Joint location and inventory models and algorithms for deployment of hybrid electric vehicle charging stations.

This thesis describes a study of a novel concept of hybrid electric vehicle charging stations in which two types of services are offered: battery swapping and fast level-3 DC charging. The battery swapping and fast-charging service are modeled by using the M/G/s/s model and the M/G/s/$\infty$ model, respectively. In particular, we focus on the operations of joint battery swapping and fast charging services, develop four joint locations and inventory models: two for the deployment of battery swapping service, two for the deployment of hybrid electric vehicle charging service. The first model for each deployment system considers a service-level constraint for battery swapping and hybrid charging service, whereas the second for each deployment system considers total sojourn time in stations. The objective of all four models is to minimize total facility setup cost plus battery and supercharger purchasing cost. The service level, which is calculated by the Erlang loss function, depends on the stockout probability for batteries with enough state of charge (SOC) for the battery swapping service and the risk of running out of superchargers for the quick charging service. The total sojourn time is defined as the sum of the service time and the waiting time in the station. Metaheuristic algorithms using a Tabu search are developed to tackle the proposed nonlinear mixed-integer optimization model. Computational results on randomly generated instances and on a real-world case comprised of 714,000 households show the efficacy of proposed models and algorithms

Scheduling and Power Control for Wireless Multicast Systems via Deep Reinforcement Learning

Multicasting in wireless systems is a natural way to exploit the redundancy in user requests in a Content Centric Network. Power control and optimal scheduling can significantly improve the wireless multicast network's performance under fading. However, the model based approaches for power control and scheduling studied earlier are not scalable to large state space or changing system dynamics. In this paper, we use deep reinforcement learning where we use function approximation of the Q-function via a deep neural network to obtain a power control policy that matches the optimal policy for a small network. We show that power control policy can be learnt for reasonably large systems via this approach. Further we use multi-timescale stochastic optimization to maintain the average power constraint. We demonstrate that a slight modification of the learning algorithm allows tracking of time varying system statistics. Finally, we extend the multi-timescale approach to simultaneously learn the optimal queueing strategy along with power control. We demonstrate scalability, tracking and cross layer optimization capabilities of our algorithms via simulations. The proposed multi-timescale approach can be used in general large state space dynamical systems with multiple objectives and constraints, and may be of independent interest.Comment: arXiv admin note: substantial text overlap with arXiv:1910.0530

The effect of workload constraints in mathematical programming models for production planning

Linear and mixed integer programming models for production planning incorporate a model of the manufacturing system that is necessarily deterministic. Although these eterministic models are the current-state-of-art, it should be recognized that they are used in an environment that is inherently stochastic. This fact should be kept in mind, both when making modeling choices and when setting the parameters of the model. In this paper we study the relation between workload constraints that reflect the finite capacity of the manufacturing system, and the use of planned lead times. It is a common practice in rolling schedule based production planning to limit the periodic output to the average production rate. If lead times are not modeled explicitly, this also implies a restricition on the periodic releases to the average production rate. We demonstrate that this common practice results in inefficient use of the production capacity and show that the use of planned lead times leads to a better trade-off between efficiency and reliability. We analyze a stylized model of a manufacturing system with a single exponential server and two queues in series: an admission queue and a work-in-progress (WIP) queue. The admission queue represents the pool of unreleased orders that is virtually present in the state variables of the planning model. Periodically, jobs from the admission queue are released to the WIP queue such that the number of jobs in WIP and in service does not exceed the workload constraint. We present a simple formula for the maximum utilization rate of such a system, characterize the stationary queue-length distribution by its generating function, and give the distribution of the sojourn time of a job. We use the results to compare various settings of the workload constraint and the planned lead time