When a fire breaks out in a distribution center, it causes a lot of damage. And the most casualties are caused by Fire accidents. Therefore, training for fire prevention should be mandatory at the distribution center. Also, the contents of education should be different in room temperature warehouses and low temperature warehouses. Fire education in low-temperature warehouses should be more emphasized. This is because many fires occur in low-temperature warehouses. In this study, a study was conducted to determine the important order of training hours and contents for fire prevention education according to the type of distribution center. The importance of time and content for safety education in all types of warehouses did not differ significantly. It was first decided that safety prevention training should be conducted periodically in all types of warehouses

Industrial complexes are areas where manufacturing companies are integrated, and logistics between tenant companies play a very important role, but idle resources can occur depending on the situation if each company operates independently. Accordingly, this study aimed to reduce overall logistics costs and increase corporate productivity by looking at ways to share and utilize logistics resources such as warehouses and transportation equipment to efficiently utilize logistics resources in industrial complexes and implementing a logistics sharing platform that can share these idle resources. To this end, this study conducted a research survey on the logistics status of manufacturing companies in Ulsan-Mipo Industrial Complex, based on this analysis, the necessity of logistics resource types and utilization of industrial complex resident companies, and based on this, a service model for logistics resource sharing was studied. In addition, it was intended to analyze the operational characteristics of the existing logistics system to derive improvements and to derive optimal measures to utilize information on shared idle resources. This study confirmed the importance of sharing and utilizing idle resources to optimize logistics resources in industrial complexes, and is expected to contribute to reducing logistics costs and increasing logistics efficiency of tenant companies.

In order to solve the rapidly increasing domestic delivery volume and various problems in the recent metropolitan area, domestic researchers are conducting research on the development of “Urban Logistics System Using Underground Space” using existing urban railway facilities in the city. Safety analysis and scenario analysis should be performed for the safe system design of the new concept logistics system, but the scenario analysis techniques performed in previous studies so far do not have standards and are defined differently depending on the domain, subject, or purpose. In addition, it is necessary to improve the difficulty of clearly defining the control structure and the omission of UCA in the existing STPA safety analysis. In this study, an improved scenario table is proposed for the AGV horizontal transport device, which is a key equipment of an urban logistics system using underground space, and a process model is proposed by linking systematic STPA safety analysis and scenario analysis, and UCA and Control Structure Guidelines are provided to create a safety analysis.

In the era of the 4th Industrial Revolution, Logistic 4.0 using data-based technologies such as IoT, Bigdata, and AI is a keystone to logistics intelligence. In particular, the AI technology such as prognostics and health management for the maintenance of logistics facilities is being in the spotlight. In order to ensure the reliability of the facilities, Time-Based Maintenance (TBM) can be performed in every certain period of time, but this causes excessive maintenance costs and has limitations in preventing sudden failures and accidents. On the other hand, the predictive maintenance using AI fault diagnosis model can do not only overcome the limitation of TBM by automatically detecting abnormalities in logistics facilities, but also offer more advantages by predicting future failures and allowing proactive measures to ensure stable and reliable system management. In order to train and predict with AI machine learning model, data needs to be collected, processed, and analyzed. In this study, we have develop a system that utilizes an AI detection model that can detect abnormalities of logistics rotational equipment and diagnose their fault types. In the discussion, we will explain the entire experimental processes : experimental design, data collection procedure, signal processing methods, feature analysis methods, and the model development.

In an automotive plant, an automated storage and retrieval system (ASRS) synchronizes material handling flows from a part production line to an auto-assembly line. The part production line transfers parts on small-/large-sized pallets. The products on pallets are temporarily stored on the ASRS, and the ASRS retrieves the products upon request from the auto-assembly line. Each ASRS aisle is equipped with narrow-/wide-width racks for two pallet sizes. An ASRS aisle with narrow-/wide-width racks improves both storage space utilization and crane utilization while requiring delicate ASRS aisle design, i.e., the locations of the narrow-/wide-width racks in an ASRS aisle, and proper operation policies affect the ASRS performance over demand fluctuations. We focus on operation policies involving a common storage zone using wide-width racks for two pallet sizes and a storage-retrieval job-change for a crane based on assembly-line batch size. We model a discrete-event simulation model and conduct extensive experiments to evaluate operation policies. The simulation results address the best ASRS aisle design and suggest the most effective operation policies for the aisle design.

