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Hybrid simulation of biological processes becomes widely used to overcome the limitations of the pure stochastic or the complete deterministic simulation. In this manual, we present easy-to-follow steps for constructing and executing hybrid models via Snoopy [HHL+12]. Snoopy is a tool to design and animate or simulate hierarchical graphs, i.e., qualitative, stochastic, continuous, and hybrid Petri nets. This manual is concerned with hybrid Petri nets (HPN) [HH12] as well as their coloured counterpart (HPNC) [HLR14]. HPN combine the merits of stochastic and continuous Petri nets into one single class. Moreover, HPN in Snoopy supports state of the art hybrid simulation algorithms (e.g., [HH16]) to execute the constructed HPN models. Simulating a model using Snoopy's hybrid simulation involves first constructing the reaction network via HPN notations and afterwards executing such model.
Drahtlose Sensornetze stellen eine vielversprechende Technologie zur Beobachtung und Beeinflussung von Vorgängen in der realen Welt dar. Autonome Sensorknoten nehmen dabei Parameter der Umwelt durch Sensoren wahr und können diese durch Aktoren beeinflussen. Viele solcher autonomen und ressourcenbeschränkten Knoten kooperieren dabei mittels drahtloser Kommunikation. Die Eigenschaften dieser Knoten und Netze implizieren eine Vielzahl von neuartigen Herausforderungen, die sich in einer regen Forschungsaktivität widerspiegeln. Ziel dieser Reihe von Fachgesprächen ist es, Wissenschaftlerinnen und Wissenschaftlern aus Hochschule und Industrie die Möglichkeit zu einem informellen Gedankenaustausch zu geben und die Kooperation in diesem multidisziplinären Forschungsbereich zu verstärken.
For the third time we were able to hold our PhD workshop "Research is Calling" on smart medical devices and systems, this time in Berlin again. This unique workshop brings together young scientists from computer science and medicine to provide a forum to discuss the very latest approaches to smart medical devices and systems. This year, for the first time, it is possible to publish corresponding workshop proceedings via the publication service of the Brandenburg Technical University (BTU). In our view, this format can create true interdisciplinarity because everyone presents their ideas and interim results to colleagues from a wide variety of disciplines and, conversely, hears and sees the latest from many other specialties. This forum is the vehicle for initiating new collaborations and planning joint research projects. The 'Cottbus project' emerged from personal partnerships between 'technicians' and 'physicians' and was planned from the beginning as a platform and an offer: as a platform for free and fully open scientific exchange and discourse, as an offer to all who are interested, also and gladly beyond Brandenburg. Begun as a "PhD Colloquium", Cottbus' "Research is Calling" is an ideas laboratory for young scientists. Cottbus is to establish a university medical faculty in the next few years as part of the structural change program following the coal phase-out. From our point of view, this lab is an important building block to establish cross-sectional issues right at the interface between medicine and technology, a culture of open discourse and scientific dialogue. The workshop covers the following topics in particular: Smart sensors for medical applications, Low-power wearable sensors, artificial intelligence for medical applications, medical robots, algorithms for medical applications, applications and case studies. Despite the pandemic situation the workshop could be held in presence. In five exciting presentations new ideas and cooperation possibilities were shown. Two keynotes (including Alina Nechyporenko, Marcus Frohme as special guests) and a dinner rounded off the program. At this point we would like to thank all participants, the reviewers, and the organizers, Stefan Scharoba and Kathleen Galke. We look forward to establishing this event as permanent and reaching an ever-growing audience.
The article develops an analytical framework to explain the successes and failures of local community energy projects in the countryside. Theoretical elements based on ownership, technology acceptance, value chains and public opinions are developed and synthesised in two concepts: ´technical complexity and its antagonist social complexity. The achievement of a mature community energy regime is explained as a continuous process of consecutive single steps in both social and technical arenas.
We deal with a very complex and hard scheduling problem. Several types of products are processed by a heterogeneous resource set, where resources have different operating capabilities and setup times are considered. The processing of the products follows different workflows, allowing also assembly lines. The goal is to process all products in minimum time, i.e., the makespan is to be minimized. Because of the complexity of the problem an exact solver would require too much running time. We propose a compound method where a heuristic is combined with an exact solver. Our proposed heuristic is composed of several phases applying different smart strategies. In order to reduce the computational complexity of the exact approach, we exploit the makespan determined by the heuristic as an upper bound for the time horizon, which has a direct in uence on the instance size used in the exact approach. We demonstrate the efficiency of our combined method on multiple problem classes. With the help of the heuristic the exact solver is able to obtain an optimal solution in a much shorter amount of time.
