Mohamed Gheith

Efficiently operating a single microgrid (MG) is increasingly challenging due to volatile electricity demand and intermittent renewable generation. Traditional static networks often fail to adapt to these fluctuations, compromising reliability. Incorporating these uncertainties into planning is essential for developing resilient optimization models that can withstand the stochastic nature of decentralized energy systems. This study proposes a dynamic reconfiguration strategy for interconnected microgrids that reroutes households based on real-time supply and demand. A stochastic nonlinear optimization model was developed to maximize load factors and flatten peaks while accounting for current-dependent power and distribution losses. The Sample Average Approximation (SAA) method was used to handle uncertainty, converting probabilistic variables into a robust deterministic equivalent that prioritizes electrical proximity during reconfiguration. The model was validated using a composite dataset spanning nearly two years of hourly load and renewable profiles. A total of 600 stochastic scenarios were considered and analyzed to represent an empirical distribution of real-world uncertainty while preserving key temporal correlations. Performance was tested under N-1 and N-2 contingency events, in which one or more microgrids are deactivated, to evaluate system resilience. Results indicate that while a single active MG improves the load factor, it also increases operational instability and objective function variance. Conversely, a three-MG configuration enhances system stability and predictability. Economically, the mesh architecture allows for temporary MG deactivation to reduce maintenance and fuel costs without compromising service. The proposed strategy achieves 100% resilience, ensuring uninterrupted service even under severe constraints.

Coordinating operating room schedules with downstream inpatient bed availability remains a critical challenge for hospitals, particularly under emergency-driven uncertainty. Emergency arrivals introduce variability that propagates congestion across surgical and inpatient systems, reducing elective surgery throughput and resource utilization. Existing approaches often treat operating rooms and inpatient beds as isolated planning problems, limiting the ability to anticipate system-wide congestion effects. This study proposes a system-level decision-support framework that integrates elective operating room scheduling, emergency arrivals, and inpatient bed capacity within a unified stochastic optimization model. Uncertainty in surgical duration and patient length of stay is represented through scenario-based stochastic modeling. Computational experiments examine system performance under varying levels of emergency demand and bed availability. The results identify critical congestion thresholds beyond which elective throughput deteriorates rapidly, highlighting the role of downstream bed constraints in governing system capacity under uncertainty. The proposed framework provides hospital managers with practical insights for coordinated surgical and inpatient capacity planning, bridging operations research optimization with operations management principles at the system level.

Financial Portfolio Optimization Problem (FPOP) is a cornerstone in quantitative investing and financial engineering, focusing on optimizing assets allocation to balance risk and expected return, a concept evolving since Harry Markowitz’s 1952 Mean-Variance model. This paper introduces a novel meta-heuristic approach based on the Black Widow Algorithm for Portfolio Optimization (BWAPO) to solve the FPOP. The new method addresses three versions of the portfolio optimization problems: the unconstrained version, the equality cardinality-constrained version, and the inequality cardinality-constrained version. New features are introduced for the BWAPO to adapt better to the problem, including (1) mating attraction and (2) differential evolution mutation strategy. The proposed BWAPO is evaluated against other metaheuristic approaches used in portfolio optimization from literature, and its performance demonstrates its effectiveness through comparative studies on benchmark datasets using multiple performance metrics, particularly in the unconstrained Mean-Variance portfolio optimization version. Additionally, when encountering cardinality constraint, the proposed approach yields competitive results, especially noticeable with smaller datasets. This leads to a focused examination of the outcomes arising from equality versus inequality cardinality constraints, intending to determine which constraint type is more effective in producing portfolios with higher returns. The paper also presents a comprehensive mathematical model that integrates real-world constraints such as transaction costs, transaction lots, and a dollar-denominated budget, in addition to cardinality and bounding constraints. The model assesses both equality/inequality cardinality constraint versions of the problem, revealing that the inequality constraint tends to offer a wider range of feasible solutions with increased return potential.

