The demand for automated diagnostic facilities has increased due to the rise in high-risk infectious diseases. However, small and medium-sized centers struggle to implement full automation because of limited resources. An integrated molecular diagnostics automation system addresses this issue by integrating small-scale automated facilities for each diagnostic process. Nonetheless, determining the optimal number of facilities and human resources remains challenging. This study proposes a methodology combining discrete event simulation and a genetic algorithm to optimize job-shop facility layout in the integrated molecular diagnostics automation system. A discrete event simulation model incorporates the number of facilities, processing times, and batch sizes for each step of the molecular diagnostics process. Genetic algorithm operations, such as tournament, crossover, and mutation, are applied to derive the optimal strategy for facility layout. The proposed methodology derives optimal facility layouts for various scenarios, minimizing costs while achieving the target production volume. This methodology can serve as a decision support tool when introducing job-shop production in the integrated molecular diagnostics automation system

MES(manufacturing execution system) plays a critical role in improving production efficiency by managing operations across the entire manufacturing system. Conventional manufacturing systems employ a centralized control structure, which has limitations in terms of the flexibility, scalability and reconfigurability of the manufacturing system. Agent-based manufacturing systems, on the other hand, are better suited to dynamic environments due to their inherent high autonomy and reconfigurability. In this study, we propose an agent-based MES and present its collaboration model between agents along with a data structure. The agent-based MES consists of three types of core agents: WIPAgent, PAgent(processing agent), and MHAgent(material handling agent). The entire manufacturing execution process operates through collaboration among these core agents, and all collaboration is carried out through autonomous interactions between the agents. In particular, the order-by-order dispatching process and the WIP(work-in-process) routing process are represented as respective collaboration models to facilitate understanding and analyzing the processes. In addition, we define data specifications required for MES implementation and operation, and their respective structures and relationships. Moreover, we build a prototype system employing a simulation model of an exemplary shop-floor as a simulation test bed. The framework proposed in this study can be used as a basis for building an automated operating system in a distributed environment.

Recently, in the manufacturing industry, changes in various environmental conditions and constraints appear rapidly. At this time, a dispatching system that allocates work to resources at an appropriate time plays an important role in improving the speed or quality of production. In general, a rule-based static dispatching method has been widely used. However, this static approach to a dynamic production environment with uncertainty leads to several challenges, including decreased productivity, delayed delivery, and lower operating rates, etc. Therefore, a dynamic dispatching method is needed to address these challenges. This study aims to develop a reinforcement learning-based dynamic dispatching system, in which dispatching agents learn optimal dispatching rules for given environmental states. The state space represents various information such as WIP(work-in-process) and inventory levels, order status, machine status, and process status. A dispatching agent selects an optimal dispatching rule that considers multiple objectives of minimizing total tardiness and minimizing the number of setups at the same time. In particular, this study targets a multi-area manufacturing system consisting of a flow-shop area and a cellular-shop area. Thus, in addition to the dispatching agent that manages inputs to the flow-shop, a dispatching agent that manages transfers from the flow-shop to the cellular-shop is also developed. These two agents interact closely with each other. In this study, an agent-based dispatching system is developed and the performance is verified by comparing the system proposed in this study with the existing static dispatching method.

In the manufacturing industry, dispatching systems play a crucial role in enhancing production efficiency and optimizing production volume. However, in dynamic production environments, conventional static dispatching methods struggle to adapt to various environmental conditions and constraints, leading to problems such as reduced production volume, delays, and resource wastage. Therefore, there is a need for dynamic dispatching methods that can quickly adapt to changes in the environment. In this study, we aim to develop an agent-based model that considers dynamic situations through interaction between agents. Additionally, we intend to utilize the Q-learning algorithm, which possesses the characteristics of temporal difference (TD) learning, to automatically update and adapt to dynamic situations. This means that Q-learning can effectively consider dynamic environments by sensitively responding to changes in the state space and selecting optimal dispatching rules accordingly. The state space includes information such as inventory and work-in-process levels, order fulfilment status, and machine status, which are used to select the optimal dispatching rules. Furthermore, we aim to minimize total tardiness and the number of setup changes using reinforcement learning. Finally, we will develop a dynamic dispatching system using Q-learning and compare its performance with conventional static dispatching methods.

Up to date cosmetic OEM/ODM (original equipment manufacturing/original development manufacturing) industry receives attention as a future growth engine due to steady growth. However, because of limited research and development capability, many companies have employed commercial management platforms specialized for large-sized companies; thus, overall system effectiveness and efficiency is low. Especially, MRP (material requirement planning) system introduced originally in 1970s is employed to calculate the requirement of the parts. However, dynamic nature of production lead time usually results in incorrect requirements. In addition, its algorithm does not consider the capability of the production resources. Also, because the commercial MRP system calculates all subcomponent for fixed period, the more goods have subcomponent, the slower calculation is. Therefore, conventional MRP system cannot respond complicated situation in time. In this study, we will suggest a new method that can respond to complicated situations resulting from short lead time and urgent production order in Korean cosmetic market. In particular, a distributed MRP system is proposed, that consists of multi-functional and operational modules, based on the characteristic of the BOM (bill of material). The distributed MRP system divides components (i.e. products and parts) into several fields and decrease the problem size; thus, we can respond to dynamically changed data any time. Through this solution, we can order components quickly, adjust schedules and planned quantity, and manage stocks reasonably. In addition, a prototype of the distributed MRP system is presented in this paper, in which ERP (enterprise resource planning) sever data is associated with an excel spreadsheet via MSsql. System user interface is implemented by a VBA (visual basic for applications) tool. According to a case study, response rate for delivery and planning achievement rate were enhanced about 20%, and inventory turnover was also decreased. Consequently, the proposed system improves overall profit.

