Rack is a place to store products. Workers pick products from rack storage. After picking up, workers move to another location by forklift. Driving speed, worker condition, and number/frequency of operation is responsible for forklift accidents. When an accident occurs, products get damaged. Therefore, it is important to prevent one. As the research result, To prevent forklift accidents, the minimum order quantity, the minimum number of operations, and low speed operation are required.

When picking up cargo, workers manually load and unload the cargo. Workers have different intensity of work depending on the amount and weight of cargo. In particular, as the intensity of manual work increases, workers are exposed to cumulative traumatic diseases. A manual for picking safety work for workers cargo handling in the distribution center is required. In this study, the worker’s picking safety work based on the cargo volume and weight was presented as an experimental design model. Like the research results, the disease begins when the worker feels the number of pains presented by the model.

The present study was carried out to deduce optimum harvesting time based on fruit traits that were examined with fruits hanging the higher part (HP, higher than or equal to 2m height from the ground) and lower part (LP, lower than or equal to 1.5m height from the ground) on ‘Niitaka’ tree. The fruits of HP compared to of LP tended to be heavy and showed significantly higher soluble solid contents (SSC), hardness and gravity at each of the harvest time. On the basis f the discrepancies of the fruit traits between HP and LP, harvest time for the fruits of LP are acquired to be delayed for raising fruit weight and SSC. Due to the substantial probability of flesh pithiness based on the dramatic decrease of fruit gravity, it is necessary that the fruits of LP distributed just after harvesting or stored for short-term period.

During the fierce competition among the companies, improvement of logistics center managing efficiency is importantly recognized. Among them, studies on improvement of picking work process on about 55% of logistics center managing costs are continuously conducted. And, most of studies focus on analysis on strengths and weaknesses of picking method performable on the stage of logistics center design, etc, so the purposes of this research are to define load location rule by product releasing frequency in logistics managing environment based on conveyer and suggest expected effects by simulation analyzing method to improve picking managing efficiency.

This paper is to analyze the picking lead time for picking batch size in a warehouse system and to get minimum picking batch size that is the warehouse system feasible. The warehouse system consists of aisles and racks, which two racks face each other through aisle. The products are picked from the storage locations by batch size. The probability that items are picked in the each row of the rack in the aisle for order picking activity is derived. The picking lead time for picking batch size is the time passed from the first picking location to arrival at starting location in aisle picking all items included in a batch size. The picking lead time for picking batch size in an aisle is analyzed. The picking lead time for picking batch size in the whole warehouse system is obtained. The warehouse system is feasible if all items that customers order are picked from the storage locations for same period. The picking batch size that is the warehouse system feasible is obtained. The problem is analyzed, a solution procedure is developed, and a numerical example is shown to explain the problem.

본 연구는 하동지역의 녹차에 대해 근적외선 분광분석법(Near Infrared Spectroscopy, NIRS)을 이용하여 채취시기별 화학적 성분(총질소, 엽록소, 탄닌, 유리아미노산, 데아닌, 비타민 C, 카페인, 카테킨류, 총카테킨) 및 무기물 성분 변화에 대하여 분석하였다. 녹차의 화학적 성분 함량에 있어 엽록소, 탄닌, 비타민 C와 총카테킨 함량은 채취시기가 늦어질수록 증가한 반면, 총질소, 유리아미노산, 데아닌, 카페인 등은 대체로 감소하는 경향을 보였다. 무기물 함량은 채취시기가 늦어질수록 Mg, Ca, Mn은 증가하였으나 Na, K, B는 감소하였다. 또한 우전과 세작의 화학성분 중 총질소, 엽록소, 유리아미노산, 데아닌, 카페인, 총카테킨과 무기물 중 Na, Mg, Ca, B, Se 함량에 있어서는 차이를 나타내지 않았다.

