Linear Programming for a Cutting Problem in the Wood Processing Industry – A Case Study

In this paper the authors present a case study from the wood-processing industry. It focuses on a cutting process in which material from stock is cut down in order to provide the items required by the customers in the desired qualities, sizes, and quantities. In particular, two aspects make this cutting process special. Firstly, the cutting process is strongly interdependent with a preceding handling process, which, consequently, cannot be planned independently. Secondly, if the trim loss is of a certain minimum size, it can be returned into stock and used as input to subsequent cutting processes. In order to reduce the cost of the cutting process, a decision support tool has been developed which incorporates a linear programming model as a central feature. The model is described in detail, and experience from the application of the tool is reported.one-dimensional cutting, linear programming, wood-processing industry

A Grouping Genetic Algorithm for the Order Batching Problem in Distribution Warehouses

Order picking is a warehouse function that deals with the retrieval of articles from their storage locations in order to satisfy certain customer demands. Combining several single customer orders into one (more substantial) picking order can increase the efficiency of warehouse operations. The Order Batching Problem considered in this paper deals with the question of how different customer orders should be grouped into picking orders, such that the total length of all tours through the warehouse is minimized, which are necessary to collect all requested articles. For the solution of this problem, the authors introduce a Grouping Genetic Algorithm. This genetic algorithm is combined with a local search procedure which results in a highly competitive hybrid algorithm. In a series of extensive numerical experiments, the algorithm is benchmarked against a genetic algorithm with a standard item-oriented encoding scheme. The results show that the new genetic algorithm based on the group-oriented encoding scheme is preferable for the Order Batching Problem, and that the algorithm provides high quality solutions in reasonable computing times

Metaheuristics for the Order Batching Problem in Manual Order Picking Systems

In manual order picking systems, order pickers walk or drive through a distribution warehouse in order to collect items which are requested by (internal or external) customers. In order to perform these operations effciently, it is usually required that customer orders are combined into (more substantial) picking orders of limited size. The Order Batching Problem considered in this paper deals with the question of how a given set of customer orders should be combined such that the total length of all tours is minimized which are necessary to collect all items. The authors introduce two metaheuristic approaches for the solution of this problem; the rst one is based on Iterated Local Search, the second one on Ant Colony Optimization. In a series of extensive numerical experiments, the newly developed approaches are benchmarked against classic solution methods. It is demonstrated that the proposed methods are not only superior to existing methods, but provide solutions which may allow for operating distribution warehouses signicantly more effcient.Warehouse Management, Order Picking, Order Batching, Iterated Local Search, Ant Colony Optimization

A Grouping Genetic Algorithm for the Order Batching Problem in Distribution Warehouses

Order picking is a warehouse function that deals with the retrieval of articles from their storage locations in order to satisfy certain customer demands. Combining several single customer orders into one (more substantial) picking order can increase the efficiency of warehouse operations. The Order Batching Problem considered in this paper deals with the question of how different customer orders should be grouped into picking orders, such that the total length of all tours through the warehouse is minimized, which are necessary to collect all requested articles. For the solution of this problem, the authors introduce a Grouping Genetic Algorithm. This genetic algorithm is combined with a local search procedure which results in a highly competitive hybrid algorithm. In a series of extensive numerical experiments, the algorithm is benchmarked against a genetic algorithm with a standard item-oriented encoding scheme. The results show that the new genetic algorithm based on the group-oriented encoding scheme is preferable for the Order Batching Problem, and that the algorithm provides high quality solutions in reasonable computing times.Warehouse Management, Order Picking, Order Batching, Genetic Algorithms

Order Batching in Order Picking Warehouses: A Survey of Solution Approaches

Order picking is a warehouse function dealing with the retrieval of articles from their storage location in order to satisfy a given demand specified by customer orders. Of all warehouse operations, order picking is considered to include the most cost-intensive ones. Even though there have been different attempts to automate the picking process, manual order picking systems are still prevalent in practice. This article will focus on order batching, one of the main planning issues in order picking systems. Order Batching has been proven to be pivotal for the efficiency of order picking operations. With respect to the availability of information about the customer orders, order batching can be distinguished into static batching and dynamic batching. Improved order batching reduces the total picking time required to collect the requested articles. According to experience from practice, this can result in significant savings of labor cost and into a reduction of the customer order\u27s delivery lead time.The aim of this contribution is to provide comprehensive insights into order batching by giving a detailed state-of-the-art overview of the different solution approaches which have been suggested in the literature. Corresponding to the available publications, the emphasis will be on static order batching.In addition to this, the paper will also review the existing literature for variants and extensions of static order batching (e.g. due dates, alternative objective functions). Furthermore, solution approaches for dynamic order batching problems (like time window batching) will be presented

