Simulation constitutes a crucial part of designing and operating logistics warehouses. The use of agent-based models (ABM) allows to incorporate human workers specifics in the modeling framework. Warehouse operate in a complex supply chain system and their operation should not be a bottleneck for other processes. Thus, warehouse operation must be predictable, repeatable and finally optimal. Order picking is the main process in any warehouse, being the most time consuming and therefore the most costly. This study addresses the issue of order picking process in the real scale warehouse. Optimization results are compared with the existing picking practice generated by implemented Warehouse Management System (WMS), and with common routing strategies, like Sshape or the largest gap. It is shown that evolutionary algorithm allows to improve the picking routing problem giving clear benefits.

The noise in control systems was studied based on data from several hundreds of control loops operating in different process industries located in several sites all over the world. That data showed that the theoretical assumption of Gaussian properties for the data is hardly ever satisfied. This paper will focus on some illustrative examples of stochastic models with non-Gaussian noise and will present the evolution process in using stochastic processes that include fractional Brownian motion processes, Rosenblatt and Rosenblatt–Volterra processes as a replacement of commonly used ordinary Brownian motions. Theoretical advancements will demonstrate challenges and fascinating opportunities in them for developing the models that meet the expectations of industrial practitioners.

The notions of agent-based modeling and optimization in the area of distributional supply chain management are well established. The literature delivers quite many potential solutions. Agent-based modeling and simulations are quite common, however they mostly incorporate deterministic approaches. Fully stochastic solutions are less frequent, while additional incorporation of optimization introduces another degree of complexity. This research covers two aspects of the distributional transport between distribution center (DC) and final store destinations: agent-based transport modeling and trucks routing. Both, the model and the optimization incorporates knowledge about road network, driving times, traffic jams and accidents. Custom congestion model is designed and used. Optimization solution compares various vehicle routing problem (VRP) and global optimization approaches to select the most appropriate solution.

Simulation constitutes a crucial part of designing and operating logistics warehouses. An agent-based modeling (ABM) allows to capture various elements of such a system, which may include human workers, material-handling equipment and different kinds of autonomous subsystems. Warehouse operate in a complex supply chain system and must meet its requirements. Customers expect higher responsiveness, which translates into completion times shortening. Robotic systems are deterministic, while human-based picker-to-parts warehouses are not. This paper presents novel agent-based stochastic distribution center (DC) model, which uses distributional gradient boosting machine learning (ML) to introduce pickers’ uncertainties associated with human behavior. Obtained modeling approach is validated using full-scale DC environment and real-time warehouse data.

Non-Gaussian statistical signal processing is be-coming increasingly significant in the current complex world. However, the variance of a non-Gaussian distribution may not exist, therefore many conventional methods will be considerably weakened, meaningless or even misleading. For example, the least-squares criterion may not be robust in the non-Gaussian environment, owing to the impulsive behaviors and outliers. In the paper, the statistical measures with fractional lower order moments (FLOM) are used instead of second-order moments to assess the control performance for non-Gaussian processes. The results show that FLOM is robust against outliers for the non-Gaussian signals. In the end, Hurst exponent fitting with FLOM and multifractal detrended fluctuation analysis (MFDFA) with FLOM are applied to the real data.

Outliers always exist in real industrial data. They originate from different, often unknown sources. They are considered with respect in statistical analysis, robust regression and in data mining. One may find a lot of interesting approaches and consideration. Their detection, labelling, identification, isolation, filtering and interpretation is subject of many research activities. On the other hand effect of the outliers on control systems analysis has not been sufficiently investigated. Their effect is often neglected or considered contemptuously. This work addresses the subject of outlier detection from the perspective of control system performance analysis. The work focuses on statistical data-driven approaches. Selected statistical outlier detection approaches are proposed and compared on real industrial control loop data originating from process industry. Obtained results form a starting point for potential application of automatic outlier detection methods.

This paper presents analysis of finding and removing outliers on raw dataset obtained from random control loop of large-scale industrial ammonia distillation system. Research concerns an impact of analysis results on determining relationships between variables in the control loop using Transfer Entropy (TE) approach. Authors conducted research using different methods of analysis on the same dataset. Summary is comparison of results for using TE with raw and processed data and further discussion.