Due to increased container traffic, the problems of congestion at terminal gates generate serious air pollution and decrease terminal efficiency. To address this issue, many terminals are implementing a truck appointment system (TAS) based on several concepts. Our work addresses gate congestion at a container terminal. A conceptual model was developed to identify system components and interactions, analyzing container flow from both static and dynamic perspectives. A truck appointment system (TAS) was modeled to optimize waiting times using a non-stationary approach. Compared to existing methods, our TAS introduces a more adaptive scheduling mechanism that dynamically adjusts to fluctuating truck arrivals, reducing peak congestion and improving resource utilization. Unlike traditional static appointment systems, our approach helps reduce truckers’ dissatisfaction caused by the deviation between the preferred time and the assigned one, leading to smoother operations. Various genetic algorithms were tested, with a hybrid genetic–tabu search approach yielding better results by improving solution stability and reducing computational time. The model was applied and adapted to the Port of Casablanca using real-world data. The results clearly highlight a significant potential to enhance sustainability, with an annual reduction of 785 tons of CO₂ emissions from a total of 1281 tons. Regarding trucker dissatisfaction, measured by the percentage of trucks rescheduled from their preferred times, only 7.8% of arrivals were affected. This improvement, coupled with a 62% decrease in the maximum queue length, further promotes efficient and sustainable operations.

The development of sustainable insulation materials plays a crucial role in creating energy-efficient and environmentally responsible buildings. This study investigates eco-friendly composite materials based on plaster and wood shavings for insulation purposes. Incorporating wood shavings into plaster improves thermal insulation and mechanical behavior by enhancing porosity, reducing density, and improving bonding. As the wood shaving content increases from 5% to 15%, the thermal conductivity decreases from 0.252 W/mK to 0.099 W/mK, reflecting superior insulating performance. Concurrently, thermal resistance rises, showcasing enhanced insulation. The material also demonstrates increased flexibility, with the Young’s modulus decreasing at higher wood shaving proportions. Numerical simulations confirm these observations, indicating a 12 K temperature drop for composites with 15% wood shavings compared to a 6 K drop for pure plaster. This study suggests that an insulation thickness of 6–7 cm for the 15% composite strikes the optimal balance between performance and cost-efficiency. The findings underscore the potential of wood shavings to significantly enhance the thermal efficiency and mechanical adaptability of plaster composites, promoting sustainable and effective building insulation solutions.

Emerging economies face mounting pressure to adopt sustainable and cost-efficient methods for delivering products and services in urban areas. This study examines the Electric Vehicle Routing Problem with Time Windows (EVRPTW) within a pragmatic urban context. We concentrate on the short-haul delivery network in Marrakesh, Morocco, whose operational viability is influenced by climatic, infrastructural, and regulatory limitations. We present a simulated annealing (SA) metaheuristic, augmented with repair heuristics and a penalty-based cost function, to concurrently reduce routing costs and lateness fines, subject to time-window and battery capacity restrictions. The technique undergoes evaluation through extensive computer tests utilizing realistic instance sets that replicate local demand patterns and charging infrastructure. The penalty-calibrated model demonstrates delivery completion rates of up to 100%, significantly reducing route costs and the number of unserved clients relative to baseline setups. We thoroughly analyze the tuning parameters among several runs. This study intends to provide a useful tool for real-world decision support by fusing extensive literature synthesis with local context validation and by integrating a simulation module that evaluates time-window settings and charging patterns under realistic traffic.

Outcome-Based Engineering Education (OBEE), a performance-driven approach at the forefront of curriculum design, offers a reliable and scalable framework for reforming engineering education. This research examines the industrial and logistics engineering major at the National School of Applied Sciences of Marrakesh as a case study to develop and implement a new hybrid model that merges the OBEE approach and Lean Management principles and methods through five layers. This paper presents the second and third layers of the Lean-OBEE architecture: the Target layer and Assessment layer, respectively. The target layer employs Hoshin Kanri’s X-Matrix in the OBEE process as a Lean strategic planning tool for visual and efficient management of the educational outcomes. Teachers and academic staff used the X-Matrix to monitor the unfolding of strategic educational objectives and progress throughout the course and curriculum. The assessment layer integrates a set of Lean principles, including PDCA (Plan-Do-Check-Act) cycles, Poka-Yoke, Flow, Muri, Standard Work, Takt Time, and Collective Intelligence, to design and assess the course session. The findings of this study provide preliminary evidence that the proposed Lean-OBEE model supports the development of sustainable engineering education by continuously improving the relevance and efficiency of the curriculum and teaching practices to meet the dynamic needs of industry and all stakeholders. This study serves as a practical reference for achieving the stated outcomes.