Linear Programming for a Cutting Problem in the Wood Processing Industry: A Case Study

In this paper the authors present a case study from the wood-processing industry. It focuses on a cutting process in which material from stock is cut down in order to provide the items required by the customers in the desired qualities, sizes, and quantities. In particular, two aspects make this cutting process special. Firstly, the cutting process is strongly interdependent with a preceding handling process, which, consequently, cannot be planned independently. Secondly, if the trim loss is of a certain minimum size, it can be returned into stock and used as input to subsequent cutting processes. In order to reduce the cost of the cutting process, a decision support tool has been developed which incorporates a linear programming model as a central feature. The model is described in detail, and experience from the application of the tool is reported

Bedeutung des Adipokins CTRP3 für das Fettgewebe und die Entwicklung der Atherosklerose

Atherosklerotisch bedingte Folgeerkrankungen gehören zu den häufigsten Todesursachen in den Industrienationen und sind stark mit dem metabolischen Syndrom assoziiert. Infolge von Überernährung kommt es zu einer Hypertrophie und einer Inflammation des Fettgewebes, zu einer Dysbalance der sekretierten Adipokine und damit zur Induktion von systemisch metabolischen Veränderungen. CTRP3 ist ein Adipokin mit überwiegend antiinflammatorischer Wirkung. Erniedrigte CTRP3-Spiegel sind mit metabolischen und kardiovaskulären Erkrankungen sowie einer erhöhten Mortalität assoziiert. Die Bedeutung von CTRP3 in Bezug auf die Entstehung dieser Erkrankungen in vivo ist jedoch bisher experimentell wenig erforscht. Um Aufschluss über die Funktion von CTRP3 zu erhalten, wurden Mäuse mit adipozytenspezifischem CTRP3-KO nach Fütterung mit einer fett- und cholesterinreichen Diät untersucht und mit einer Kontrollgruppe ohne CTRP3-KO verglichen. Die CTRP3-Defizienz führte in den ersten 6 Wochen der Fütterung zu einer beschleunigten Gewichtszunahme bei erhöhtem Körperfettanteil und war mit einer verstärkten Hypertrophie der Adipozyten sowie einer erhöhten Infiltration von Makrophagen in das Fettgewebe assoziiert. Nach 12 Wochen zeigten sich diese Effekte nicht mehr. Im Plasma konnten erhöhte Konzentrationen von VLDL und Gesamtcholesterin nachgewiesen werden. Nach 12 Wochen waren keine dieser Veränderungen mehr nachweisbar. Ein Einfluss der CTRP3-Defizienz auf die Nüchternglukose, die Glukosetoleranz und die Insulinsensitivität ließ sich nicht nachweisen. Sowohl nach 6 als auch nach 12 Wochen konnte eine erhöhte Plaquebelastung in der Aorta detektiert werden, begleitet von einer erhöhten Akkumulation von Lipiden in der Aortenwurzel und einer erhöhten Infiltration von Makrophagen in den Plaques nach 12 Wochen. Ein erhöhtes Vorkommen von nekrotischen Kernen zeigte sich nicht. Neben den Untersuchungen an CTRP3-KO-Mäusen wurde zudem die Genexpression von CTRP3 in verschiedenen Organen von WT-Mäusen analysiert. Von den getesteten Organen wies die Aorta die größte Expression von CTRP3-mRNA auf. In weiterführenden Arbeiten sollte ein Screening auf systemische Inflammationsmarker erfolgen und die Reversibilität der beobachteten Veränderungen durch Applikation von rekombinantem CTRP3 geprüft werden. Das Angleichen im metabolischen Phänotyp nach 12 Wochen Fütterung ist möglicherweise auf eine kompensatorisch reduzierte Nahrungsaufnahme der CTRP3- KO-Mäuse oder aber auf eine sekundär erworbene Verminderung beziehungsweise ein Funktionsverlust von CTRP3 in CTRP3-WT-Mäusen zurückzuführen und erfordert weitere Analysen. Obwohl CTRP3 hauptsächlich als Adipokin gilt, scheint CTRP3 auch in anderen Organen gebildet zu werden. Hinsichtlich der Atherosklerose bedarf insbesondere die Expression von CTRP3 in der Aorta weiterer Aufklärung. Zusammenfassend zeigt diese Arbeit, dass die adipozytenspezifische Defizienz des Adipokins CTRP3 zur Hypertrophie und Inflammation des Fettgewebes, zu erhöhten Konzentrationen von Cholesterin im Plasma sowie zur Entstehung von Atherosklerose beiträgt

