This work concerns a new concept of vertical farms that can be installed in orbital stations and future lunar settlements in order to produce fresh food for astronauts. The main novelty of this kind of greenhouse lies in the possibility of continuously adapting the volume assigned to each crop to the level of plant growth by automatically moving the shelves, thus allowing to grow a greater number of crops per unit of volume than existing solutions based on vertical farms with fixed shelves. The main results on the production yield estimation of this structure, compared to conventional vertical farms, are presented together with the prospect of future research work.

No description available.

This paper describes automated examination scheduling for the Faculty of Information Technology of Brno University of Technology. It specifies a list of restrictions that must by satisfied. Furthermore, this limitations are classified due to their influence on a quality of the final version of the examination schedule. There are two types of restrictions; soft and hard. The task is to find such a solution that satisfies all hard constraints and breaks the minimum of soft constraints using techniques described in this paper.

The numerous man-made disasters and natural catastrophes that menace major communities accentuate the need for proper planning for emergency evacuation. Transportation networks in cities evolve over long time spans in tandem with population growth and evolution of travel patterns. In emergencies, travel demand and travel patterns drastically change from the usual everyday volumes and patterns. Given that most US and Canadian cities are already congested and operating near capacity during peak periods, network performance can severely deteriorate if drastic changes in Origin-Destination (O-D) demand patterns occur during or after a disaster. Also, loss of capacity due to the disaster and associated incidents can further complicate the matter. Therefore, the primary goal when a disaster or hazardous event occurs is to coordinate, control, and possibly optimize the utilization of the existing transportation network capacity. Emergency operation management centres face multi-faceted challenges in anticipating evacuation flows and providing proactive actions to guide and coordinate the public towards safe shelters. Numerous studies have contributed to developing and testing strategies that have the potential to mitigate the consequences of emergency situations. They primarily investigate the effect of some proposed strategies that have the potential of improving the performance of the evacuation process with modelling and optimization techniques. However, most of these studies are inherently restricted to evacuating automobile traffic using a certain strategy without considering other modes of transportation. Moreover, little emphasis is given to studying the interaction between the various strategies that could be potentially synergized to expedite the evacuation process. Also, the absence of an accurate representation of the spatial and temporal distribution of the population and the failure to identify the available modes and populations that are captive to certain modes contribute to the absence of multimodal evacuation procedures. Incorporating multiple modes into emergency evacuation has the potential to expedite the evacuation process and is essential to assuring the effective evacuation of transit-captive and special-needs populations . This dissertation presents a novel multimodal optimization framework that combines vehicular traffic and mass transit for emergency evacuation. A multi-objective approach is used to optimize the multimodal evacuation problem. For automobile evacuees, an Optimal Spatio-Temporal Evacuation (OSTE) framework is presented for generating optimal demand scheduling, destination choices and route choices, simultaneously. OSTE implements Dynamic Traffic Assignment (DTA) techniques coupled with parallel distributed genetic optimization to guarantee a near global optimal solution. For transit evacuees, a Multi-Depots, Time Constrained, Pick-up and Delivery Vehicle Routing Problem (MDTCPD-VRP) framework is presented to model the use of public transit vehicles in evacuation situations. The MDTCPD-VRP implements constraint programming and local search techniques to optimize certain objective functions and satisfy a set of constraints. The OSTE and MDTCPD-VRP platforms are integrated into one framework to replicate the impact of congestion caused by traffic on transit vehicle travel times. A proof-of-concept prototype has been tested; it investigates the optimization of a multimodal evacuation of a portion of the Toronto Waterfront area. It also assesses the impact of multiple objective functions on emergency evacuation while attempting to achieve an equilibrium state between transit modes and vehicular traffic. Then, a large-scale application, including a demand estimation model from a regional travel survey, is conducted for the evacuation of the entire City of Toronto. This framework addresses many limitations of existing evacuation planning models by: 1) synergizing multiple evacuation strategies; 2) utilizing robust optimization and solution algorithms that can tackle such multi-dimensional non deterministic problem; 3) estimating the spatial and temporal distribution of evacuation demand; 4) identifying the transit-dependent population; 5) integrating multiple modes in emergency evacuation. The framework presents a significant step forward in emergency evacuation optimization.

