Abdellah El Barkany

Background: Manufacturing supply chains must increasingly coordinate cost, environmental impact, and service continuity under demand uncertainty and limited capacity. Methods: This study develops a stochastic mixed-integer linear programming framework for carbon-aware production–distribution planning in an automotive supply chain. The model jointly optimizes production quantities, inventory levels, shipments, truck usage, and lost sales over a multi-period horizon. Demand uncertainty is represented through scenarios, while production- and transportation-related emissions are monetized using an internal carbon price. Lost-sales penalties capture service degradation when demand cannot be fulfilled by the focal plant, and a rolling-horizon analysis evaluates planning responsiveness as demand information is updated. The framework is applied to an industrially inspired, capacity-constrained automotive case with multiple products, production lines, destinations, and demand scenarios. Computational experiments assess carbon pricing, lost-sales penalties, demand volatility, deterministic versus stochastic planning, and rolling-horizon replanning. Results: Results show that carbon pricing mainly acts as an economic valuation mechanism under the studied fixed-structure configuration, whereas lost-sales penalties strongly influence service performance. Demand volatility increases unmet demand, and lower emissions may reflect lower fulfilled demand rather than improved efficiency. Conclusions: The study provides a decision-support framework for evaluating cost–carbon–service trade-offs under stochastic demand while acknowledging single-plant and fixed-routing limitations.

The growing complexity of construction projects and the need for sustainability have made supply chain optimization a crucial factor in enhancing performance, resilience, and decision-making. This study conducts a systematic review and bibliometric analysis of research on optimization and simulation techniques applied to construction supply chains. Based on 340 selected publications from Scopus and Web of Science (1997– 2024), the analysis identifies key research trends, major contributors, and emerging themes, highlighting the field’s multidisciplinary and collaborative nature. Using Biblioshiny, the study examines scientific output, citation metrics, and coauthorship networks, revealing a 15.05% annual growth rate and 31.47% international collaboration. Keyword analysis and visualization techniques, such as cooccurrence maps and thematic clustering, illustrate the evolution of research in this domain. The findings offer valuable insights for academics and practitioners, emphasizing the role of optimization and simulation in improving the efficiency and sustainability of construction supply chains.

In response to the increasing complexity of modern production environments driven by heightened competition, customer demands, and sustainability goals, this work presents a focused methodology for forming product families within reconfigurable manufacturing systems (RMSs). Unlike traditional approaches, our method emphasizes the practical implementation of the Analytic Hierarchy Process (AHP) and the Average Linkage Clustering (ALC) algorithm to optimize RMS configurations. By evaluating specific comparison criteria—such as assembly sequence, machining sequence, components, production tools, and production demand—we aim to enhance resource utilization and adaptability to market changes. The proposed methodology enables a systematic assessment of RMS performance tailored to diverse product requirements. A detailed example of machining systems demonstrates the use of machining sequence and tool usage as primary criteria, showcasing the practical application and decision-making capabilities of the approach. This work contributes to the field by providing a structured framework for decision-making in RMS, facilitating efficient and precise product family formation to meet evolving manufacturing demands.

Notwithstanding significant efforts to reduce the risk of bird strikes at and around airports, as well as advancements in technology, aviation safety continues to be threatened by this hazard. Our study of recent data shows a clear historical increase in accidents, therefore underscoring the flaws in present technologies. Although among the technologies in common use are radar, drones, cameras, and acoustic approaches, they are not always able to completely meet the challenging needs of bird strike risk mitigation. This approach creatively blends these four fundamental technologies by means of enhanced real-time data processing and targeted drone deployment. While providing more efficient and comprehensive preventive activities than present methods, this synergy helps quick and precise decision-making in crucial events. It incorporates a diagnostic system using contemporary information technology that forecasts medium- and short-term dangers, therefore enhancing the accuracy and efficacy of preventive measures. This combination of technology also makes it feasible to follow migrating birds and assemble all the gathered data in a single dashboard, therefore facilitating dynamic analysis of bird movements and improved visualization. This approach helps airports to concentrate more on administrative and preventive aspects. Thanks to more coverage and more flexibility to fit particular climatic conditions, this method covers all significant stages of flight, including takeoff, approach, and landing. It lowers false alerts, maximizes operating costs, and offers a sustainable means to prevent bird attacks, therefore enhancing aviation safety completely and efficiently.

Metal working process is one of the main activities in mechanical manufacturing industry; it is considered as a major consumer of energy and natural resources. In material removal process, the selection of cutting parameters and cooling or cutting liquid is necessary to save energy and achieve energy efficiency as well as sustainability. During the last two decades, the number of publications in this field has rapidly increased and has shown the importance of this research area. This review paper identifies and reviews in detail a total of 166 scientific studies which exhibit original contributions to the field and address multiple energy efficiency challenges. The recently developed models of energy consumption and different materials used in the machining process are presented. Therefore, this study describes various techniques for modeling and optimizing machining operations such as turning, milling, and drilling. Modeling techniques, experimental methods, multi-objective and single-objective optimization methods, and hybrid techniques optimization are presented in a detailed manner compared to previous review papers where only energy models are discussed. It can help practitioners and researchers to select the most appropriate approach for the desired experience and to highlight the progress of these methods in terms of machining energy efficiency. Additionally, this paper provides a review of different cutting fluids adopted in machining processes. This paper assists researchers and manufacturers in making advantageous technical decisions that have substantial economics in terms of energy saving.