We present a general numerical solution method for control problems with PDE-defined state variables over a finite set of binary or continuous control variables. We show empirically that a naive approach that applies a numerical discretization scheme to the PDEs (and if necessary a linearization scheme) to derive constraints for a mixed-integer linear program (MILP) leads to systems that are too large to be solved with state-of-the-art solvers for MILPs, especially if we desire an accurate approximation of the state variables. Our framework comprises two techniques to mitigate the rise of computation times with increasing discretization level parameters:
First, the linear system is solved for a basis of the control space in a preprocessing step. Second, certain constraints are just imposed on demand via the IBM ILOG CPLEX feature of a lazy constraint callback. These techniques are compared with an approach where the relations obtained by the discretization of the continuous constraints are directly included in the MILP. We demonstrate our approach on two examples: modeling of the spread of wildfire and the mitigation of water contamination. In both examples the computational results demonstrate that the solution time is significantly reduced by our methods. In particular, the dependence of the computation time on the size of the spatial discretization of the PDE is significantly reduced.
A tourism company that offers fly-in safaris is faced with the challenge to route and schedule its fleet of airplanes in an optimal way. Over the course of a given time horizon several groups of tourists have to be picked up at airports and flown to their destinations within a certain time-window. Furthermore the number of available seats, the consumption of fuel, the maximal takeoff weight, and restrictions on the detour of the individual groups have to be taken into account. The task of optimally scheduling the airplanes and tour groups belongs to the class of vehicle routing problems with pickup and delivery and time-windows. A flow-over-flow formulation on the time expanded graph of the airports was used in the literature in order to model this problem as a mixed integer linear program. Most of the benchmark problems however could not be solved within a time limit of three hours, which was overcome by formulating the problem for a simplified (time-free) graph and the use of an incumbent callback to check for feasibility in the original graph. While this approach led to very good results for instances, where few time-free solutions were infeasible for the original problem, some instances remained unsolved. In order to overcome this problem we derive two new exact formulations that include time as variables. Although these formulations by themselves are not better than the approach from the literature, they allow for an effective construction of graphs which can be interpreted as intermediate graphs between the graph of airports and the expanded graph with vertices for each visit. Using similar relaxation techniques to the time-free approach and constructing these graphs based on solutions of the relaxations guarantees that only critical airports are expanded. A computational study was performed in order to compare the new formulations to the methods from the literature. Within a time limit of 3 hours the new approach was able to find proven optimal solutions for all previously unsolved benchmark instances. Furthermore the average computation time of all benchmark instances was reduced by 90 percent.
This report compares some stochastic simulation tools for biochemical reaction networks. The stochastic simulation tools are selected on the basis of some selection criteria. Simulations are performed for the different stochastic simulation tools on different benchmark models. This report gives an overview of how the comparison is carried out for the chosen tools.
The tools are compared on a common evaluation protocol. The evaluation protocol comprises a set of benchmark models along with the parameters which are provided as input to the tools. The benchmark models are represented as Petri nets and fed in SBML (System Biology Markup Language) to the different tools. Experiments are performed on each tool and the results are recorded. The tools are finally compared based on the comparison criteria.
Planning the construction of new transport routes or power lines on terrain is usually carried out manually by engineers, with no guarantee of optimality. We introduce a new approach for the computation of an optimal trajectory for the construction of new transit routes and power lines between two locations on a submanifold U _ R3 representing the topography of a terrain. U is approximatively modeled by a special weighted grid. On this grid, the shortest paths for the construction of new routes are determined, whereby we consider three optimization criteria: routes with minimum distance, routes with lowest construction costs and routes with minimum absolute altitude variations or minimum absolute gradients. Subsequently, a combination of these criteria is used to expand this problem into a multi-criteria optimization problem. A shortest path algorithm, such as the Dijkstra algorithm, is used to compute optimal compromises for the construction of new routes.
The multiple traveling salesmen problem with moving targets is a generalization of the classical traveling salesmen problem, where the targets (cities or objects) are moving over time. Additionally, for each target a visibility time window is given. The task is to find routes for several salesmen so that each target is reached exactly once within its visibility time window and the sum of all traveled distances of all salesmen is minimal. We present different modeling formulations for this TSP variant. The time requirements are modeled differently in each approach. Our goal is to examine what formulation is most suitable in terms of runtime to solve the multiple traveling salesmen problem with moving targets with exact methods. Computational experiments are carried out on randomly generated test instances to compare the different modeling approaches. The results for large-scale instances show, that the best way to model time requirements is to directly insert them into a formulation with discrete time steps.