Agrophotovoltaic (APV) system is an integrated system producing crops as well as solar energy. Because crop production underneath Photovoltaic (PV) modules requires delicate management of crops, smart farming equipment such as real-time remote monitoring sensors (e.g., soil moisture sensors) and micro-climate monitoring sensors (e.g., thermometers and irradiance sensors) is installed in the APV system. This study aims at introducing a decision support system (DSS) for smart farming in an APV system. The proposed DSS is devised to provide a mobile application service, satellite image processing, real-time data monitoring, and performance estimation. Particularly, the real-time monitoring data is used as an input of the DSS system for performance estimation of an APV system in terms of production yields of crops and monetary benefit so that a data-driven function is implemented in the proposed system. The proposed DSS is validated with field data collected from an actual APV system at the Jeollanamdo Agricultural Research and Extension Services in South Korea. As a result, farmers and engineers enable to efficiently produce solar energy without causing harmful impact on regular crop production underneath PV modules. In addition, the proposed system will contribute to enhancement of the smart farming technology in the field of agriculture.

Agrophotovoltaic (APV) system is an integrated system producing crops as well as solar energy. Because crop production underneath Photovoltaic (PV) modules requires delicate management of crops, smart farming equipment such as real-time remote monitoring sensors (e.g., thermometers, irradiance sensors, and soil moisture sensors) is installed in the APV system. This study aims at introducing a simulation-based decision support system (DSS) for smart farming in an APV system. The proposed DSS is devised to provide a mobile application service, satellite image processing, real-time data monitoring, and simulation-based performance estimation. Particularly, an agent-based simulation (ABS) is used to mimic functions of an APV system so that a data-driven function and digital twin environment are implemented in the proposed system. The ABS model is validated with field data collected from an actual APV system at the Jeollanamdo Agricultural Research and Extension Services in South Korea. As a result, farmers and engineers enable to efficiently produce solar energy without causing harmful impact on regular crop production underneath PV modules. In addition, the proposed system will contribute to enhancement of the digital twin technology in the field of agriculture.

Due to changes in the agricultural market environment and both overseas and domestic farming conditions, uncertainties in agricultural production and management are becoming greater. Hence, there is a stronger need for farmers to choose crops in the optimal condition. This research aims to introduce the result and process of developing a decision support system for selecting crops, aimed to assist farmers in selecting the optimal crops most suitable in the given situation. There are basically three main factors to consider in the decision-making process for farmers when selecting a crop to introduce to their lands. First of all, one must consider how much profit crop A will produce when it is cultivated. Secondly, one must consider which crop to cultivate in order to earn a certain amount of profit. Thirdly, one must consider what is the best way to maximize Farm A’s business profit. For instance, a farm may have land as its resource, and one must research which location, type of crop, level of technology, and so forth, to maximize profit.This research creates a database of the profitability of a total of 180 crop types by analyzing Rural Development Administration’s survey of agricultural products income of 115 crop types, small land profitability index survey of 53 crop types, and Statistics Korea’s survey of production costs of 12 crop types. Furthermore, this research presents the result and developmental process of a web-based crop introduction decision support system that provides overseas cases of new crop introduction support programs, as well as databases of outstanding business success cases of each crop type researched by agricultural institutions.

The quick response(QR) system is very popular in Korean apparel companies. However, the usage of QR system was not known well. The purpose of this study is to identify the usage of the quick response decision support system(QR DSS) and postponement manufacturing in the Korean apparel company. The researched company was the only one which used the QR DSS. The researchers carried out the depth interview with the QR decision makers of the company. This company had 14 brands, and had used the QR DSS since January, 2008. The results are as follows: The QR DSS was supportive computer software program, and it helped the staffs to make agile decision about QR repeat production of clothing. The QR DSS automatically calculated the related data, and suggested the expected sales volume and the proper supply amounts of the styles. There were four functions in QR DSS : 'QR Alert', 'Proper Supply Amount Simulation', 'Sensible QR', and 'Supply/Sales Simulation by Item'. The men's clothing brands effectively used 'Supply/Sales Simulation by Item' function. And the women's clothing brands effectively used 'QR Alert' function. This company also used the postponement production system for QR repeat production. The postponement production was conducted with four methods : the yarn stocking, the grey fabric stocking, the dyed fabric stocking, and the fabric sourcing. The men's clothing brands usually used of the yarn stocking methods and the dyed fabric stocking methods. The women's clothing brands usually used the grey fabric stocking methods. By using QR DSS and postponement production system the company was able to shorten the lead time for QR decision making.

The bullwhip effect is known as the significant factor which causes unnecessary inventory, lost sales or cost increase in supply chains. Therefore, the causes of the bullwhip effect must be examined and removed. In this paper, we develop two analytical to

  In order to implement Artificial Intelligence, various technologies have been widely used. Artificial Intelligence are applied for many industrial products and machine tools are the center of manufacturing devices in intelligent manufacturing devices. T

In order to implement Artificial Intelligence, various technologies have been widely used. Artificial Intelligence are applied for many industrial products and machine tools are the center of manufacturing devices in intelligent manufacturing devices. The purpose of this paper is to present the design of Decision Support Agent that is applicable to machine tools. This system is that decision whether to act in accordance with machine status is support system. It communicates with other active agents such as sensory and dialogue agent. The proposed design of decision support agent facilitates the effective operation and control of machine tools and provides a systematic way to integrate the expert's knowledge that will implement Intelligent Machine Tools.

As quality becomes a primary leading factor of organizational success, various management strategies have been In-troduced to Improve quality competitiveness Quality competitiveness, however, is difficult to measure and numerous organ-izations are struggl

Utilization rate for the gate and required time and walking distance to boarding flight are important measures for the gate management and passenger's convenience estimation. So, the main purpose of the gate management are the maximization of utilization rate and improvement of airport terminal user's convenience through the efficient gate management. This study intends to maximize the utilization rate of usable gates by considering layout, terminal configuration, local passenger of the airport and development of gate assignment algorithm and DSS which maximizes the gate utilization and minimizes the passengers' walking distance. And the decision support system can provide an efficient means of airport management of airport using an assignment algorithm.

This paper deals with a network model for the efficient transportation of post and consists of the formulation based on the network model and the LINGO programming model including the operations of the post transportation. This network model is represented by using Time Space Network. The generalized formulation is built up with the input variables and the decision variables, which are defined on the basis of the network model. And LINGO programming model to be proposed with DB and LINGO is constructed in consideration of how to manage the post transportation and the intermodal transport. The results of the model implementation were represented on Time Space Network and they are analyzed and verified. The LINGO programming model is used as the module to be set in application software. Specifically with using GEOmania, GIS tool, the LINGO Model is applied to develop the application for Mail Transportation Decision Support System.