The textile industry has witnessed remarkable growth, largely driven by the expansion of e-commerce. To meet the increasing demand for faster order fulfillment, warehouses are adopting advanced automation technologies. This study focuses on the idle time analysis within a goods-to-person order picking system in an automated warehouse. As a first step, a cause-and-effect tree is developed to systematically identify potential factors that contribute to idle time. Subsequently, the most significant indicators are determined through a mathematical model based on the Least Squares Principle. This approach prioritizes the indicators that have the most impact, enabling a targeted analysis of the root causes of idle time and providing valuable insights to improve system efficiency.

Healthcare systems are essential to society but face numerous challenges in maintaining their resilience and long-term viability. Disruptions, much like those experienced in supply chains-can significantly hinder operational continuity. To mitigate such risks, strategic investments can be made to reduce the likelihood of service interruptions. However, given the constraints of a limited protection budget, it is crucial to allocate resources efficiently to enhance the resilience of selected critical facilities. The objective of this research is to develop an optimal protection strategy that minimizes both patient travel distances for accessing care and the number of patients left without timely treatment due to disruptions. Since accurately predicting facility failures remains difficult, robust optimization provides a valuable framework. In particular, a scenario-based robust decision-making approach allows for the evaluation of multiple plausible disruption scenarios, ensuring that the selected strategy performs well under various conditions. Our methodology leverages scenario analysis to identify the most effective investment strategy for sustaining healthcare system performance over time. Each scenario corresponds to a different allocation of protective investments, and we assess their respective impacts on system resilience. By analyzing the relationship between the fortification of specific facilities and overall system performance, we generate insights to inform budget allocation decisions aimed at maximizing resilience. This process helps quantify the relative importance of each facility, enabling an optimized distribution of resources within fixed budgetary limits.

Outpatient clinics are integral to healthcare, offering vital services without the need for hospital admission. However, appointment scheduling in these settings remains challenging due to uncertainties such as patient no-shows and variable service times. This study proposes a data-driven approach to minimize physician idle time and patient waiting time by analyzing an eight-year (2016-2023) dataset from primary and specialized care for American veterans. Adopting the Cross Industry Standard Process for Data Mining (CRISP-DM) methodology, four predictive models: Random Forest, Extreme Gradient Boosting (XGBoost), Adaptive Boosting (AdaBoost), and Artificial Neural Networks (ANN), were developed for both classification (noshow) and regression (service time) tasks. The ANN model demonstrated superior predictive performance in both domains. The key predictors of no-shows included waiting time, type of care and care provider, while type of care, care provider, and veteran ZIP code were the most influential in forecasting service time. These findings highlight the potential of machine learning to improve appointment scheduling in outpatient clinics.

This paper introduces a novel pre-positioned inventory model aimed at mitigating supply chain disruptions by enhancing the resilience of supply networks characterized by multiple facilities subject to disruptions. Based on a Time-To-Recover model, we explore a single-tier supply chain framework, incorporating real-world disruption scenarios to assess the efficacy of pre-positioned inventories in disruption mitigation. A two-stage stochastic programming approach is used to formulate the problem, incorporating a special case scenario that allows for the development of a closed-form equation. This enables a detailed analysis of the impact of pre-positioned inventory on supply chain resilience, examining various scenarios to ascertain the optimal inventory levels required to mitigate disruption risks effectively. Some numerical examples are presented to illustrate the practical application of the model, offering valuable insights into the strategic positioning of inventories and the implications for supply chain design.

Quality control is essential in the manufacturing and production sectors. This study advances Incoming Quality Control (In-QC) by proposing a new data-driven sampling plan that optimizes decision-making for quality inspection with a system of partial control. This system is characterized by specific parameters that facilitate strategic decisions. The methodology’s efficacy is validated through targeted simulations, followed by a practical application in a real-world case study. This dual-phase evaluation underscores the approach’s utility and effectiveness, aiming to improve In-QC practices with significant implications for the industry.

The addressed problem considers a market model where a firm produces and sells a single product over a finite horizon divided into different periods. The firm aims to set the price of each period such that the total profit is maximized, while also satisfying constraints on available production capacity and price bounds. The market demand for each period is represented by the multinomial logit (MNL) model. This problem has been previously tackled in the literature, where it was formulated as a non-convex nonlinear programming (NLP) model. The solution approach involved the integration of neural networks and evolutionary algorithms. In this paper, the initial non-convex (NLP) model is transformed into a convex one based on properties of the MNL model. This new formulation enables the resolution of the addressed problem in an optimal way. A large experimental study is carried out to evaluate the solution quality and computational times of the two models. The obtained results show the effectiveness of the convex formulation compared to the non-convex one.