This paper addresses the analysis of the travel distance and order picking time of the vehicle in a aisle when items are picked by the batch in a warehouse system. Batching is to combine several orders in a single tour of the storage/retrieval machine. An

The picking process in distribution center is one of the most difficult and time-consuming process. The improvement of picking productivity is a core element which decides efficiency of the distribution center. The time to shipping on vehicles from receiving depends on the arrangement of items or picking methods. The Pick to Light system typically is used to improve the efficiency of order picking. In some cases the layout design of Pick to Light system has been performed rather than scientific analysis by a common experience. Therefore, this study analyzed the impact of picking performance by the zone arrangement method in order picking process of Pick to Light system.

The SCM is that Supply Chain Network is Promptly and Voluntarily Optimized in Unstable Market Change Environment. The Cash flow Efficiency of Hole Supply Chain Network is Improved by Changing the Information and Changing the Foundation of Business Processes. The Role of WMS has been Changing Importantly with the Introduction of SCM. WMS Needed to Change to the Information Center in Order to Change Information in Real Time and the WMS of Information Storing in Order to Support an Idea Decision. This Development was Defined about the Importance of WMS in SCM Environment. The Criterion of Valuation is Normally Measured Time between Taking a Order Receive and Bringing the Items to Customer. The Decreasing Move Time of Order Picker in Warehouse is Directly Influence to the Job Execution. So, this Research is Defined about the Optimized Route of Order Picker and Suggests Algorithm. To do this, Past Algorithm is Studied. It's Easy to Introduce and this Study is Looking for Method about the Noticing of Order Picker. The Algorithm will Improve to be Adapt to Standard Process System.

This paper presents a convolutional neural network to automatically conduct the peak picking in frequency domain of structural responses. The peaks in frequency domain have a high potential to be the natural frequencies, which are one of the important indicator to be used for structural health monitoring purposes, such as damage detection, cable tension estimation, and finite element model updating. In general, the peaks with the corresponding natural frequencies are manually selected by the users from the frequency domain. Although this previous approach is possible to simply extract the candidate of natural frequencies, it is inappropriate in the practical applications of the long-term monitoring and the implementation for wireless smart sensor. To overcome the drawbacks, this study proposes the convolutional neural network that can automatically identify the peaks with the corresponding natural frequencies from the frequency domain of structural responses.

In this paper the order picking problem in warehouses is considered, a topic which has received considerable attention from the international academic body in recent years. The order picking problem deals with the retrieval of order items from prespecified locations in the warehouse, and its objective is usually the minimization of travel time or travel distance. Hence, a well-thought order picking policy in combination with an appropriate storage policy will enhance warehouse efficiency and reduce operational costs. This paper starts with a literature overview summarizing approaches to routing order pickers, assigning stock-keeping units to pick locations and designing warehouse layouts. Since the layout design might affect both storage and routing policies, the three factors are interdependent with respect to order picking performance. To test these interdependencies, a simulation experiment was set up, involving two types of warehouse layout, four types of storage policy, five well-known heuristics and five sizes of order picking list. Our results illustrate that from the point of view of order picking distance minimization it is recommended to equip the warehouse with a third cross aisle, although this comes at the cost of a certain space loss. Additionally, we propose a set of most appropriate matches between order picking heuristics and storage policies. Finally, we give some directions for further research and recommend an integrated approach involving all factors that affect warehouse efficiency.

가을울금과 봄울금의 채취시기에 따른 유기산, 무기질, 색도, curcumin 및 쓴맛성분 등의 함량은 다음과 같다. 주요 유기산 중에 oxalic acid와 lactic acid는 채취시기가 늦어질수록 점차 증가하였으나 malic acid, malonic acid는 감소하는 경향을 보였으며, 유기산 총함량은 채취시기가 늦어질수록 점차 증가하다가 다음해인 1월에는 다소 감소하는 경향을 보였다. 무기질은 채취시기가 늦을수록 함량이 증가하다가 이듬해에

가을울금과 봄울금의 채취시기에 따른 일반성분, 아미노산, 유리당 및 환원당 및 비타민 C와 등의 함량을 비교분석 하였다. 가을울금과 봄울금의 수분, 조회분, 조단백 및 조지방 함량은 채취시기가 늦을수록 증가하였고, 가용성무질소분 함량은 채취시기가 늦을수록 감소하는 경향을 보였다. 구성아미노산은 채취시기가 늦을수록 아미노산의 함량이 증가하다가 다음해인 1월말에는 약간 감소하였으며, 총 아미노산에 대한 필수아미노산의 비율도 증가하였다. 유리 아미노

