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The pressing need to address climate change emphasizes the critical importance for industries to adopt energy transition strategies and integrate sustainable practices into their daily operations. Among the viable alternatives, integrating intermittent renewable energy into production activities stands out. In this context, a novel mathematical model designed to meet the challenges of lot sizing and production scheduling within a flexible flow line while integrating solar photovoltaic energy (PV) is proposed. To address the inherent complexity and uncertainty of the problem, an innovative solution is set up, drawing on all the advantages of a multi-agent system. The effectiveness of the proposed approach is evaluated through numerical experiments carried out on a benchmark case, along with a comparative study to display its performance in comparison with an automatic solver.

with climate change, many companies are looking to reduce their carbon footprint and ensure a sustainable manufacturing. To meet this challenge, one of the alternatives is to replace carbon intensive processes with low-carbon processes involving electrical and/or renewable energies. Within this scope, a novel scheduling approach is proposed to take into account the introduction of onsite renewable energy. In particular, a lot sizing and production-scheduling problem in flexible flow line with renewable energy integration is formulated as a versatile optimization model. With regard to associated complexity issues, a multi-agent reinforcement learning approach is advocated to solve the lot sizing and scheduling problem. Finally, the approach is evaluated with a benchmark case and other numerical experiments.

Despite the prolongation of coagulation tests, recent studies reported an increased frequency of thromboembolic events in patients with cirrhosis. The aim of this study was to evaluate the haemostatic balance in cirrhotic patients through assessing the variation of pro- and anticoagulant factors and evaluating the in-vitro thrombin generation in patients with cirrhosis and in healthy patients. Fifty-one cirrhotic patients with or without thromboembolic events and 50 controls matched by age and sex were enrolled. Procoagulant (factors VII, II, V, VIII, and XII) and inhibitor (protein C, protein S and antithrombin) factor activities were determined. Thrombin generation was measured as endogenous thrombin potential (ETP). Haemostatic balance was assessed by means of both procoagulant to inhibitor coagulation factor ratios and ETP with to without protein C activation ratios. There were 24 males and 27 females. The mean age was 57.8 years [16–91 years]. Pro and anticoagulant factors were significantly lower in patients than in controls (P < 0.001) except for factor VIII and protein S. In fact factor VIII level was significantly higher in patients than in controls and protein S levels were not significantly different between patients and controls. Almost all the pro to anticoagulant factor ratios were higher in cirrhotics than in controls, especially the factor VIII to protein C ratios which increased significantly from Child Pugh A to C (P < 0.001), the ratio of ETP with to without protein C activator was higher in patients than in controls, but did not reach a significant level (0.8 vs. 0.52) There was no statistically significant difference between Child classes. When comparing patients with history of thrombosis (n = 7) to those matched by age and sex and without history of thrombosis (n = 14), the ratios were not statistically different between the two groups. Haemostatic changes in cirrhosis tend to rebalance the haemostatic system. This state often results in a hypercoagulable state attested by increased pro- to anticoagulant factor ratios and a normal thrombin generation.

This paper presents an indirect adaptive neural network sliding mode Control (IANSMC) technique and a neural network sliding mode control (NNSMC) for underactuated robot manipulators. The adaptive neural network (NN) based on radial basis functions (RBF) is used to estimate the equivalent control and to compensate model uncertainties. In IANSMC, the adaptive learning algorithms are derived using Lyapunov stability analysis. Sliding mode control and indirect adaptive technique are combined to deal with modeling parameter uncertainties and bounded disturbances. The stability of the mixed controller is then proved. NN parameters are tuned on-line, without an off-line learning phase. For the NNSMC, the NN control is used to learn the equivalent control due to the unknown nonlinear system dynamics and the robust sliding mode control (SMC) is designed for a trajectory tracking control. Simulation results show that the NNSMC and IANSMC are better than the classical SMC to control underactuated manipulators. Although the proposed controllers can eliminate the chattering phenomena and estimate matching uncertainties. The IANSMC can also reject mismatched perturbations. Discussions and comparisons between proposed controllers are presented.

A supply network design problem is addressed under a price elastic demand from the market. It is formulated as a biform game, combining a strategic subgame with a cooperative subgame. The cooperative subgame has a quadratic objective function. The results of this game are the winning coalition of manufacturing firms and the payoff profile associated with it. The proposed payoff allocation policy is shown to be fair, efficient and individually rational. Coalitional stability is conditioned on the game convexity, which is not always verified. Global profit optimality is obtained if the manufacturing network is directly facing the market. In the presence of an autonomous retailer between the manufacturing network and the market, the supply chain output is not globally optimal any more.