The coronavirus disease 2019 (COVID-19) placed significant and unprecedented challenges on the Tunisian public healthcare system. Healthcare facilities were overwhelmed by a surge in demand, resulting in crisis-level shortages of essential healthcare resources, including qualified personnel, respiratory support equipment, and Intensive Care Unit (ICU) beds. This resource scarcity created a critical imbalance between demand and healthcare system capacity, severely compromising the ability to provide adequate and timely patient care during the COVID-19 crisis. Accurate prediction of future case numbers and medical equipment needs is crucial to assist healthcare facilities in optimizing resource allocation during the COVID-19 epidemic surges. This paper presents a Long Short-Term Memory (LSTM) recurrent neural network model to forecast the required number of intensive care unit (ICU) beds. Various error quantification metrics measure the LSTM model’s performance, such as the R2, MAE, MSE, and RMSE. Despite the proposed LSTM model demonstrating good predictive performance, we observe a deviation of approximately 3 beds between the predicted values and the actual number of occupied beds, which can be significant in Tunisian public hospitals during the COVID-19 outbreak.

Accurate forecasting of hospital-specific requirements during healthcare crises enables decision-makers to tackle key strategic challenges, such as determining the optimal allocation of beds, respiratory support machines, and other critical medical equipment. Identify departments that may need to suspend operations temporarily to efficiently reallocate their resources, and assess the feasibility of resource pooling, inter-hospital sharing, and patient transfers to optimize capacity during peak demand periods. This study evaluates the forecasting accuracy of LSTM, Bi-LSTM, ARIMA, Holt-Winters, and Exponential Smoothing models in predicting demand for ventilators and oxygenation devices over two weeks during the COVID-19 pandemic. Utilizing a local dataset with detailed records of respiratory support devices provides a more precise predictive analysis of critical hospital resource demand, thereby optimizing resource allocation and improving hospital preparedness during health crises. The proposed models were evaluated using MAPE, RMSE, MSE, MAE, and R-squared. While ARIMA outperformed LSTM in terms of accuracy and model fit, it struggled to capture sudden surges in resource demand, limiting its reliability during crisis peaks. LSTM exhibited lower accuracy, likely due to the lack of long-term dependencies and limitations of the dataset. Among all models, the Bi-LSTM showed the highest predictive accuracy, aligning closely with actual observations and achieving the lowest mean absolute error (MAE), making it the most suitable approach for forecasting hospital resources during critical periods.

The 3D-bin packing problem consisted of making the suitable decision on how to arrange a set of rectangular objects in their bins. It entails optimizing the allocation of a set of items into a minimal number of bins, each with a fixed capacity. In our paper, we are interested to a three-dimension bin-packing problem, which is a combinatorial optimization problem and is known to be NP-Hard problem. We refer to the recent research work to create our hierarchical hybrid optimization named GenSA-3DBPP to solve the three-Dimension bin-packing problem. The empirical study aims to highlight the improvements made with GenSA-3DBPP on three levels. To solve this issue, we proposed The threeDorientationsII heuristic, which places the item in the orientation that maximizes load while respecting weight constraints. The Genetic Algorithm level explores the search space to tackle the main purpose of the problem, which is the minimization of both number of bins and wasted space. The Simulated Annealing level utilizes the Genetic Algorithm’s best solution as input and generates a best solution.

Aggregate production planning and disaggregate production planning are two key steps to improve efficiency of production planning systems. Solving them separately can decrease complexity and be adapted to the standard structure of an organization. Nevertheless, interaction between these planning levels is major to prevent the obtaining of infeasible and inconsistent plans. Additionally, the optimization by sub-problem usually incurs a global problem with suboptimal results. To cope with this, an integrated model considering both levels becomes fundamental. This model is based on using linking constraints, aggregation constraints or verification constraints. Computational tests considering data from literature and real data have been done to compare the performance of hierarchical production planning with integrated one. It was shown that the integrated approach based on verification constraints outperforms efficiently the hierarchical one.

New healthcare units called "Covid units" dedicated to the medical care of individuals infected by the Coronavirus disease have been rapidly established under enormous pressure. This reflects the strong commitment of the Tunisian national healthcare system and public action in combating the COVID-19 pandemic. From this perspective, this study aims to evaluate the effectiveness of these units in providing effective and timely responses to the affected communities. A real Covid-19 unit at the University Hospital SAHLOUL in Sousse, Tunisia is modeled and simulated using ARENA simulation software. The simulation model analyzes the performance of the current healthcare Covid unit by providing relevant statistics on the patient flow and resource utilization rates. The simulation results identify barriers to the efficiency of the Covid care unit and question the relevance of the current distribution of hospital resources during the Covid-19 pandemic. To help healthcare managers evaluate alternative choices and identify potential solutions to optimize critical resources management, "what if" models are executed to address bottlenecks in different stages of Covid service and improve resource allocation. The simulation results of the proposed scenarios demonstrate a significant improvement, reducing the average patient waiting time by 93%, and increasing, in turn, the average daily throughput to 36,51%

This paper introduces two three-level hierarchical frameworks, one for the production campaign planning and other for the production planning and scheduling for multi product multi stage batch plants. Each level in each framework is formulated as a mixed integer linear programming model. These models are solved sequentially where the output of each campaign production planning model presents the input of the corresponding planning level model. Using data from the literature, the proposed optimization models were tested and validated.

Several processes constituting the supply chain contain a common step participating in their improvements. It‘s the step of filling the products in their containers. We are talking about the bin-packing problem, which consists of finding the most economical storage of a set of objects in a finite number of bins that minimize the total of bins used.In our paper, we are interested to a three-dimension bin-packing problem, which is a combinatorial optimization problem and is known to be NP-Hard problem. We refer to two recent research works to create our multi-stage optimization named FillAbin to solve the three-Dimension bin-packing problem. The empirical study is based on two main plans. The first one is inspired from a real case in the PROMENS Company. The second plan is based on a benchmark used by two recent works. The obtained results showed the improvement realized by the proposed algorithm.

For better supply chain management, it is necessary to think about better managing its cost sources. Inventory is considered the most important element of the supply chain that generates the different logistics costs, mainly the inventory holding cost and the transportation cost. One of the most widely used models to jointly solve these two problems is the Inventory Routing Problem (IRP), which will be the focus of this study. The proposed model in this work deals with a two-tier supply network. The first level contains the supplier with a single vehicle to serve a set of customers with a deterministic and periodic demand that are located at the second level. Our work consists in studying the effect of the increase of the replenishment lead time on the different logistic costs. In addition, we introduced the Lateral Transshipment (LT) technique as an option for inventory transfer if it is economical. New mathematical models corresponding to the above-mentioned problems have been developed and solved by an exact method. The obtained results show that the variation of the replenishment lead time leads to an increase of the different logistics costs and that LT can improve the total network cost and balance the customers' inventory level.

This study considers the application of a hybrid genetic algorithm (HGA) to the Inventory Routing Problem (IRP), that aims to minimize the cost of the total distance traveled over a time horizon discretized in periods, while guaranteeing that the customers do not incur a stock-out event. The proposed algorithm is tested using the instances proposed by Archetti et al. [1] and [2] where we consider only one vehicle available at the supplier. This instance is the biggest one solved so far. In this paper, computational results are given by an HGA, showing that this approach is competitive with other search and simulated annealing in terms of solution time and quality.