The numerous man-made disasters and natural catastrophes that menace major communities accentuate the need for proper planning for emergency evacuation. Transportation networks in cities evolve over long time spans in tandem with population growth and evolution of travel patterns. In emergencies, travel demand and travel patterns drastically change from the usual everyday volumes and patterns. Given that most US and Canadian cities are already congested and operating near capacity during peak periods, network performance can severely deteriorate if drastic changes in Origin-Destination (O-D) demand patterns occur during or after a disaster. Also, loss of capacity due to the disaster and associated incidents can further complicate the matter. Therefore, the primary goal when a disaster or hazardous event occurs is to coordinate, control, and possibly optimize the utilization of the existing transportation network capacity. Emergency operation management centres face multi-faceted challenges in anticipating evacuation flows and providing proactive actions to guide and coordinate the public towards safe shelters. Numerous studies have contributed to developing and testing strategies that have the potential to mitigate the consequences of emergency situations. They primarily investigate the effect of some proposed strategies that have the potential of improving the performance of the evacuation process with modelling and optimization techniques. However, most of these studies are inherently restricted to evacuating automobile traffic using a certain strategy without considering other modes of transportation. Moreover, little emphasis is given to studying the interaction between the various strategies that could be potentially synergized to expedite the evacuation process. Also, the absence of an accurate representation of the spatial and temporal distribution of the population and the failure to identify the available modes and populations that are captive to certain modes contribute to the absence of multimodal evacuation procedures. Incorporating multiple modes into emergency evacuation has the potential to expedite the evacuation process and is essential to assuring the effective evacuation of transit-captive and special-needs populations . This dissertation presents a novel multimodal optimization framework that combines vehicular traffic and mass transit for emergency evacuation. A multi-objective approach is used to optimize the multimodal evacuation problem. For automobile evacuees, an Optimal Spatio-Temporal Evacuation (OSTE) framework is presented for generating optimal demand scheduling, destination choices and route choices, simultaneously. OSTE implements Dynamic Traffic Assignment (DTA) techniques coupled with parallel distributed genetic optimization to guarantee a near global optimal solution. For transit evacuees, a Multi-Depots, Time Constrained, Pick-up and Delivery Vehicle Routing Problem (MDTCPD-VRP) framework is presented to model the use of public transit vehicles in evacuation situations. The MDTCPD-VRP implements constraint programming and local search techniques to optimize certain objective functions and satisfy a set of constraints. The OSTE and MDTCPD-VRP platforms are integrated into one framework to replicate the impact of congestion caused by traffic on transit vehicle travel times. A proof-of-concept prototype has been tested; it investigates the optimization of a multimodal evacuation of a portion of the Toronto Waterfront area. It also assesses the impact of multiple objective functions on emergency evacuation while attempting to achieve an equilibrium state between transit modes and vehicular traffic. Then, a large-scale application, including a demand estimation model from a regional travel survey, is conducted for the evacuation of the entire City of Toronto. This framework addresses many limitations of existing evacuation planning models by: 1) synergizing multiple evacuation strategies; 2) utilizing robust optimization and solution algorithms that can tackle such multi-dimensional non deterministic problem; 3) estimating the spatial and temporal distribution of evacuation demand; 4) identifying the transit-dependent population; 5) integrating multiple modes in emergency evacuation. The framework presents a significant step forward in emergency evacuation optimization.

No description available.

