Automation in the area of ship navigation and course planning is adversely affected by oceanic conditions. It leads to deviation from the set course, damages the ship structure, and reduces overall efficiency. Autopilot in ships can keep the ship on course but is unable to choose an efficient path in real-time. Varying wave height is one of the most prominent causes that lead to this inefficiency in the ship’s autopilot system. Current state-of-the-art methods in the domain include building machine and deep learning-based models to estimate wave height. However, the existing systems have several limitations, such as difficulty in handling abrupt non-linear and chaotic variations present in the data, low generalizability, noise sensitivity, and many more. To resolve this issue, the current study proposes a hybrid approach involving a combination of Variable Mode Decomposition and Bidirectional Long Short-Term Memory model (VMD -BiLSTM) and its integration to the ship’s autopilot system. The VMD-based data decomposition enables deep learning models to smoothly and accurately capture observed variational components present in the data, contributing to improved accuracy. Performance comparison with state-of-the-art prediction models validates the efficiency and reliability of the proposed prediction approach.

Summary Traveling has contributed a lot to the evolution of mankind. Today, electric vehicles (EVs) are being preferred due to their greater efficiency, comfort, and environment‐friendly qualities. The EVs' contribution to future mobility is projected to rise exponentially in years to come. To make this innovative technology more successful, there is a dire need to install a sufficient number of charging stations (CSs). As the EVs are limited by their cruising range, they require multiple recharging to cover long distances (especially in the case of logistics delivery services). Thus, there is a great need to develop an efficient and cost‐effective EV route optimization approach considering multiple recharging options and time‐of‐use (ToU) energy prices. In this regard, a novel mat‐heuristic approach named (firefly with ant colony algorithm) has been proposed to solve the problem of EVRPTW (electric vehicle routing problem with time windows) incorporating detailed modeling of multiple charging flexibility (i.e., battery swapping, partial recharge, and different charging levels) and ToU energy prices. Our proposed approach aims to minimize the total cost of traveling, which is highly influenced by the cost of recharging. Ant colony algorithm (ACA) serves as the basic optimization framework in the proposed approach, while the firefly approach explores hitherto unexplored solution space and avoids local optima. The computation performance of the proposed approach is compared with existing state‐of‐the‐art similar domain approaches such as variable neighborhood search (VNS) and ant colony optimization using local search (ACO‐LS) which has average deviation of nearly 20%–25% from with optimal solution achieved by the proposed . The proposed approach yields a near‐optimal solution with a faster convergence rate (approximately 50%) compared to other existing approaches. Moreover, the multiple recharging options modeled in our proposed approach justify their significance in terms of cost‐effectiveness for most scenarios.