Approaches of production planning and control under Industry 4.0: A literature review

Purpose: Industry 4.0 technologies significantly impact how production is planned, scheduled, and controlled. Literature provides different classifications of the tasks and functions of production planning and control (PPC) like the German Aachen PPC model. This research aims to identify and classify current Industry 4.0 approaches for planning and controlling production processes and to reveal researched and unexplored areas of the model. It extends a reduced version that has been published previously in Procedia Computer Science (Herrmann, Tackenberg, Padoano & Gamber, 2021) by presenting and discussing its results in more detail. Design/methodology/approach: In an exploratory literature review, we review and classify 48 publications on a full-text basis with the Aachen PPC model’s tasks and functions. Two cluster analyses reveal researched and unexplored tasks and functions of the Aachen PPC model. Findings: We propose a cyber-physical PPC architecture, which incorporates current Industry 4.0 technologies, current optimization methods, optimization objectives, and disturbances relevant for realizing a PPC system in a smart factory. Current approaches mainly focus on production control using real-time information from the shop floor, part of in-house PPC. We discuss the different layers of the cyber-physical PPC architecture and propose future research directions for the unexplored tasks and functions of the Aachen PPC model. Research limitations/implications: Limitations are the strong dependence of results on search terms used and the subjective eligibility assessment and assignment of publications to the Aachen PPC model. The selection of search terms and the texts’ interpretation is based on an individual’s assessment. The revelation of unexplored tasks and functions of the Aachen PPC model might have a different outcome if the search term combination is parameterized differently. Originality/value: Using the Aachen PPC model, which holistically models PPC, the findings give comprehensive insights into the current advances of tools, methods, and challenges relevant to planning and controlling production processes under Industry 4.0Peer Reviewe

A Novel Black Box Process Quality Optimization Approach based on Hit Rate

Hit rate is a key performance metric in predicting process product quality in integrated industrial processes. It represents the percentage of products accepted by downstream processes within a controlled range of quality. However, optimizing hit rate is a non-convex and challenging problem. To address this issue, we propose a data-driven quasi-convex approach that combines factorial hidden Markov models, multitask elastic net, and quasi-convex optimization. Our approach converts the original non-convex problem into a set of convex feasible problems, achieving an optimal hit rate. We verify the convex optimization property and quasi-convex frontier through Monte Carlo simulations and real-world experiments in steel production. Results demonstrate that our approach outperforms classical models, improving hit rates by at least 41.11% and 31.01% on two real datasets. Furthermore, the quasi-convex frontier provides a reference explanation and visualization for the deterioration of solutions obtained by conventional models

Independent Scheduling System in Online Classrooms with Simple Multi-agent Temporal Networks

Abstrak. Karena semakin populernya kelas daring, diperlukan solusi penjadwalan fleksibel yang mengakomodasi berbagai jadwal dan preferensi pengguna. Ketika fleksibilitas jadwal menjadi salah satu alasan seseorang memilih kelas daring, maka menjadi jelas bahwa algoritma penjadwalan yang baik merupakan kebutuhan dasar agar pengguna mendapatkan fleksibilitas yang mereka cari. Tujuan dari penelitian ini adalah untuk membuat dan mengembangkan algoritma penjadwalan berdasarkan jaringan temporal multi-agen untuk mengatasi batasan penjadwalan guru dan siswa baik dalam situasi kelas maupun jarak jauh. Penelitian ini menggunakan metode jaringan temporal multi-agen untuk membuat algoritma yang menawarkan solusi penjadwalan independen untuk guru dan siswa. Algoritme ini mempertimbangkan berbagai batasan, sehingga menghasilkan penjadwalan yang berhasil di dalam dan di luar situasi kelas pada umumnya. Ide yang dikemukakan menunjukkan hasil penjadwalan yang efektif, memberikan kebebasan bagi siswa dan guru. Strategi multi-agen secara efektif mengontrol berbagai batasan, memberikan solusi penjadwalan yang dapat disesuaikan untuk berbagai kebutuhan pengguna. Guru dan siswa dapat secara mandiri membentuk solusi penjadwalan dengan algoritma yang akan menyelesaikan semua kendala internal dan eksternal guru dan siswa.   Abstract. Because of the growing popularity of online classes, flexible scheduling solutions that accommodate a wide range of user schedules and preferences are required. When schedule flexibility is one reason a person chooses online classes, it becomes clear that good scheduling algorithms are a basic requirement so that users get the flexibility they seek. The goal of this research is to create and develop a scheduling algorithm based on a multi-agent temporal network to solve the scheduling restrictions of teachers and students in both classroom and distant situations. This study uses a multi-agent temporal network method to create algorithms that offer independent scheduling solutions for teachers and students. These algorithms consider a variety of restrictions, providing successful scheduling within and outside of typical classroom situations. The idea that was put forward demonstrates effective scheduling outcomes, allowing students as well as teachers freedom. The multi-agent strategy effectively controls numerous restrictions, providing customizable scheduling solutions for a wide range of user requirements. Teachers and students can independently form scheduling solutions with algorithms that will solve all internal and external constraints of teachers and students

Deep Reinforcement Learning using Capsules in Advanced Game Environments

Reinforcement Learning (RL) is a research area that has blossomed tremendously in recent years and has shown remarkable potential for artificial intelligence based opponents in computer games. This success is primarily due to vast capabilities of Convolutional Neural Networks (ConvNet), enabling algorithms to extract useful information from noisy environments. Capsule Network (CapsNet) is a recent introduction to the Deep Learning algorithm group and has only barely begun to be explored. The network is an architecture for image classification, with superior performance for classification of the MNIST dataset. CapsNets have not been explored beyond image classification. This thesis introduces the use of CapsNet for Q-Learning based game algorithms. To successfully apply CapsNet in advanced game play, three main contributions follow. First, the introduction of four new game environments as frameworks for RL research with increasing complexity, namely Flash RL, Deep Line Wars, Deep RTS, and Deep Maze. These environments fill the gap between relatively simple and more complex game environments available for RL research and are in the thesis used to test and explore the CapsNet behavior. Second, the thesis introduces a generative modeling approach to produce artificial training data for use in Deep Learning models including CapsNets. We empirically show that conditional generative modeling can successfully generate game data of sufficient quality to train a Deep Q-Network well. Third, we show that CapsNet is a reliable architecture for Deep Q-Learning based algorithms for game AI. A capsule is a group of neurons that determine the presence of objects in the data and is in the literature shown to increase the robustness of training and predictions while lowering the amount training data needed. It should, therefore, be ideally suited for game plays.Comment: Master Thesis in Computer Scienc

Tensor Regression

Regression analysis is a key area of interest in the field of data analysis and machine learning which is devoted to exploring the dependencies between variables, often using vectors. The emergence of high dimensional data in technologies such as neuroimaging, computer vision, climatology and social networks, has brought challenges to traditional data representation methods. Tensors, as high dimensional extensions of vectors, are considered as natural representations of high dimensional data. In this book, the authors provide a systematic study and analysis of tensor-based regression models and their applications in recent years. It groups and illustrates the existing tensor-based regression methods and covers the basics, core ideas, and theoretical characteristics of most tensor-based regression methods. In addition, readers can learn how to use existing tensor-based regression methods to solve specific regression tasks with multiway data, what datasets can be selected, and what software packages are available to start related work as soon as possible. Tensor Regression is the first thorough overview of the fundamentals, motivations, popular algorithms, strategies for efficient implementation, related applications, available datasets, and software resources for tensor-based regression analysis. It is essential reading for all students, researchers and practitioners of working on high dimensional data.Comment: 187 pages, 32 figures, 10 table