<div>Smart warehouses have become more popular in these days, with Automated Guided Vehicles (AGVs) being used for order pickups. They also allow efficient cost management with optimized storage and retrieval. Moreover, optimization of resources in these warehouses is essential to ensure maximum efficiency. In this thesis, we consider a three dimensional smart warehouse system equipped with heterogeneous AGVs (i.e., having different speeds). We propose scheduling and placement policies that jointly consider all the different design parameters including the scheduling decision probabilities and storage assignment locations. In order to provide differentiated service levels, we propose a prioritized probabilistic scheduling and placement policy to minimize a weighted sum of mean latency and latency tail probability (LTP). Towards this goal, we first derive closed-form expressions for the mean latency and LTP. Then, we formulate an optimization problem to jointly optimize a weighted sum of both the mean latency and LTP. The optimization problem is solved efficiently over the scheduling and decision variables. For a given placement of the products, scheduling decisions of customers’ orders are solved optimally and derived in closed forms. Evaluation results demonstrate a significant improvement of our policy (up to 32%) as compared to the state of other algorithms, such as the Least Work Left policy and Join the Shortest Queue policy, and other competitive baselines.</div>

Eine Herausforderung der Parallelverarbeitung ist das Erreichen von Skalierbarkeit großer paralleler Anwendungen für verschiedene parallele Systeme. Das zentrale Problem ist, dass die Ausführung einer Anwendung auf einem parallelen System sehr gut sein kann, die Portierung auf ein anderes System in der Regel jedoch zu schlechten Ergebnissen führt. Durch die Verwendung des Programmiermodells der parallelen Tasks mit Abhängigkeiten kann die Skalierbarkeit für viele parallele Algorithmen deutlich verbessert werden. Die Programmierung mit parallelen Tasks führt zu Task-Graphen mit Abhängigkeiten zur Darstellung einer parallelen Anwendung, die auch als gemischt-parallele Anwendung bezeichnet wird. Die Grundlage für eine effiziente Abarbeitung einer gemischt-parallelen Anwendung bildet ein geeigneter Schedule, der eine effiziente Abbildung der parallelen Tasks auf die Prozessoren des parallelen Systems vorgibt. Für die Berechnung eines Schedules werden Schedulingalgorithmen eingesetzt. Ein zentrales Problem bei der Bestimmung eines Schedules für gemischt-parallele Anwendungen besteht darin, dass das Scheduling bereits für Single-Prozessor-Tasks mit Abhängigkeiten und ein paralleles System mit zwei Prozessoren NP-hart ist. Daher existieren lediglich Approximationsalgorithmen und Heuristiken um einen Schedule zu berechnen. Eine Möglichkeit zur Berechnung eines Schedules sind layerbasierte Schedulingalgorithmen. Diese Schedulingalgorithmen bilden zuerst Layer unabhängiger paralleler Tasks und berechnen den Schedule für jeden Layer separat. Eine Schwachstelle dieser Schedulingalgorithmen ist das Zusammenfügen der einzelnen Schedules zum globalen Schedule. Der vorgestellte Algorithmus Move-blocks bietet eine elegante Möglichkeit das Zusammenfügen zu verbessern. Dies geschieht durch eine Verschmelzung der Schedules aufeinander folgender Layer. Obwohl eine Vielzahl an Schedulingalgorithmen für gemischt-parallele Anwendungen existiert, gibt es bislang keine umfassende Unterstützung des Schedulings durch Programmierwerkzeuge. Im Besonderen gibt es keine Schedulingumgebung, die eine Vielzahl an Schedulingalgorithmen in sich vereint. Die Vorstellung der flexiblen, komponentenbasierten und erweiterbaren Schedulingumgebung SEParAT ist der zweite Fokus dieser Dissertation. SEParAT unterstützt verschiedene Nutzungsszenarien, die weit über das reine Scheduling hinausgehen, z.B. den Vergleich von Schedulingalgorithmen und die Erweiterung und Realisierung neuer Schedulingalgorithmen. Neben der Vorstellung der Nutzungsszenarien werden sowohl die interne Verarbeitung eines Schedulingdurchgangs als auch die komponentenbasierte Softwarearchitektur detailliert vorgestellt.