warehousing enviornment artificial intelligence automated logistics
214009653
ARTIFICIAL INTELLIGENCE APPLICATION IN SMART WAREHOUSING ENVIRONMENT FOR AUTOMATED LOGISTICS
The advancements in the technologies, the revolution in the business procedures and the entailment to modify the operation in the warehousing as the result of the accumulating orders along with the complications involved in it, and the shortage in the management skills has paved way for the emergence of the smart ware housing. More over as the warehousing takes a vital role in the supply chain and prevails as the key feature in the logistics, smart ware housing is very much necessitated to enhance the organization management and success. The application of the artificial intelligence in the warehousing operations enhances the potentials of the warehousing functioning in the logistics, management and the co ordination. The application of the artificial intelligence in the warehousing to make it a smart environment for the automated logistics is proposed in the paper. The paper concentrates on the automated storage and the retrieval using the internet of things, artificial intelligence and the cloud computing to have an any time access of the stock available in the warehouse.
Dr A Pasumpon Pandian
219019251
Cognitive and artificial intelligence system for logistics industry
Artificial intelligence is an important branch of cognitive science. Artificial intelligence has been applied to medical, education, logistics and other industries, which has broad prospects for development. Hotel logistics industry uses artificial intelligence technology to complete intelligent search, face recognition, combined with large data calculation and planning reasonable path in warehousing. Taking China's hotel logistics industry as the research object, this paper analyses the application of artificial intelligence technology in the logistics industry. In the warehousing process, artificial intelligence technologies including compile storage code, automatic picking with automated guided vehicle, warehouse robot to improve work efficiency. Logistics distribution links use artificial intelligence technology to plan the best path, improve the recognition rate of express waybill that save a lot of labour. Artificial intelligence technology allocates logistics resources, optimises logistics links, and improves logistics efficiency and other measures to promote the development of logistics informatisation and automation.
Feng Xie Jing Zhao
225067748
Research on the Artificial Intelligence Applied in Logistics Warehousing
Artificial intelligence is changing the world. The application of the artificial intelligence in logistics has brought a significant reduction in labor costs and huge increase in logistics efficiency. Artificial intelligence will lead the logistics industry truly enter the era of intelligent logistics. Warehousing is one of the most important function of logistics, so the intelligent level of warehousing has great effects on the construction and promotion of intelligent logistics. This article aims to study the applications of artificial intelligence in logistics warehousing, and analyze the limiting factors of artificial intelligence applied in the construction of intelligent warehousing, finally briefly conclude its general situation and prospects of future development.
AIAM
Xinke Du
169331806
Digital Warehousing as a Leading Logistics Potential
Digital transformation of business processes in various sectors and spheres of life radically changes the concept of "warehouse logistics." Leading modern logistics systems in warehousing are a combination of digital communication platforms equipped with automated and robotic technologies. The priority is to accelerate the turnover of stocks. Flexibility, mobility, speed and smart analytics determine the competitiveness of warehouse logistics. In the logistics management system, the focus shifts towards information flow management. Conditions are created for the implementation of methods and tools for processing structured and unstructured data from various information sources and the optimization of diverse information flows using artificial intelligence technology. In Russia, the level of digital activity in this sector of the economy is still low. Among the main reasons, the following ones come through as significant: the decline in the growth rate of the logistics services market, the high cost of digital products, information risks and the lack of specialists with digital competencies. We associate the implementation of reserves of digital potential of warehouse logistics with the development of the infrastructure of access to the Internet system; the growth of Internet users; the development of online commerce; connecting state authorities to solving digitalization problems. We propose to provide state support for digital type warehouse logistics systems based on the implementation of cluster type technology platforms. Their institutional support can be implemented on the basis of a public private partnership mechanism.
Vera Borisova Khasan Taymashanov Tamila S Tasueva
219060355
Artificial intelligence in manufacturing and logistics systems: algorithms, applications, and case studies
Modern manufacturing and logistics systems are supported by increasingly ubiquitous and powerful computing networks. Within these networks, oceans of data are continuously being generated by sensors, machines, systems, smart devices, and people. Together with rising computational capabilities, this Big Data is being analysed faster, more broadly, and more deeply than ever before. These advances have redefined the value of Artificial Intelligence (AI) technologies and opened a new age known as Industry 4.0 or the Smart Factory. Advanced cognitive computing and deep learning methods have begun to find application in manufacturing systems for automated visual inspections, fault detection, and maintenance. There are active efforts to apply reinforcement learning methods to material handling systems and production scheduling. Industries hoping to convert real time data into actionable decisions seek opportunities to integrate AI methods with traditional Operational Research approaches, the concepts and technologies of the Internet of Things (IoT) and cyber physical systems. The aim of this special issue is to provide insight into the latest advances emerging in the production research community that seek to exploit AI methods in this field. The special issue received 61 submissions from which nine papers were selected. The papers are grouped into three categories: AI methods for manufacturing systems, AI developments specifically in semiconductor manufacturing, and AI in additive manufacturing and maintenance. Within these categories the papers are ordered alphabetically by the last name of the first author. As part of Industry 4.0, the exploitation of data for intelligent decision making is essential for general manufacturing systems. The paper by Deng et al. considers milling systems and endeavours to predict machining parameters that will provide reliable chatter free milling. They use a neural network to model the limiting axial cutting depth as a component of the second order fourth moment method. The next paper by Fang et al. considers the prediction of the time remaining for jobs to be completed in a job shop. Using big data, they develop a deep learning method for the prediction that is superior to previous regression