The up-to-date business environment for Korean manufacturers is very complex and rapidly changing. Especially, the companies have faced with various changes derived from small quantity batch production, diversification of customer demands, and short life cycles of products. Consequently, the Korean manufacturing companies are in need of more efficient production planning and scheduling techniques. In this paper, the research trend of scheduling techniques is investigated to provide relevant information to researchers in this field. Furthermore, some implications for future researches are presented regarding literatures published in Korea over the last 10 years. This paper presents an entire investigation into Korean research works on scheduling (2,569 papers) that are published from 2007 to 2016. Especially, detailed analysis was carried out in the following three industry : 1) semiconductor, 2) shipbuilding and 3) automobile. In this paper, approaches to scheduling presented in the literature are categorized into the following three categories : 1) application, 2) algorithm, and 3) simulation modeling. First, in the semiconductor industry, scheduling techniques related to semiconductor cleaning processes, photolithography processes, chemical processes, transport and transport equipment have been found to be dominant. Second, the shipbuilding industry is focused on assembly processes, transporter, crane and various existing production management system. On the other hand, the scheduling research of the automobile industry is mainly focused on the vehicle movement routing and procurement supply-chain planning algorithm in terms of logistics. The conclusion of this study are expected to provide many implications for various types of academic and practical follow-up studies related to scheduling in consideration of main characteristics of semiconductor, shipbuilding and automobile industries.

Today, the management environment of the manufacturing industry is faced with drastic changes. The manufacturing industry is planning to innovate for itself via upgrading manufacturing technology and securing manufacturing competitiveness through 'Industry 4.0' and 'Manufacturing 3.0' strategies combined with ICT (information and communications technologies) for smart factory construction. In addition, as the era of the fourth industrial revolution began, the smart factory is emerging as a new paradigm that can lead to new changes in the manufacturing industry and achieve sustainable development. However, most of SMEs (small and medium-sized manufacturing enterprises) in Korea have very low technology, investment capital and expertise for smartization, and the level of informatization is so low that they cannot build basic systems such as a management module of production records. Therefore, this study proposes a framework that integrates cloud-based production data management and production scheduling with intuitive rules for smart production site management of SMEs. The main features of the proposed framework for SMEs are as follows: 1) the collection and management of production data using the cloud system; 2) operation management using intuitive heuristic algorithm; 3) production scheduling through timing constraints-based simulation.

Up-to-date manufacturing companies have faced a market-driven environment of pull production order. There should be a difference in operating manufacturing resources according to the type, quantity, and delivery time of manufactured products, because the process situation in pull production is changed by customer orders. And it should be taken into account from the stage of preparing for production such as process design and the placement and utilization of manufacturing resources. However, the feasibility of production plans is limited because most of small manufacturing businesses make production/supply plan of the parts and products assuming that equipment abilities in scheduling is sufficient without managing process standard information systemically. In this study, a discrete event simulation system based on BOM (bill of material), that is F-OPIS (online productivity innovation system), is introduced and a case study on application of the system leading to improving productivities is presented. F-OPIS deals with a decision-problem on production management and it is specialized for small-and- medium sized manufacturing companies. The target company of this case study is a typical small-and-medium sized manufacturing company in Korea, that produces various machined parts. The target company adopts make-to-stock production management to prevent tardy delivery because of fluctuations in demand. Therefore, it is required to apply an efficient inventory control solution for improving productivities. In this paper, based on the constraints of working capacity of manufacturing resources, the bottleneck process is analyzed as production conditions are changed. Consequently, an improvement plan is proposed, that eventually enhances overall utilization rates of resources in the bottleneck process and reduces overall production lead-time and inventory level.

The up-to-date small and medium-sized enterprises (SMEs) in Korea have tried to respond flexibly and rapidly to dynamic business environment and to establish efficient production management system based on information technologies. However, most of SMEs have faced with low applicability of the production management system resulting from high costs of introduction and maintenance. In this paper, a production planning and control system, that is S-PMS (production management system for SMEs), is proposed to solve the problem of low applicability and limited human resources. S-PMS enables production managers to efficiently collect and manage master data with the actual target production systems and explores the bottleneck process by means of simulation techniques to improve productivity. Furthermore, it implements rescheduling mechanism in terms of a variety of process routes. In essence, intuitive dispatching rules and integrated data management of S-PMS improve field applicability of production management system. Consequently, S-PMS is expected to be used as an efficient production management system of SMEs in Korea.