Efficient Algorithm Based on Adaptive Window–Model Predictive Control for Automatic Stacking in Warehouse Center

Abstract

In this article, three-dimensional (3D) stacking problem, as a mandatory task in the warehouse distribution centers, is smartly represented by a new mathematical formulation. A model predictive control (MPC) scheme is proposed to optimally solve the problem. The MPC cost function consists of several sequential criteria to suitably put the incoming packages on the dedicated cart and fully use up the cart capacity as much as possible. The practical and hard constraints are taken into consideration without confronting with a complicated optimization problem. A theorem is presented to guarantee the constraints satisfaction. Regarding the burden of computations, the proposed strategy is efficiently applicable to large carts by introducing the adaptive window (AW) idea. The efficacy of the proposed AW-MPC framework is shown by a numerical case study. INDEX TERMSAdaptive window, matrix representation, model predictive control, stacking problem.

Concept Drift Analysis and Malware Attack Detection System Using Secure Adaptive Windowing

“…Among the machine learning techniques suggested in [11] for identifying fake websites are (Lagrangian Support Vector Machine) LSVM, (Logistic regression) LR, Random Forest (RF), Naive Bayes (NB), and statistical techniques for finding Concept Drifts in websites. Only a few studies have produced practical results, according to the author in [12], intending to foster research on intelligent security techniques based on a cyclic process that begins with the discovery of new threats and ends with the analysis and development of prevention measures.…”