The Internet of Things (IoT) has transformed many industries, and LoRaWAN (Long Range Wide Area Network), built on LoRa (Long Range) technology, has become a crucial solution for enabling scalable, low-cost, and energy-efficient communication in wide-area networks. Simulation tools are essential for optimizing the transmission parameters and, therefore, the energy efficiency and performance of LoRaWAN networks, where energy efficiency is widely recognized as a primary performance objective in distributed and parallel systems. However, although existing simulation frameworks can accurately represent LoRaWAN behavior by modeling multiple protocol layers or by focusing on specific network aspects, they often suffer from high computational cost and limited flexibility when fast and systematic exploration of transmission parameter configurations is required. To address this issue, we introduce FAST-LoRa, a novel simulation framework designed to enable fast and efficient evaluation of LoRaWAN networks and selection of transmission parameters. FAST-LoRa streamlines computation by relying on analytical models without complex packet-level simulations and implementing gateway reception using efficient matrix operations. We evaluate FAST-LoRa by employing a well-established simulator using multiple network configurations with varying numbers of end devices and gateways. The results show that FAST-LoRa achieves similar accuracy in estimating key network metrics, even in complex scenarios with interference and multi-gateway reception, with a Mean Absolute Error (MAE) of $0.940\times 10^{-2}$ for the Packet Delivery Ratio (PDR) and 0.040 bits/mJ for Energy Efficiency (EE), while significantly reducing computational time by up to three orders of magnitude.

Warehouse operations involve dynamic item flows, fluctuating demand, and heterogeneous layouts, making adaptive decision-making essential for efficient storage and order fulfillment. In this context, reinforcement learning (RL) provides a promising approach for learning adaptive warehouse control policies under stochastic environments. However, evaluating RL-based solutions in real warehouse settings is often costly and time-consuming, motivating the need for realistic and reproducible simulation environments. In this paper, we introduce a configurable warehouse simulation environment modeling stochastic item arrivals, order generation, and internal logistics operations across diverse layouts and workload conditions. Based on this environment, we construct a reproducible experimental testbed composed of multiple scenarios ranging from low-load to highly congested settings. The testbed is publicly released to support reproducible research and comparative evaluation within the research community. We formulate the warehouse management problem as a Markov decision process (MDP) and apply a Maskable Proximal Policy Optimization (Maskable PPO) agent to learn adaptive control policies. The RL-based approach is evaluated across the defined scenarios and compared against heuristic baseline strategies. Experimental results show that the proposed solution achieves performance comparable to a strong greedy first-in, first-out (FIFO) heuristic while improving order fulfillment by up to 13.5 percentage points under challenging workload conditions. These results demonstrate the ability of RL to learn robust warehouse control policies that adaptively optimize performance and maintain operational stability across a wide spectrum of distinct scenarios.