An optimization model for multi-deep storage

Multi-deep storage systems have seen many implementations in warehouses due to their high floor space utilization. Setting the optimal lane depth for the incoming products has a considerable influence on the space utilisation and the storage efficiency, as well as the layout of the storage zones and the selection of the storage modes, the handling equipment and all the induced costs. Conventional models in designing block stacked warehouse assume uniform and deterministic inflow and outflow of products in specific quantities and time intervals. These assumptions would lead to underestimate the space required for each specific case. In this study a recursive model is developed to address the decisions of the combination of single-deep and multi-deep lanes of different depth of a warehouse when flow of products is stochastic and dynamic in nature. Furthermore, the model gives additional value to the designer to maximize warehouse space efficiency, and thus, diminishing the costs. The main objective is to find out the combination of single-deep lanes and multi-deep lanes with different depths that make up the storage system.Outgoin

Magyar Mesterséges Intelligencia Bibliográfia : Válogatás az 1988-96 között (esetenként korábban) megjelent publikációkból

Tartalom: referált folyóiratokban, konferencia kiadványokban, tanulmánykötetekben megjelent dolgozatok, könyvek, tankönyvek, disszertációk referenciáit, közel 190 magyar szerző/társszerző 400 (tárgyszavazott) dolgozatát tartalmazza. Függelékében az Új ALAPLAP folyóirat Jakab Ágnes által szerkesztett TUDÁSTECHNOLÓGIA c. tematikus MI-sorozat dolgozatainak jegyzéke található. Az anyagok az NJSZT által Budapesten szervezett ECAI’96 konferenciát kísérő kiállításra készültek. A Bibliográfia és a hozzá kapcsolódó Reprint Gyűjtemény az NJSZT standján volt kiállítva, míg az OMIKK adatbázisában való keresést egy oda kihelyezett terminál biztosította. A tárgyszavazást és az adatfelvitelt Kladiva Ottmár (OMIKK) irányította

An optimization model for multi-deep storage

Multi-deep storage systems have seen many implementations in warehouses due to their high floor space utilization. Setting the optimal lane depth for the incoming products has a considerable influence on the space utilisation and the storage efficiency, as well as the layout of the storage zones and the selection of the storage modes, the handling equipment and all the induced costs. Conventional models in designing block stacked warehouse assume uniform and deterministic inflow and outflow of products in specific quantities and time intervals. These assumptions would lead to underestimate the space required for each specific case. In this study a recursive model is developed to address the decisions of the combination of single-deep and multi-deep lanes of different depth of a warehouse when flow of products is stochastic and dynamic in nature. Furthermore, the model gives additional value to the designer to maximize warehouse space efficiency, and thus, diminishing the costs. The main objective is to find out the combination of single-deep lanes and multi-deep lanes with different depths that make up the storage system.Outgoin

A Multi Agent System for Flow-Based Intrusion Detection

The detection and elimination of threats to cyber security is essential for system functionality, protection of valuable information, and preventing costly destruction of assets. This thesis presents a Mobile Multi-Agent Flow-Based IDS called MFIREv3 that provides network anomaly detection of intrusions and automated defense. This version of the MFIRE system includes the development and testing of a Multi-Objective Evolutionary Algorithm (MOEA) for feature selection that provides agents with the optimal set of features for classifying the state of the network. Feature selection provides separable data points for the selected attacks: Worm, Distributed Denial of Service, Man-in-the-Middle, Scan, and Trojan. This investigation develops three techniques of self-organization for multiple distributed agents in an intrusion detection system: Reputation, Stochastic, and Maximum Cover. These three movement models are tested for effectiveness in locating good agent vantage points within the network to classify the state of the network. MFIREv3 also introduces the design of defensive measures to limit the effects of network attacks. Defensive measures included in this research are rate-limiting and elimination of infected nodes. The results of this research provide an optimistic outlook for flow-based multi-agent systems for cyber security. The impact of this research illustrates how feature selection in cooperation with movement models for multi agent systems provides excellent attack detection and classification

The terminator : an AI-based framework to handle dependability threats in large-scale distributed systems

With the advent of resource-hungry applications such as scientific simulations and artificial intelligence (AI), the need for high-performance computing (HPC) infrastructure is becoming more pressing. HPC systems are typically characterised by the scale of the resources they possess, containing a large number of sophisticated HW components that are tightly integrated. This scale and design complexity inherently contribute to sources of uncertainties, i.e., there are dependability threats that perturb the system during application execution. During system execution, these HPC systems generate a massive amount of log messages that capture the health status of the various components. Several previous works have leveraged those systems’ logs for dependability purposes, such as failure prediction, with varying results. In this work, three novel AI-based techniques are proposed to address two major dependability problems, those of (i) error detection and (ii) failure prediction. The proposed error detection technique leverages the sentiments embedded in log messages in a novel way, making the approach HPC system-independent, i.e., the technique can be used to detect errors in any HPC system. On the other hand, two novel self-supervised transformer neural networks are developed for failure prediction, thereby obviating the need for labels, which are notoriously difficult to obtain in HPC systems. The first transformer technique, called Clairvoyant, accurately predicts the location of the failure, while the second technique, called Time Machine, extends Clairvoyant by also accurately predicting the lead time to failure (LTTF). Time Machine addresses the typical regression problem of LTTF as a novel multi-class classification problem, using a novel oversampling method for online time-based task training. Results from six real-world HPC clusters’ datasets show that our approaches significantly outperform the state-of-the-art methods on various metrics