Background: Container terminals (CTs) have constantly administered truck appointment systems (TASs) to effectively accomplish the planning and scheduling of drayage operations. However, since the operations in the gate and yard area of a CT are stochastic, there is a need to incorporate uncertainty during the development and execution of appointment schedules. Further, the situation is complicated by disruptions in the arrival of external trucks (ETs) during transport, which results in congestion at the port due to unbalanced arrivals. In the wake of Industry 4.0, simulation can be used to test and investigate the present CT configurations for possible improvements. Methods: This paper presents a simulation optimization (SO) and simulation-based optimization (SBO) iteration framework which adopts a dual transactions approach to minimize the gate operation costs and establish the relationship between productivity and service time while considering congestion in the yard area. It integrates the use of both the developed discrete event simulation (DES) and a mixed integer programming (MIP) model from the literature to iteratively generate an improved schedule. The key performance indicators considered include the truck turnaround time (TTT) and the average time the trucks spend at each yard block (YB). The proposed approach was verified using input parameters from the literature. Results: The findings from the SO experiments indicate that, at most, two gates were required to be opened at each time window (TW), yielding an average minimum operating cost of USD 335.31. Meanwhile, results from the SBO iteration experiment indicate an inverse relationship between productivity factor (PF) values and yard crane (YC) service time. Conclusions: Overall, the findings provided an informed understanding of the need for dynamic scheduling of available resources in the yard to cut down on the gate operating costs. Further, the presented two methodologies can be incorporated with Industry 4.0 technologies to design digital twins for use in conventional CT by planners at an operational level as a decision-support tool.

Background: Agro-food supply chains possess specific characteristics due to the diverse nature of products involved and contribute to all three pillars of sustainability, making the optimal design of a sustainable agro-food supply chain a complex problem. Therefore, efficient models incorporating the unique characteristics of such chains are essential for making optimal supply chain decisions and achieving economically and environmentally sustainable agro-food supply chains that contribute to global food security. Methods: This article presents a multi-objective mixed-integer linear programing model that integrates agricultural-related strategic decisions into the tactical design of an agro-food supply chain. The model considers transportation, inventory, processing, demand fulfilment, and waste disposal decisions. It also accounts for seasonality and perishability, ensuring a comprehensive approach to sustainability. The model aims to maximize the total generated profits across the supply chain while simultaneously minimizing CO₂ emissions as a measure of environmental impact. Results: By implementing the model on a sugar beet supply chain in the Netherlands, strategic crop rotation farm schedules for the crop rotation cycle and the optimum supply network decisions are obtained. Furthermore, different objectives are analyzed and the Pareto-efficient frontier is investigated to analyze the underlying trade-offs. Additionally, the model serves as a decision support tool for managers facilitating informed investment decisions in technologies that prolong product shelf life while maintaining profitability. Conclusions: The proposed multi-objective model offers a valuable framework for designing economically and environmentally sustainable agro-food supply chains. By aligning with sustainability goals and providing decision support, this research contributes to enhancing global food security and promoting sustainable resource utilization.

Background: The key performance index for the container terminals is the vessel berthing time which is highly affected by the scheduling of the different handling equipment. Proper integrated scheduling of the handling equipment is crucial, especially in automated container terminals, where all the handling equipment is automated and must be coordinated to avoid interference. One of the most challenging problems both scholars and terminal operators face is introducing a proper scheduling plan for different equipment, considering the buffer capacity of dual-trolley quay cranes (QCs) and the limited storage locations of import containers. Methods: A mathematical model is proposed to integrate the scheduling of automated yard cranes and automated guided vehicles (AGVs), considering the limited buffer capacity beneath dual-trolley QCs and the storage allocation of import containers. Results: different instances were solved to evaluate the proposed model’s performance and investigate the impact of using dual-trolley QCs instead of single-trolley QCs, and the impact of using different buffer capacities. Conclusions: The results show that the model provides detailed scheduling and assigning plans for the YCs and AGVs besides allocating import containers. Additionally, the dual-trolley QCs can significantly decrease the completion time and increase AGVs’ utilization compared to the single-trolley QCs.