This paper addresses the problem of scheduling different non-preemptive jobs on a single machine under time of use electricity tariffs consideration. The considered machine has three main states (OFF, ON, Idle) and two transition states (Turn-on and Turn-off), Each of these machine's states as well as the processing jobs, consume a specific amount of energy. Moreover, a speed-scalable case of the problem is considered, in which jobs can be processed at an arbitrary speed with a trade-off between speed and energy consumption. First, an integer linear programming model with the objective of minimizing total energy consumption costs formulates this scheduling problem. Then, since this problem is strongly NP-hard, different approximate optimization methods are investigated to provide near-optimal solutions. Finally, an extensive computational study is carried out to establish the efficiency of the proposed algorithms. The obtained results show, how a well-tuned genetic algorithm combined with an adequate local search procedure constitutes an efficient method for solving this energy-aware scheduling problem.

Improving the efficiency of healthcare organizations requires primarily improving the quality of service and the well-being of medical staff. Nursing staff is a crucial human resource for which better schedules lead to better service quality, higher job satisfaction, and well being. Consequently, the Nurse Scheduling Problem has drawn significant attention during the last few decades. In practice, the schedules are usually generated manually by head service nurses. However, it is often both difficult and time consuming. Hence, in this paper, a mathematical model is proposed to solve the Nurse Scheduling Problem for a real case study from a French hospital. The proposed approach offers the possibility of a long time horizon planning satisfying work and regulation constraints, workload and shift-type balance, and nurse preferences. The results approve the effectiveness of the proposed approach in comparison with the manual schedule.

In recent years, climate change and health crises have multiplied, including the global pandemic of Covid-19. These crises have revealed the vulnerability of healthcare systems, but also their ability to adapt quickly and become resilient. However, this adaptation often comes at a cost. To address this problem, it is necessary to identify and prioritize measures to strengthen the current health system and make it more able to cope with future crises. One approach is to strengthen critical assets through effective investment strategies. The purpose of this study is to identify an optimal investment strategy to improve the reliability of health systems. We proposed a bi-objective investment strategy to strengthen healthcare systems against random disruptions. We contribute to the literature with a novel two-step modeling approach. First, we model the patient journey through the healthcare system as a Markov chain. The Markov chain allows us to capture the sequential nature of the patient journey and to analyze the probability of patient transitions between different stages in the system. Second, we have a bi-objective model mathematical model that calculates the optimal investment strategy based on the probabilities established earlier in order to minimize both patient travel distance and the number of patients waiting for care. The model and its computational results with a full enumeration approach are presented in the paper. The existence of a large number of facilities and investment levels increases the time needed to evaluate all solutions and find the optimal set of solutions. Therefore, it is important to consider the need for granularity in the investment levels when applied to real-world situations.

This paper proposes a novel approach for the improvement of production quality in knitting workshops in the textile industry. The method is based on two steps. The first step consists of analyzing the performance of each operator to detect the different types of quality defects on the different machines of the workshop. The second step consists of optimizing the assignment of the operators to the different machines taking into account their performance in order to maximize the detection rate of the quality defects. The evaluation of the operator's performance is carried out using a multi-criteria analysis method called TOPSIS: Technique for Order by Similarity to the Ideal Solution. This performance indicator serves as a crucial input for the second proposed method, which is an optimized model for assigning operators in the workshop taking into account different constraints. The proposed approach is tested based on a real industrial configuration and the obtained results show its effectiveness.

This paper deals with optimizing the location of multi-sources power plants, using renewable energies, based on a given statistical data and satisfying economic, environmental and social criteria. In the classical version of the goal-programming model, the different types of plants should be located in order to minimize the total deviations from predefined goals; keeping in mind that the parameters are place dependent. However, this paper shows that relaxing the constraints leads to more feasible solutions and improves some criteria. We introduce place-dependent parameters for placing the different power plants. Since different types of plants can be positioned in each location, we combined the goal-programming model with a flow control approach to evaluate the location frequencies distribution of power plants to each position in order to identify a characteristic energy map. The obtained results are based on numerical experiments inspired from the literature. The results show the effectiveness and the efficiency of the proposed approach.