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.

This paper introduces a novel information sharing mechanism, the Improved Information Sharing Mechanism (I2SM), an adaptive real-time framework designed to enhance the performance of metaheuristic algorithms. I2SM dynamically collects and evaluates critical metrics, such as improvement rates and stagnation levels, through parallel processing, enabling real-time actions such as hybridization and parameter tuning. The mechanism’s adaptive nature ensures efficient handling of diverse optimization challenges by dynamically balancing exploration and exploitation, with a reasonable tradeoff in execution time.To assess the performance of the proposed I2SM mechanism, we selected the Particle Swarm Optimization (PSO) algorithm as a representative test framework. Empirical results from various benchmark functions demonstrate that PSO integrated with I2SM achieves superior performance, outperforming standard PSO in 90% of the cases. Although I2SM-PSO incurs a slightly higher execution time compared to standard PSO, significant improvements in solution quality validate its efficiency. However, this increase in execution time highlights a limitation that should be addressed in future research to optimize computational efficiency while maintaining performance gains.

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.

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 an application of constraint programming (CP) for a hybrid flexible flow shop scheduling problem (HFFS) with realistic features, such as job-dependent and sequence-dependent setup times, due dates, and waiting policies, with the aim of minimizing the total tardiness, so that the workshop can fulfill the delivery deadline. After describing all the characteristics of the proposed problem, we formulate it using CP model. Numerical simulations are prformed to evaluate its performance. The results show that the proposed CP model is highly effective in finding good quality solutions for both small-sized and large-sized instances. The conducted experiments, using case study instances, show that our model performs well at exploring optimal solutions in a short computational time and hence allows us to find good quality solutions. The proposed method can be easily adjusted to a huge number of real-life environements for scheduling problems with similar features.

Parallel machine scheduling problems are among the most studied scheduling problems in the literature. We study the unrelated parallel machine scheduling problem with release dates and machine-and sequence-dependent setup times to minimize the makespan. We introduce three heuristics, five local search methods and three metaheuristics for the problem: the Late Acceptance Hill Climbing and two variants of Simulated Annealing. Furthermore, we introduce a three-set 1620-instance benchmark in which the number of jobs and machines, release dates, processing and setup times are generated according to existing procedures. The proposed approaches are analyzed and compared on the proposed benchmark. We compare the proposed heuristics and derive the best heuristic to be used to generate the initial solution of the metaheuristics. Moreover, we show that the different metaheuristics are efficient, each performing best on one of the sets.

International audience

International audience

In mobile sensor networks, it is important to manage the mobility of the nodes in order to improve the performances of the network. This paper addresses the problem of single target tracking in controlled mobility sensor networks. The proposed method consists of estimating the current position of a single target. Estimated positions are then used to predict the following location of the target. Once an area of interest is defined, the proposed approach consists of moving the mobile nodes in order to cover it in an optimal way. It thus defines a strategy for choosing the set of new sensors locations. Each node is then assigned one position within the set in the way to minimize the total traveled distance by the nodes. While the estimation and the prediction phases are performed using the interval theory, relocating nodes employs the ant colony optimization algorithm. Simulations results corroborate the efficiency of the proposed method compared to the target tracking methods considered for networks with static nodes.

This paper considers the allocation of reliability and redundancy to parallel-series systems, while minimizing the cost of the system. It is proven that under usual conditions satisfied by cost functions, a necessary condition for optimal reliability allocation of parallel-series systems is that the reliability of the redundant components of a given subsystem are identical. An optimal algorithm is proposed to solve this optimization problem. This paper proves that the components in each stage of a parallel-series system must have identical reliability, under some nonrestrictive condition on the component's reliability cost functions. This demonstration provides a firm grounding for what many authors have hitherto taken as a working hypothesis. Using this result, an algorithm, ECAY, is proposed for the design of systems with parallel-series architecture, which allows the allocation of both reliability and redundancy to each subsystem for a target reliability for minimizing the system cost. ECAY has the added advantage of allowing the optimal reliability allocation in a very short time. A benchmark is used to compare the ECAY performance to LM-based algorithms. For a given reliability target, ECAY produced the lowest reliability costs and the optimum redundancy levels in the successive reliability allocation for all cases studied, viz, systems of 4, 5, 6, 7, 8, 9 stages or subsystems. Thus ECAY, as compared with LM-based algorithms, yields a less costly reliability allocation within a reasonable computing time on large systems, and optimizes the weight and space-obstruction in system design throughout an optimal redundancy allocation.