Adaptive robust optimization with minimax regret criterion: Multiobjective optimization framework and computational algorithm for planning and scheduling under uncertainty

Control strategy for biopharmaceutical production by model predictive control

The biopharmaceutical industry is rapidly advancing, driven by the need for cutting-edge technologies to meet the growing demand for life-saving treatments. In this context, Model Predictive Control (MPC) has emerged as a promising solution to address the complexity of modern biopharmaceutical production processes. Its ability to optimize operations and ensure consistent product yields has made it an attractive option for manufacturers in this sector. Furthermore, MPC's alignment with the Process Analytical Technology (PAT) initiative provides an additional layer of assurance, facilitating real-time monitoring and enabling swift adjustments to maintain process integrity. This comprehensive review delves into the various applications of MPC, ranging from robust control to stochastic model predictive control, thereby equipping biotechnologists and process engineers with a powerful toolset. By harnessing the capabilities of MPC, as elucidated in this review, manufacturers can confidently navigate the intricate bioprocessing landscape and unlock this approach's full potential in their production processes.

Decision-making approaches in the antagonistic digital communication of the online communities users

The information space is an effective hybrid warfare tool on the web. This study investigated the decision-making process in the antagonistic digital communication of internet services users. Implementation of the proposed methods in this study minimized the negative impact of the spread of destructive content and actions of unfair competitors in the ecosystem of social internet services. The innovativeness of the suggested methods is the ability to make real-time decisions in a situation of antagonistic behavior of online users. The authors presented a model to secure user data, contributing to sustainable communicative interaction for its managers and users. This article presented approaches to decision-making by online community administrators under conditions of antagonistic behavior of the online services, which ensures the increase of efficiency of online communication. Timely and effective decision-making eliminated the negative impact of conflicts between communities and the spread of threats to users' information security. The algorithm of decision-making by users of stable internet services is suggested. This article contains examples of using the examined Wald and Savage criteria for making optimal decisions, confirmed by many experiments. The developed methods are tested on the online community on the social network Facebook.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13278-022-01021-4.

A responsiveness metric for the design and planning of resilient supply chains

Supply Chain Management is in constant evolution, and Supply Chain Resilience (SCR) appears as a recent offspring result of changes in how companies do business. Research efforts on the topic have led to a focus on the basic concepts of SCR, leaving a relevant research gap on the modelling and quantification of the SCR behaviour. In fact, there is not yet a consensus on SCR metrics or on how to quantify SCR. Most SCR models fail to incorporate relevant characteristics of the supply chain's performance, as are the impacts perceived by downstream customers. This work addresses such gaps, and a new resilient SC metric is proposed, which is incorporated into a developed optimisation model, where economic and responsiveness objectives are maximised when designing and planning resilient SC considering all SC entities. The model is applied to a case study that shows that decision-makers should avoid adopting universal strategies when managing their SC and instead should define the best plan for their SC operation. The impacts perceived by downstream customers are analysed. Moreover, it can be concluded that there is a correlation between the SC performance and the new SCR metric, easing the process of designing and planning the SC when resilience concerns are at stake.

Robust Optimization of High-Speed Railway Train Plan Based on Multiple Demand Scenarios

The optimization of train plans is highly dependent on the space–time distribution of passenger demand in high-speed railway systems. A train plan usually needs to be implemented on multiple operation days, and obviously the amount and space–time distribution of demand over these days has noteworthy differences. To ensure the same train plan is able to be implemented on multiple operation days while effectively satisfying the different levels of demand on those days, a novel robust optimization of a high-speed railway train plan based on multiple demand scenarios is performed in this research. Firstly, the passenger demand of each operation day is described as a demand scenario, and a candidate train set is generated that is able to satisfy the multiple demand scenarios. Then, a regret value corresponding to the total cost, including the train operation cost and passenger travel expense, is proposed to measure the deviation in the costs generated between the robust and the optimal train plan under each demand scenario. Then a robust optimization model for a high-speed railway train plan is constructed to minimize the maximum regret value. Moreover, a simulated annealing algorithm for solving the model is designed by constructing some neighborhood solution search strategies for multiple demand scenarios. Finally, the validity and feasibility of the proposed robust optimization method for train planning are verified on the Shijiazhuang–Jinandong high-speed railway line in China.