Augmenting Biogas Process Modeling by Resolving Intracellular Metabolic Activity

The process of anaerobic digestion in which waste biomass is transformed to methane by complex microbial communities has been modeled for more than 16 years by parametric gray box approaches that simplify process biology and do not resolve intracellular microbial activity. Information on such activity, however, has become available in unprecedented detail by recent experimental advances in metatranscriptomics and metaproteomics. The inclusion of such data could lead to more powerful process models of anaerobic digestion that more faithfully represent the activity of microbial communities. We augmented the Anaerobic Digestion Model No. 1 (ADM1) as the standard kinetic model of anaerobic digestion by coupling it to Flux-Balance-Analysis (FBA) models of methanogenic species. Steady-state results of coupled models are comparable to standard ADM1 simulations if the energy demand for non-growth associated maintenance (NGAM) is chosen adequately. When changing a constant feed of maize silage from continuous to pulsed feeding, the final average methane production remains very similar for both standard and coupled models, while both the initial response of the methanogenic population at the onset of pulsed feeding as well as its dynamics between pulses deviates considerably. In contrast to ADM1, the coupled models deliver predictions of up to 1,000s of intracellular metabolic fluxes per species, describing intracellular metabolic pathway activity in much higher detail. Furthermore, yield coefficients which need to be specified in ADM1 are no longer required as they are implicitly encoded in the topology of the species’ metabolic network. We show the feasibility of augmenting ADM1, an ordinary differential equation-based model for simulating biogas production, by FBA models implementing individual steps of anaerobic digestion. While cellular maintenance is introduced as a new parameter, the total number of parameters is reduced as yield coefficients no longer need to be specified. The coupled models provide detailed predictions on intracellular activity of microbial species which are compatible with experimental data on enzyme synthesis activity or abundance as obtained by metatranscriptomics or metaproteomics. By providing predictions of intracellular fluxes of individual community members, the presented approach advances the simulation of microbial community driven processes and provides a direct link to validation by state-of-the-art experimental techniques

A U-Shaped Layout for a Manual Order Picking System

In manual order picking systems, order pickers walk or ride through a warehouse in order to collect items requested by customers. The performance of such a system is significantly dependent on its layout which determines the lengths of the order pickers\u27 tours and the corresponding picking times. Whereas for classic warehouse layouts all picking aisles are arranged in parallel to each other, in the warehouse layout presented here the picking aisles are arranged around a U-shaped central aisle. This layout has been developed for order picking systems in which slow-moving items are prevalent. A new routing strategy for such a warehouse is presented and an analytical expression for the expected tour length per picking order is derived. By comparing this estimation with those of routing schemes in classic warehouse layouts, it is demonstrated in which situations such U-shaped layouts allow for operating warehouses more efficiently

Metaheuristics for the Order Batching Problem in Manual Order Picking Systems

In manual order picking systems, order pickers walk or drive through a distribution warehouse in order to collect items which are requested by (internal or external) customers. In order to perform these operations effciently, it is usually required that customer orders are combined into (more substantial) picking orders of limited size. The Order Batching Problem considered in this paper deals with the question of how a given set of customer orders should be combined such that the total length of all tours is minimized which are necessary to collect all items. The authors introduce two metaheuristic approaches for the solution of this problem; the rst one is based on Iterated Local Search, the second one on Ant Colony Optimization. In a series of extensive numerical experiments, the newly developed approaches are benchmarked against classic solution methods. It is demonstrated that the proposed methods are not only superior to existing methods, but provide solutions which may allow for operating distribution warehouses signi cantly more effcient