Background: With the increasing number of International Medical Graduates (IMGs) hired by the UK National Healthcare Service (NHS), a unification of training becomes essential. One of the main obstacles facing IMGs is insufficient simulation-based training [1]. In 2021, 19,977 doctors were registered to the General Medical Council (GMC). 10,009 of them studied medicine outside the United Kingdom (UK) [2]. Blended learning offers the opportunity for engagement and interaction to facilitate learning experiences [3]. Methods: We created and distributed a survey to Anaesthetic trainees in the ‘Kent Surrey Sussex (KSS)’ Deanery. We received a total of 76 responses, 49 (64.5%) UK graduates (UKGs) and 27 (35.5%) international graduates. The survey was designed to ascertain their medical training, simulation experience, and their perception of that simulation experience. We also included additional questions aimed specifically at IMGs to demonstrate the best way they could be supported to acquaint themselves with the simulation process further. Findings: IMGs were less likely to have simulation training as an undergraduate (24% IMGs vs 96% UKGs), and 37% of IMGs had no simulation exposure before coming to the UK. IMGs were also exposed to fewer simulation sessions during their career when compared with UKGs. When asked how simulation can improve human factor variables, including teamworking, leadership, and managing stressful situations, both groups responded similarly. An exception was that IMGs were much more likely to ‘strongly agree’ that simulation improved their communication skills (41% vs 29%). IMGs were less likely to find the overall simulation experience ‘excellent’ (24% vs 51% in UKGs), suggesting more can be done to improve their perception of simulation (Table 1). Most IMGs (88.9%) believed they would benefit from blended learning. These included methods such as; an introductory video describing simulation (59%), pre-course materials (59%), manikin familiarisation (59%), a virtual tour (52%), and attending as an observer before simulation sessions (56%). Table 1: Comparison of UKGs versus IMGs simulation experience. As well as asking about how simulation improved communication skills, responders were asked the impact simulation had in improving other core skills including teamwork, leadership, and managing stressful situations with both groups responding similarly. Conclusion: There is a clear discrepancy in simulation exposure and perception of simulation between IMGs and UKGs. We believe blended learning may be the solution to help bridge this gap. Our survey highlighted potential resources we can introduce, including introductory videos, additional learning materials, and attending simulation as an observer. We hope to incorporate some of these in the future within our Trust Simulation Department and, if successfully received, expand throughout the KSS deanery. References 1. Martinerie L, Rasoaherinomenjanahary F, Ronot M, Fournier P, Dousset B, Tesnière A, Mariette C, Gaujoux S, Gronnier C. Health care simulation in developing countries and low-resource situations. J Contin Educ Health Prof. 2018;38(3):205–212 2. Campbell D. NHS hiring more doctors from outside UK and EEA than inside for first time. The Guardian. 8 June 2022. https://www.theguardian.com/society/2022/jun/08/nhs-hiring-more-doctors-from-outside-uk-and-eea-than-inside-for-first-time. [Accessed on 29/09/2022] 3. Singh H. Building effective blended learning programs. Educational Technology. 2003; 43:51–54 Background: With the increasing number of International Medical Graduates (IMGs) hired by the UK National Healthcare Service (NHS), a unification of training becomes essential. One of the main obstacles facing IMGs is insufficient simulation-based training [1]. In 2021, 19,977 doctors were registered to the General Medical Council (GMC). 10,009 of them studied medicine outside the United Kingdom (UK) [2]. Blended learning offers the opportunity for engagement and interaction to facilitate learning experiences [3]. Methods: We created and distributed a survey to Anaesthetic trainees in the ‘Kent Surrey Sussex (KSS)’ Deanery. We received a total of 76 responses, 49 (64.5%) UK graduates (UKGs) and 27 (35.5%) international graduates. The survey was designed to ascertain their medical training, simulation experience, and their perception of that simulation experience. We also included additional questions aimed specifically at IMGs to demonstrate the best way they could be supported to acquaint themselves with the simulation process further. Findings: IMGs were less likely to have simulation training as an undergraduate (24% IMGs vs 96% UKGs), and 37% of IMGs had no simulation exposure before coming to the UK. IMGs were also exposed to fewer simulation sessions during their career when compared with UKGs. When asked how simulation can improve human factor variables, including teamworking, leadership, and managing stressful situations, both groups responded similarly. An exception was that IMGs were much more likely to ‘strongly agree’ that simulation improved their communication skills (41% vs 29%). IMGs were less likely to find the overall simulation experience ‘excellent’ (24% vs 51% in UKGs), suggesting more can be done to improve their perception of simulation (Table 1). Most IMGs (88.9%) believed they would benefit from blended learning. These included methods such as; an introductory video describing simulation (59%), pre-course materials (59%), manikin familiarisation (59%), a virtual tour (52%), and attending as an observer before simulation sessions (56%). Table 1: Comparison of UKGs versus IMGs simulation experience. As well as asking about how simulation improved communication skills, responders were asked the impact simulation had in improving other core skills including teamwork, leadership, and managing stressful...

Background: Scheduling the arrival of external trucks in container terminals is a critical operational decision that faces both terminal managers and trucking companies. This issue is crucial for both stakeholders since the random arrival of trucks causes congestion in the terminals and extended delays for the trucks. The objective of scheduling external truck appointments is not only to control the workload inside the terminal and the costs resulting from the excessive waiting times of trucks but also, to reduce the truck turnaround time. Methods: A binary programming model was proposed to minimize the waiting time cost, demurrage cost, and container delivery cost. Moreover, a sensitivity analysis was performed to compare various scenarios in terms of cost and to study to what extent the workload level is affected. The mathematical model was solved using Gurobi© 8.1.0 software. Results: 30 instances found in the literature were solved and evaluated in terms of the objective function value (i.e., cost) and truck turnaround time before and after controlling the workload inside the container terminal using the new proposed constraint. Conclusions: The obtained results showed a better distribution of the terminal workload, as well as a lower truck turnaround time that reduces the total cost.