Sökning: "automated scheduling"
Visar resultat 1 - 5 av 34 uppsatser innehållade orden automated scheduling.
1. Adaptive Control of a Permanent Magnet Synchronous Motor for a Robotic Arm under Variable Load
Master-uppsats, KTH/Mekatronik och inbyggda styrsystemSammanfattning : The implementation of automated systems in manufacturing industries increases efficiency, precision, and safety by reducing human intervention, errors, and waste. Variable loads can cause several problems for automation systems. LÄS MER
2. Oven Usage Optimization : A study on scheduling at the wear edge production at Olofsfors AB
Uppsats för yrkesexamina på avancerad nivå, Umeå universitet/Institutionen för matematik och matematisk statistikSammanfattning : Olofsfors is a steel product manufacturer in Nordmaling, Sweden, producing steel edges for snowplows, tracks for forest machines, and wear edges for buckets on heavy equipment. Most of their products are heated to 900◦ C and then cooled down in water, so-called quenching, during the hardening process. LÄS MER
3. Effects of Design Space Discretization on Constraint Based Design Space Exploration
Master-uppsats, KTH/Matematik (Avd.)Sammanfattning : Design Space Exploration (DSE) is the exploration of a space of possible designs with the goal of finding some optimal design according to some constraints and criteria. Within embedded systems design, automated DSE in particular can allow the system designer to efficiently find good solutions in highly complex design spaces. LÄS MER
4. Workforce Scheduling for Flamman Pub & Disco
Kandidat-uppsats, Linköpings universitet/Tillämpad matematik; Linköpings universitet/Tekniska fakultetenSammanfattning : Workforce scheduling is widely used within most industries. A well-outlined and efficient schedule gives cost savings, such as reduced number of overtime hours, increases overall utilization, and facilitates meeting demands. LÄS MER
5. Deep Reinforcement Learning and Simulation for the Optimization of Production Systems
Master-uppsats, Uppsala universitet/Institutionen för informationsteknologiSammanfattning : The main objective of this master thesis project is to use the deep reinforcement learning (DRL) and simulation method for optimization of production systems. In this project, the Deep Q-learning Networks (DQN) algorithm is first used to optimize seven decision variables in Averill Law’s production system to find the best profit, with 99. LÄS MER