Many manufacturers applying third party logistics (3PLs) have some challenges to increase their logistics efficiency. This study introduces an effort to estimate the weight of the delivery trucks provided by 3PL providers, which allows the manufacturer to package and load products in trailers in advance to reduce delivery time. The accuracy of the weigh estimation is more important due to the total weight regulation. This study uses not only the data from the company but also many general prediction variables such as weather, oil prices and population of destinations. In addition, operational statistics variables are developed to indicate the availabilities of the trucks in a specific weight category for each 3PL provider. The prediction model using XGBoost regressor and permutation feature importance method provides highly acceptable performance with MAPE of 2.785% and shows the effectiveness of the developed operational statistics variables.

An automated material handling system (AMHS) has been emerging as an important factor in the semiconductor wafer manufacturing industry. In general, an automated guided vehicle (AGV) in the Fab’s AMHS travels hundreds of miles on guided paths to transport a lot through hundreds of operations. The AMHS aims to transfer wafers while ensuring a short delivery time and high operational reliability. Many linear and analytic approaches have evaluated and improved the performance of the AMHS under a deterministic environment. However, the analytic approaches cannot consider a non-linear, non-convex, and black-box performance measurement of the AMHS owing to the AMHS’s complexity and uncertainty. Unexpected vehicle congestion increases the delivery time and deteriorates the Fab’s production efficiency. In this study, we propose a Q-Learning based dynamic routing algorithm considering vehicle congestion to reduce the delivery time. The proposed algorithm captures time-variant vehicle traffic and decreases vehicle congestion. Through simulation experiments, we confirm that the proposed algorithm finds an efficient path for the vehicles compared to benchmark algorithms with a reduced mean and decreased standard deviation of the delivery time in the Fab’s AMHS.

The growth of the online market is accelerating due to the development of technology and the pandemic era. The delivery service through the courier must be used to deliver the ordered goods to the customer through the online market. With the growth of the online market, the logistics market for delivery is also growing. The traffic and environmental problems are emerging as social issues. Urban logistics technology using underground space based on the urban railway developed to improve logistics efficiency in a metropolitan area and a new alternative to environmental problems. This study proposed a plan to secure system safety through safety analysis based on operational concept definition and scenario analysis by applying model-based perspective analysis to the system under development.

농촌 종점 물류는 농촌 주민들의 온라인 쇼핑에 영향을 주는 중요한 요인임. 농촌 인터넷 쇼핑의 서비스 품질은 양호한 종점 물류 배송과 지원이 필요함. 농촌 이커머스의 발전에 의 하여 농촌 주민들이 서비스의 품질에 대한 기대치도 높아지고 있음. 본 연구에서는 이커머스 만족도를 통한 농촌 종점 물류 서비스 품질이 농촌 주민의 온라인 구매행위에 어떠한 영향 미치는지 검토하고, 농촌 종점 물류 서비스의 발전을 위한 개 선 및 발전전략을 제시하였음. 본 연구에서는 구조방정식모델 (SEM)과 온충린의 매개효과 테스트를 적용하여 분석하였음. 1. 신뢰성과 서비스 편의성은 만족도에 긍정적 영향을 미치며, 원가절약은 만족도에 부정적 영향을 미침. 2. 만족도는 인터넷 쇼핑에 긍정적인 영향을 미치는 것으로 나타났음. 3. 만족도는 반응성과 구매행위 사이에 매개효과가 있고 경제와 구매행위 사이에도 매개효과가 있는 것으로 나타났음.