Effective replenishment planning and inventory control are essential for the smooth operation and adaptability of supply chains. These aspects play a pivotal role in upholding a company’s competitiveness and triumph in today’s fiercely competitive markets. Supply chain planners encounter significant hurdles in choosing the most appropriate suppliers in diverse scenarios, reducing average inventory levels, and determining optimal safety lead times. This research tackles these challenges by examining and evaluating a multi-period replenishment planning issue within the framework of dynamic demand and multiple suppliers. The suppliers are pre-selected and defined by procurement costs, with lead times considered as independent discrete random variables with known and limited probability distributions. The goal is to optimize the distribution of order quantities among these pre-selected suppliers while minimizing the anticipated total cost. Two strategies and corresponding linear models are suggested to investigate the impact of dividing orders between suppliers, order crossover, and order flexibility. Numerical experiments provide evidence that concurrently considering splitting and flexibility yields benefits in terms of cost optimization.

This article studies a bi-objective stochastic optimization problem for multi-period multi-sourcing supply planning. The formulated problem accounts for stochastic lead times, degressive pricing, holding and backlog costs, delivery flexibility costs, as well as both holding and transportation carbon footprint. The first objective is to minimize the expected total cost, while the second objective is to minimize the expected total footprint. These objectives must be achieved while adhering to suppliers’ capacity constraints and meeting customer demand. In this paper, the proposed stochastic integer linear program is detailed, and the ϵ-constraint method used to solve it is described. The first results of experiments are presented and discussed.

In order to meet item demands, end-of-life (EOL) product and subassembly ordering and disassembly schedules are determined by disassembly lot sizing, which is the subject of this study. We take into consideration a stochastic version with undetermined ordering lead time (OLT). In this case, OLT stands for the amount of time that passes between placing and receiving an order (we can only order EOL products). Throughout the planning horizon, scenarios are used to model the stochasticity. The objective is to reduce the expected total of setup, purchasing, inventory, and backlog expenses. This is achieved by expressing the problem as a two-stage mixed integer linear programming (2S-MILP) model across all potential scenarios. The 2S-MILP is unsolvable since it is predicated on every scenario conceivable. A Simultaneous Backward Reduction approach is proposed to make it tractable. To confirm the suggested method’s efficacy, it is assessed in a variety of environments.

Internet of Things (IoT) is commonly adopted in Industry 4.0/5.0, but generates an excessive amount of data that affects quality of service (QoS). To address this challenge, Fog-based architectures have emerged to enable faster and more efficient data processing. However, their deployment can be challenging, particularly when dealing with mobile connected devices like robots. To optimize the deployment of Fog-based solution in the presence of mobile connected devices, this article proposes an Integer Linear Programming (ILP) model to find the optimal network structure of hardware devices that will ensure complete coverage of the working area, good QoS, and cost minimization. The proposed model is tested in real and benchmark cases. Results show that the model is effective and robust in optimizing Fog-based solutions deployment while considering the dynamic aspect of mobile devices. Overall, the model offers a valuable approach for managing the increased data volume generated by IoT in industrial environments.

Industry 4.0 is based on machine learning and advanced digital technologies, such as Industrial-Internet-of-Things and Cyber-Physical-Production-Systems, to collect and process data coming from manufacturing systems. Thus, several industrial issues may be further investigated including, flows disruptions, machines’ breakdowns, quality crisis, and so on. In this context, traditional machine learning techniques require the data to be stored and processed in a central entity, e.g., a cloud server. However, these techniques are not suitable for all manufacturing use cases, due to the inaccessibility of private data such as resources’ localization in real time, which cannot be shared at the cloud level as they contain personal and sensitive information. Therefore, there is a critical need to go toward decentralized learning solutions to handle efficiently distributed private sub-datasets of manufacturing systems. In this article, we design a new monitoring tool for system disruption related to the localization of mobile resources. Our tool may identify mobile resources (human operators) that are in unexpected locations, and hence has a high probability to disturb production planning. To do so, we use federated deep learning, as distributed learning technique, to build a prediction model of resources locations in manufacturing systems. Our prediction model is generated based on resources locations defined in the initial tasks schedule. Thus, system disruptions are detected, in real time, when comparing predicted locations to the real ones, that is collected through the IoT network. In addition, our monitoring tool is deployed at Fog computing level that provides local data processing support with low latency. Furthermore, once a system disruption is detected, we develop a dynamic rescheduling module that assigns each task to the nearest available resource while improving the execution accuracy and reducing the execution delay. Therefore, we formulate an optimization problem of tasks rescheduling, before solving it using the meta-heuristic Tabu search. The numerical results show the efficiency of our schemes in terms of prediction accuracy when compared to other machine learning algorithms, in addition to their ability to detect and resolve system disruption in real time.

Industry 4.0 revolution aims to satisfy the manufacturing systems need to deal with the unexpected customers behaviour and market variation. Thanks to Internet of Things (IoT) technology, Industry 4.0 enables to collect and analyze real-time data about Cyber Physical System (CPS) components and hence to detect and react to emergent disruptive situations as quick as possible. In such context, tasks rescheduling becomes a crucial research topic, which aims to revise the initial schedule in cost-effective way. In this paper, we focus on system disruption related to resources unavailability of a resource, or when it is in an unexpected location. We propose a new tasks rescheduling module based on a reference schedule generated by an Initial Planning and Scheduling system (IPS). Our module considers the main schedule objective and aims to assign tasks to the nearest resources while improving the execution accuracy. To do so, we formulate an optimization problem of tasks rescheduling, before solving it using the meta-heuristic Tabu-search. The experimental results show the efficiency of our module to optimize the tasks rescheduling when considering both localization and accuracy information, in addition to the ability of Tabu-Search algorithm finding an optimal solution.