and-robot system that does not allow deliveries by both truck and robots on the same tour. Further analyses reveal additional benefits of such mixed tours and the robustness of our approach for different problem settings.

Significance We know more about the settlement of Polynesia than we do about the settlement of the Mariana Islands in the western Pacific. There is debate over where people came from to get to the Marianas, with various lines of evidence pointing to the Philippines, Indonesia, New Guinea, or the Bismarck Archipelago, and over how the ancestors of the present Mariana Islanders, the Chamorro, might be related to Polynesians. We analyzed ancient DNA from Guam from two skeletons dating to ∼2,200 y ago and found that their ancestry is linked to the Philippines. Moreover, they are closely related to early Lapita skeletons from Vanuatu and Tonga, suggesting that the early Mariana Islanders may have been involved in the colonization of Polynesia., Humans reached the Mariana Islands in the western Pacific by ∼3,500 y ago, contemporaneous with or even earlier than the initial peopling of Polynesia. They crossed more than 2,000 km of open ocean to get there, whereas voyages of similar length did not occur anywhere else until more than 2,000 y later. Yet, the settlement of Polynesia has received far more attention than the settlement of the Marianas. There is uncertainty over both the origin of the first colonizers of the Marianas (with different lines of evidence suggesting variously the Philippines, Indonesia, New Guinea, or the Bismarck Archipelago) as well as what, if any, relationship they might have had with the first colonizers of Polynesia. To address these questions, we obtained ancient DNA data from two skeletons from the Ritidian Beach Cave Site in northern Guam, dating to ∼2,200 y ago. Analyses of complete mitochondrial DNA genome sequences and genome-wide SNP data strongly support ancestry from the Philippines, in agreement with some interpretations of the linguistic and archaeological evidence, but in contradiction to results based on computer simulations of sea voyaging. We also find a close link between the ancient Guam skeletons and early Lapita individuals from Vanuatu and Tonga, suggesting that the Marianas and Polynesia were colonized from the same source population, and raising the possibility that the Marianas played a role in the eventual settlement of Polynesia.

Managing patient to bed allocations is an everyday task in hospitals which in recent years has moved into focus due to a general rise in occupancy levels and the resulting need to efficiently manage tight hospital bed-capacities. This holds true especially when being faced with high volatility and uncertainty regarding patient arrivals and lengths of stay. In our work with a large German hospital we identified three main stakeholders, namely patients, nurses, and doctors, whose individual objectives and constraints regarding patient-bed allocation (PBA) lead to a potential trade-off situation. We developed a decision support model that tackles the PBA problem considering this trade-off, while also being capable of handling overflow situations. In addition, we anticipate emergency patient arrivals based on historical probability distributions and account for uncertainty regarding patient arrival and discharge dates. We develop a greedy look-ahead heuristic which allows for generating solutions for large real-life operational planning situations involving high ratios of emergency patients. We demonstrate the performance of our heuristic approach by comparison with the results of a near-optimal solution achieved by Gurobi’s MIP solver. Finally, we tested our approach using data sets from the literature as well as actual clinic data from our case study hospital, for which we were able to reduce overflow by over 96% while increasing overall utilization by 5%.

Zunehmende Sortimentsbreiten sowie sinkende Flächenproduktivität zwingen Einzelhändler dazu, den verfügbaren Regalplatz optimal zu nutzen. In diese Planung des Regalplatzes für jedes Produkt geht eine Vielzahl von Faktoren ein. Neben Produktmargen ist dies vor allem die Kundennachfrage, deren Kenntnis essentiell für ein optimal gestaltetes Regal ist. Dieser Beitrag führt in das Thema der Regalplatzplanung ein und stellt ein Optimierungsmodell vor, das Einzelhändler bei der Regalplanung unterstützt.

Händler wollen für jedes Produkt die im Regal zu platzierende Mengen optimal bestimmen, da dies Einfluss auf die Kundennachfrage und die operativen Kosten in der Filiale hat. Zur bestmöglichen Ausnutzung des verfügbaren Regalplatzes in den Filialen sind Einzelhändler daher auf Entscheidungsunterstützungssysteme angewiesen. In diesem Beitrag wird ein Fallstudie und ein Vorgehen zur Implementierung und Lösung von Regalplatzoptimierungsmodellen vorgestellt.

Shotgun metagenomics applied to archaeological feces (paleofeces) can bring new insights into the composition and functions of human and animal gut microbiota from the past. However, paleofeces often undergo physical distortions in archaeological sediments, making their source species difficult to identify on the basis of fecal morphology or microscopic features alone. Here we present a reproducible and scalable pipeline using both host and microbial DNA to infer the host source of fecal material. We apply this pipeline to newly sequenced archaeological specimens and show that we are able to distinguish morphologically similar human and canine paleofeces, as well as non-fecal sediments, from a range of archaeological contexts. This work was supported by the US National Institutes of Health R01GM089886 (to Christina Warinner and Cecil Lewis), the Deutsche Forschungsgemeinschaft EXC 2051 #390713860 (to Christina Warinner), and the Max Planck Society. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.