The rapid growth of global trade has heightened the importance of efficient container handling, environmentally responsible operations, and high-performing equipment selection in sustaining the competitiveness of modern supply chains. Container Freight Stations (CFS) serve as critical operational hubs where loading, unloading, inspection, and temporary storage activities are conducted, thereby requiring equipment capable of safely and efficiently handling heavy-tonnage cargo while aligning with green port transformation goals. Forklifts, which constitute one of the core equipment groups in CFS yards, differ significantly in terms of lifting capacity, power systems, maneuverability, hydraulic performance, ergonomics, and environmental impact, transforming forklift selection into a complex, multi-dimensional decision problem shaped by both technical and Environmental, Social, and Governance (ESG)-oriented considerations. Incorrect equipment choices may lead to operational downtime, energy inefficiency, equipment failures, and occupational safety risks, particularly in operations involving loads exceeding 25 tons. To address these challenges, this study proposes a hybrid decision-making framework that integrates expert-driven fuzzy assessments with sustainability-based evaluation using the FF-Hamacher-MEREC-ARLON methodology. In the first stage, expert weights and criterion importance values were calculated through the FF-MEREC approach, while alternative forklifts were ranked using the FF-ARLON method in the second stage. Two sensitivity analysis scenarios were applied: one by modifying the tradeoff ratio within ARLON and the other by sequentially removing each criterion. In both scenarios, the fourth alternative consistently emerged as the most suitable option. Furthermore, comparative analyses using eight established MCDM techniques; ALWAS, AROMAN, ARTASI, MABAC, MARCOS, RAM, SAW, and WASPAS; demonstrated complete agreement with the proposed model, confirming the fourth alternative as the top-ranked choice. The findings highlight the robustness, reliability, and sustainability alignment of the proposed framework for high-stakes heavy-duty equipment selection in port-based logistics operations.

The Load Haul Dumper (LHD) is essential machinery utilized for moving ore in the underground mining industry, in order to fulfil production targets. In this connection, the efficiency of the equipment should be maintained at an ideal standard, to be accomplished by reducing unexpected failure of components or subsystems in this intricate system. Downtime analysis helped identify faulty components and subsystems, which require the development of complementary maintenance plans to facilitate the replacement or fixing of parts. Proper practices of maintenance management improve the performance of the equipment. In this research, the efficiency of the LHD machine was assessed through reliability methods. Initially, the assumption of independent and identical distribution (IID) for the data sets was validated using trend and serial correlation analyses. The statistical tests indicated that the data sets adhered to the IID assumption. Therefore, a renewal process method was utilized for additional examination. The Kolmogorov-Smirnov (K-S) test was utilized to identify the most suitable distribution for the data sets. The theoretical probability distributions were estimated parametrically using the Maximum Likelihood Estimate (MLE) approach. The dependability of each separate subsystem was determined using the optimal fit distribution. Based on the reliability outcomes, preventive maintenance (PM) time plans were created to reach the targeted 90% reliability. Different maintenance strategies, in addition, were suggested to the maintenance team to extend the lifespan of the machine.

Inefficiencies in traditional spare parts management for aircraft maintenance—including excessive inventory costs, supply chain delays, and operational disruptions—have long hindered fleet readiness and increased maintenance expenditure. To address these challenges, an integrated, reliability-driven inventory optimization framework has been developed by leveraging predictive analytics, real-time sensor data, and emerging digital technologies. The proposed model is grounded in Reliability-Centered Maintenance (RCM) principles and enhanced by Artificial Intelligence (AI), the Internet of Things (IoT), and digital twin technologies. Through the deployment of advanced sensor networks, real-time performance data are continuously collected and analyzed to forecast component degradation and predict imminent failures. This enables the transition from time-based to condition-based maintenance scheduling. Predictive models, including Long Short-Term Memory (LSTM) neural networks and Random Forest classifiers, are employed to enhance the accuracy of failure prognostics and spare parts demand forecasting. The dynamic alignment of spare parts provisioning with actual equipment reliability has been shown to reduce overstocking and prevent critical shortages. A case study conducted within a commercial airline fleet demonstrated a 20% reduction in inventory-related costs and a 15% decrease in aircraft downtime. Furthermore, operational efficiency and safety were significantly improved by minimizing unscheduled maintenance events. The proposed framework not only supports predictive and prescriptive maintenance strategies but also establishes a replicable model for digital transformation in aviation logistics. By integrating real-time analytics with digital twin simulations, a data-centric paradigm is introduced for proactive maintenance decision-making. This advancement paves the way towards more sustainable, cost-effective, and resilient aviation operations, aligning with broader industry goals of environmental responsibility and performance optimization.

The problem of job scheduling in parallel machine environments, where both processing times and setup times are characterized by stochastic variability, has been investigated with a focus on enhancing the efficiency of resource allocation in complex production systems. Job scheduling, as a critical component of operations research and systems engineering, plays a vital role in the optimization of large-scale, flexible manufacturing and service environments. In this study, a stochastic scheduling model has been formulated to minimize the maximum completion time (denoted as $Ct_{\textit{max}}$), under the simultaneous influence of probabilistic job durations and setup times associated with tool preparation. The problem has been addressed using two prominent metaheuristic algorithms: Genetic Algorithm (GA) and Simulated Annealing (SA). These methods were selected due to their demonstrated capacity to navigate large, non-deterministic search spaces efficiently and their adaptability to multi-constraint scheduling problems. A comparative analysis has been conducted by applying both algorithms under identical initial conditions, with algorithmic performance evaluated in terms of solution quality, computational efficiency, and robustness to input variability. The model incorporates key practical considerations, including randomized setup times which are often neglected in conventional deterministic scheduling models, thereby improving its relevance to real-world industrial settings. The formulation of the problem allows for additional constraints and objectives to be flexibly integrated in future research, including resource conflicts, machine eligibility constraints, and energy-aware scheduling. Empirical results suggest that while both algorithms are effective in deriving near-optimal schedules, notable differences exist in convergence behavior and sensitivity to parameter tuning. The findings offer critical insights into the comparative strengths of GA and SA in managing the stochastic nature of parallel machine scheduling problems. By advancing a robust metaheuristic framework that accounts for real-world uncertainties, this study contributes to the ongoing development of intelligent scheduling systems in systems engineering, manufacturing logistics, and automated production planning.