205 research outputs found

    A Linear Programming Model for Renewable Energy Aware Discrete Production Planning and Control

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    Industrial production in the EU, like other sectors of the economy, is obliged to stop producing greenhouse gas emissions by 2050. With its Green Deal, the European Union has already set the corresponding framework in 2019. To achieve Net Zero in the remaining time, while not endangering one's own competitiveness on a globalized market, a transformation of industrial value creation has to be started already today. In terms of energy supply, this means a comprehensive electrification of processes and a switch to fully renewable power generation. However, due to a growing share of renewable energy sources, increasing volatility can be observed in the European electricity market already. For companies, there are mainly two ways to deal with the accompanying increase in average electricity prices. The first is to reduce consumption by increasing efficiency, which naturally has its physical limits. Secondly, an increasing volatile electricity price makes it possible to take advantage of periods of relatively low prices. To do this, companies must identify their energy-intensive processes and design them in such a way as to enable these activities to be shifted in time. This article explains the necessary differentiation between labor-intensive and energy intensive processes. A general mathematical model for the holistic optimization of discrete industrial production is presented. With the help of this MILP model, it is simulated that a flexibilization of energy intensive processes with volatile energy prices can help to reduce costs and thus secure competitiveness while getting it in line with European climate goals. On the basis of real electricity market data, different production scenarios are compared, and it is investigated under which conditions the flexibilization of specific processes is worthwhile

    Learning Dynamic Priority Scheduling Policies with Graph Attention Networks

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    The aim of this thesis is to develop novel graph attention network-based models to automatically learn scheduling policies for effectively solving resource optimization problems, covering both deterministic and stochastic environments. The policy learning methods utilize both imitation learning, when expert demonstrations are accessible at low cost, and reinforcement learning, when otherwise reward engineering is feasible. By parameterizing the learner with graph attention networks, the framework is computationally efficient and results in scalable resource optimization schedulers that adapt to various problem structures. This thesis addresses the problem of multi-robot task allocation (MRTA) under temporospatial constraints. Initially, robots with deterministic and homogeneous task performance are considered with the development of the RoboGNN scheduler. Then, I develop ScheduleNet, a novel heterogeneous graph attention network model, to efficiently reason about coordinating teams of heterogeneous robots. Next, I address problems under the more challenging stochastic setting in two parts. Part 1) Scheduling with stochastic and dynamic task completion times. The MRTA problem is extended by introducing human coworkers with dynamic learning curves and stochastic task execution. HybridNet, a hybrid network structure, has been developed that utilizes a heterogeneous graph-based encoder and a recurrent schedule propagator, to carry out fast schedule generation in multi-round settings. Part 2) Scheduling with stochastic and dynamic task arrival and completion times. With an application in failure-predictive plane maintenance, I develop a heterogeneous graph-based policy optimization (HetGPO) approach to enable learning robust scheduling policies in highly stochastic environments. Through extensive experiments, the proposed framework has been shown to outperform prior state-of-the-art algorithms in different applications. My research contributes several key innovations regarding designing graph-based learning algorithms in operations research.Ph.D

    Open Problems in (Hyper)Graph Decomposition

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    Large networks are useful in a wide range of applications. Sometimes problem instances are composed of billions of entities. Decomposing and analyzing these structures helps us gain new insights about our surroundings. Even if the final application concerns a different problem (such as traversal, finding paths, trees, and flows), decomposing large graphs is often an important subproblem for complexity reduction or parallelization. This report is a summary of discussions that happened at Dagstuhl seminar 23331 on "Recent Trends in Graph Decomposition" and presents currently open problems and future directions in the area of (hyper)graph decomposition

    On Parallel Computation of Large Smooth-Degree Isogeny

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    The computation of large smooth-degree isogenies is considered to be the most time-consuming task in isogeny-based cryptosystems and, to this end, recently several proposals have been made to speed it up. For implementation in software using a single core, De Feo et al. presented an optimal way to compute such isogenies. The multi-core setting is however far more intricate but offers various ways to reduce the computation time and is an active area of research. This thesis presents a study of speeding-up large smooth-degree isogeny computation with various forms of parallelism and consists of three contributions. The first contribution of this thesis is two novel theoretical techniques for speeding-up the computation with parallelism. Our proposed technique, called precedence-constrained scheduling (PCS), transforms the isogeny computation into a task scheduling problem with precedence constraints and utilizes several task scheduling algorithms to tackle the problem. Another proposed technique of ours is to formulate the isogeny computation as an integer linear program. Combining both techniques, we are able to reduce the theoretical cost of the isogeny computation by up to 13.02% from the state-of-the-art. The second contribution of this thesis is two software implementations of the isogeny computation based on our PCS technique. We consider two execution environments for the implementations: one relies only on the parallelism provided by multi-core processors, and the other utilizes multi-core processors supporting the Intel's Advanced Vector eXtensions (AVX) technology. To our best knowledge, we are the first to utilize both parallelization technologies for the isogeny computation. Also, to achieve effective implementations, we modify PCS for each execution environments and equip both implementations with a synchronization handling technique. The implementation results show up to 14.36% speed-up for the first implementation and up to 34.05% speed-up for the second implementation. The third contribution of this thesis is two applications of using learning-based optimizations to speed-up the parallel isogeny computation. We consider the genetic algorithm and the reinforcement learning algorithm and detail our design rationale when instantiating both algorithms for our problem. From experimental results, the genetic algorithm is able to find a better approach for the isogeny computation. The approach found is nontrivial and is up to 9.95% faster than human's heuristic. On the other hand, the reinforcement learning lags PCS by as small as 2.73%. We use the experimental results of the reinforcement learning to argue that PCS may be nearly or even optimal for the computation

    k-Means

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    Robots learn to behave: improving human-robot collaboration in flexible manufacturing applications

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    L'abstract è presente nell'allegato / the abstract is in the attachmen
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