or network based predictions in their numerical experiments. The third paper in this category by Rao et al. focuses on a bi objective welding shop scheduling problem. The problem is modelled as a mixed integer programme and solved via a non dominated sorting genetic algorithm with a restarting strategy. Numerical experiments show that the proposed algorithm dominates four other algorithms. There is much excitement regarding the potential for AI in semiconductor manufacturing. In Chien et al. deep reinforcement learning is exploited to guide the selection of demand forecast models for semiconductor components and modules. An empirical study and a real world implementation demonstrated the validity of the approach. The identification and classification of defects are essential in semiconductor manufacturing and the paper by Kim et al. studies this important problem. They use a generalised uncertain decision tree model to classify defect patterns on multiple wafer maps based on uncertain features and show that their approach is significantly more efficient than existing methods to analyze real DRAM wafers. The problem of dynamic dispatching for unreliable machines in re entrant production systems, typically semiconductor manufacturing systems, is considered in the paper by Wu et al. They combine a deep neural network model and Markov decision processes (MDP) to rapidly generate near optimal dynamic control policies for problems that are too large to be only solved by MDP, thus showing the potential of machine learning in controlling unreliable manufacturing systems. Additive manufacturing and data driven maintenance are of increasing importance to support the emergence of truly Smart Factories. In the paper by Elhoone et al. a framework for a cyber additive manufacturing system is developed. Three artificial neural network algorithms are proposed and embedded in a two stage model to support the dynamic allocation of digital designs to different additive manufacturing techniques. The paper by Stanisavljevic et al. provides insights from an experimental study on methods to detect interferences, and thus improve product quality, in real time in additive manufacturing. Their approach is of practical importance and relies on sensor data and methods such as feature selection and machine learning to reach very high detection rates. With an eye toward optimal maintenance decisions in a risk based environment with fuzzy parameters, the paper by Wang et al. considers belief propagation in constrained fuzzy Bayesian networks. They
Int. J. Prod. Res.
Chen Fu Chien Stephane Dauzere-Peres Woonghee Tim Huh Young Jae Jang James R Morrison
214705802
OPPORTUNITIES TO INTEGRATE DIGITAL INTELLIGENCE INTO AN AUTOMATED LOGISTICS MANAGEMENT SYSTEM ALONG THE VALUE CHAIN
Factors that determine the effect of using automated management systems (AMS) in logistics are no longer just industrial, but also social and environmental, related to different structures in different parts of the world. In the current stage of industrial development, they are already forming the concept of modern production development using artificial intelligence and in logistics. Technological impacts dictate the tendency to continuously reduce manual, heavy and unattractive labor at the expense of higher productivity. The social aspect demands the discovery of new or preserving old jobs, and the environmental aspect imposes a lasting trend towards compliance with environmental standards and sustainable development parameters. The purpose of this report is to explore these differently impacting factors and on this basis to build an AMS of logistics across the value chain with the ability to incorporate digital intelligence.
Siyka Demirova
115283611
The use of artificial intelligence in automated in house logistics centres
The paper deals with the problem of works transport organization in logistic center with the use of artificial intelligence algorithms. The presented approach is based on non changeable path during travel along a given loop. The ordered set of containers requesting transport service was determined by fuzzy logic, while the sequence of containers in a loop was optimized by genetic algorithms. A solution for semi autonomous transport vehicles wherein the control system informs the driver about optimal route was presented. The obtained solution was verified by a computer simulation.
Tomasz Rymarczyk Grzegorz Klosowski
229492041
Export sales forecasting using artificial intelligence
Abstract Sales forecasting is important in production and supply chain management. It affects firms' planning, strategy, marketing, logistics, warehousing and resource management. While traditional time series forecasting methods prevail in research and practice, they have several limitations. Causal forecasting methods are capable of predicting future sales behavior based on relationships between variables and not just past behavior and trends. This research proposes a framework for modeling and forecasting export sales using Genetic Programming, which is an artificial intelligence technique derived from the model of biological evolution. Analyzing an empirical case of an export company, an export sales forecasting model is suggested. Moreover, a sales forecast for a period of six weeks is conducted, the output of which is compared with the real sales data. Finally, a variable sensitivity analysis is presented for the causal forecasting model.
Vahid Sohrabpour Pejvak Oghazi Reza Toorajipour Ali Nazarpour
203705722
Design of highly integrated automated warehouse control system based on artificial intelligence
The modern production system has higher and higher requirements for logistics modernization. As an important part of the modern logistics system, the automated threedimensional warehouse is more and more widely used. Based on the application and development requirements of modern logistics, this paper focuses on the management and control technology of automated warehouses, and uses the artificial intelligence theory to optimize the cargo space. The experiment proves that the management technology is convenient and efficient to use, and the performance is stable in actual use, which is worth promoting.
Wang Liuyang
168900671
Automation and artificial intelligence in business logistics systems: human reactions and collaboration requirements
ABSTRACT Increasing application areas and depths of autonomous systems in logistics provide a new level of challenge for the analysis and design of human machine interaction concepts. Due to scarce high skilled personnel in several regions and the objectives of efficiency and sustainability improvement, logistics operators have to pursue technological progress like automation with all means. In order to distinguish between more or less performing human artificial collaboration systems in logistics ex ante for investment decision purposes, a multi dimensional conceptual framework is developed. A comprehensive case study regarding automated truck driving in logistics is provided in order to test the concept concerning practical implications. Results include the notion of four distinctive and increasing resistance levels before finally an efficient 'trusted' collaboration between human operators and artificial intelligence systems can be achieved. This is important for the design of many automated systems in logistics, among others for driving and piloting professions regarding autonomous driving supervision.
Matthias Klumpp