Online 3D Bin Packing with Constrained Deep Reinforcement Learning

We solve a challenging yet practically useful variant of 3D Bin Packing Problem (3D-BPP). In our problem, the agent has limited information about the items to be packed into the bin, and an item must be packed immediately after its arrival without buffering or readjusting. The item's placement also subjects to the constraints of collision avoidance and physical stability. We formulate this online 3D-BPP as a constrained Markov decision process. To solve the problem, we propose an effective and easy-to-implement constrained deep reinforcement learning (DRL) method under the actor-critic framework. In particular, we introduce a feasibility predictor to predict the feasibility mask for the placement actions and use it to modulate the action probabilities output by the actor during training. Such supervisions and transformations to DRL facilitate the agent to learn feasible policies efficiently. Our method can also be generalized e.g., with the ability to handle lookahead or items with different orientations. We have conducted extensive evaluation showing that the learned policy significantly outperforms the state-of-the-art methods. A user study suggests that our method attains a human-level performance

Path Planning with Drones at CSP plants

The goal of this work is to apply mathematics knowledge and skills to efficiently solve a practical problem posed by the industry. We study an actual problem related to the inspection of Concentrated Solar Power (CSP) plants. Due to the big extension of solar fields, Unmanned Aerial Vehicles (UAV), commonly called drones, are used to inspect all the tubes of the CSP plant. We introduce a new problem, named the drone CSP inspection problem, that aims the computation of the tours to be performed by the drone in order to cover the CSP plant so that some penalization function is min imized. Specifically, we take into account two objective functions: the total time or the number of refills. First, we model the energy consumption of the UAV and the individual time inspection costs in a realistic fashion and use them as inputs for the procedures described. We also propose several formulations adapting classical optimization problems. In addition, we prove that this particular problem is NP-complete and develop some heuristics. An extensive comparison against the current approach adopted by the industry shows best performance of our algorithms, saving a considerable amount of time for inspection.El objetivo de este trabajo es aplicar conocimiento y habilidades matemáticas para resolver eficientemente un problema práctico propuesto por la industria. Estudiaremos un problema real relacionado con la inspección de plantas de concentración solar de potencia (CSP). Debido a la gran extensión de los campos solares se utilizan vehículos aéreos no pilotados (UAV), comúnmente llamados drones, para inspeccionar todos los tubos de la planta CSP. Introduciremos un nuevo problema, el problema de inspección CSP con drones, donde se propone calcular las trayectorias a realizar por el dron de manera que se cubra la planta CSP mientras se minimiza una cierta función de penalización. Concretamente, tendremos en cuenta dos funciones objetivo: el tiempo total de inspección y el número de recargas que el dron necesita. Primero, modelaremos el consumo de energía del UAV y los tiempos individuales de inspección de forma realista y los usaremos como entrada de los procedimientos descritos. Propondremos varias formulaciones adaptando problemas de optimización clásicos. Además, probaremos que este problema particular es NP-completo y desarrollaremos algunos heurísticos. Comparando éstos con procedimiento actual adoptado por la industria, probamos que nuestros algoritmos tienen un mayor rendimiento, ahorrando una considerable cantidad de tiempo total de inspección.Universidad de Sevilla. Grado en Matemáticas y Estadístic

On Discrete Hyperbox Packing

Bin packing is a very important and popular research area in the computer science field. Past work showed many good and real-world packing algorithms. How- ever, due to the complexity of the problem in multiple-dimensional bin packing, also called hyperbox packing, we need more practical packing algorithms for its real-world applications. In this dissertation, we extend 1D packing algorithms to hyperbox packing prob- lems via a general framework that takes two inputs of a 1D packing algorithm and an instance of hyperbox packing problem and outputs a hyperbox packing algorithm. The extension framework significantly enriches the family of hyperbox-packing algorithms, generates many framework-based algorithms, and simultaneously calls for the analysis for those algorithms. We also analyze the performance of a couple of framework-based algorithms from two perspectives of worst-case performance and average-case performance. In worst- case analysis, we use the worst-case performance ratio as our metric and analyze the relationship of the ratio of framework-based algorithms and that of the corresponding 1D algorithms. We also compare their worst-case performance against two baselines: strip optimal algorithms and optimal algorithms. In average-case analysis, we use expected waste as a metric, analyze the waste of optimal hyperbox packing algorithms, and estimate the asymptotic forms of the waste for framework-based algorithms

Runtime Scheduling, Allocation, and Execution of Real-Time Hardware Tasks onto Xilinx FPGAs Subject to Fault Occurrence

This paper describes a novel way to exploit the computation capabilities delivered by modern Field-Programmable Gate Arrays (FPGAs), not only towards a higher performance, but also towards an improved reliability. Computation-specific pieces of circuitry are dynamically scheduled and allocated to different resources on the chip based on a set of novel algorithms which are described in detail in this article. These algorithms consider most of the technological constraints existing in modern partially reconfigurable FPGAs as well as spontaneously occurring faults and emerging permanent damage in the silicon substrate of the chip. In addition, the algorithms target other important aspects such as communications and synchronization among the different computations that are carried out, either concurrently or at different times. The effectiveness of the proposed algorithms is tested by means of a wide range of synthetic simulations, and, notably, a proof-of-concept implementation of them using real FPGA hardware is outlined