채굴시기, 등급, 온도 및 저장기간에 따른 수삼의 호흡특성을 조사하였다. 채굴시기별 수삼의 호흡률은 3월부터 서서히 증가하여 8월에는에서 7.88 mL로 최고값을 보인 후 다시 감소하는 경향을 나타냈다. 수삼의 주채굴시기인 10월, 11월 및 익년 3월에 채굴한 수삼의 호흡계수는 범위로 채굴시기에 따라 뚜렷한 차이를 나타내지 않았다. 개체 당 중량이 범위인 수삼을 4 등급으로 나눠 호흡률을 비교하였던 바 중량이 클수록 호흡률이 낮아지는 경향

원료 양송이의 수확시기 및 보관 기간에 따른 신선편이 가공적성을 비교키 위하여 계절별로 수확한 양송이를 각각2 에 보관하면서 주기적으로 일정량씩을 취하여 각각 절단한 후 5 에 방치하면서 표면의 갈변진행정도와 총 페놀화합물 함량 변화를 조사하였다. 원료 양송이의 호흡률은 여름철 및 가을철에 수확한 것이 9.5510.47 mLCOkghr로 봄철 및 겨울철에 수확한 것에 비해 낮았고, 중량감소율은 봄에 수확한 양송이가 가장 낮았다. 원료상태로 보관한

매실 품종별, 수확시 기별, 부위별 그리고 매실농축액과 매실차 등 매실가공식품의 저장기간에 따른 시안화합물의 변화를 측정하여 얻은 결과는 다음과 같다. 매실 품종별 숙성과정 중 amygdalin 함량의 변화는 과육의 경우 수분후 미숙과인 70일경에는 3개품종이 비슷하였으나 앵숙이 20.2 mg%로 가장 높은 함량을 보였으며, 핵인의 경우는 재래종에서 562 mg%로 나타나 다른 품종에 비하여 1.5∼2.4배 더 높았으며, 부위별로 보면 핵인이 과

본 실험에서는 옥동들깨, 적자소, 청자소를 공시하여 적엽시기별로 수증기증류장치를 이용하여 정유성분을 추출하여 GC로서 분석한 결과는 다음과 같다. 1. 옥동들깨, 적자소, 청자소 정유는 주로 잎과 줄기에 함유하고 있으며, 뿌리에도 정유가 함유되어 있으나 옥동들깨와 청자소의 경우에는 함량이 적으며, 적자소의 경우 옥동들깨와 청자소에 비하여 2배 가량 많이 함유하고 있음을 확인할 수 있었다. 2. 총정유함량을 기준으로 청자소의 최적 채엽시기는 개화시인 8월 24일 적자소는 개화후인 9월 14일이었다. 3. 최적채엽시기를 perilladehyde를 기준으로 할 경우 청자소의 경우 개화시인 8월 24일이었으며, 적자소는 8월 4일로 나타났다.

This study aims to determine the optimal picking time and the favorate variety for Ume processing. The changes of physicochemical components and flavors of 6 varieties of the Ume were investigated during maturing. Average weight of the fruit increased to the range of 151-292% from 70 days to 90 days after blooming. The hardness of fruit decreased during maturing and Koume variety was not suitable for processing among the last harvesting samples. Moisture and ash were reached to 89-91%, 0.57-0.69%, respectively, and the ash content increased during maturing. Among the Ume varieties, relatively high content of total acid was observed in Oshuku and Koume. The major organic acid were malic, citric, succinic, and tartaric acid. The content of malic acid decreased significantly, whereas citric acid increased during maturing. Thirty five kinds of flavor components were identified from the Ume fruit and main components were ethyl acetate, butyl acetate, and organic acid derivatives. Koume contained a large amount of flavor components among the Ume and the content increased during maturing.