The diversity of smart EV(electric vehicle)-related industries is increasing due to the growth of battery-based eco-friendly electric vehicle component material technology, and labor-intensive industries such as logistics, manufacturing, food, agriculture, and service have invested in and studied automation for a long time. Accordingly, various types of robots such as autonomous mobile robots and collaborative robots are being utilized for each process to improve industrial engineering such as optimization, productivity management, and work management. The technology that should accompany this unmanned automobile industry is unmanned automatic charging technology, and if autonomous mobile robots are manually charged, the utility of autonomous mobile robots will not be maximized. In this paper, we conducted a study on the technology of unmanned charging of autonomous mobile robots using charging terminal docking and undocking technology using an unmanned charging system composed of hardware such as a monocular camera, multi-joint robot, gripper, and server. In an experiment to evaluate the performance of the system, the average charging terminal recognition rate was 98%, and the average charging terminal recognition speed was 0.0099 seconds. In addition, an experiment was conducted to evaluate the docking and undocking success rate of the charging terminal, and the experimental results showed an average success rate of 99%.

The respective delivering vehicle loaded with the own cargo moves into the respective delivery area. At the base, the delivery points D1 and D2 ,for example, have the same starting point but the destination is different. The average delivering time of the delivery vehicle is mostly more than 8 hours a day. Therefore, the efficiency of delivery is generally low. In this study, the deliveries will be forwarded from a base station to a delivery point where cross docking will be applied to a single vehicle, and will be distributed from the cross docking point through cross docking. If the distribution is implemented, one vehicle will not have to be operated from the base to the cross docking point. In that case, logistics cost will be reasonably saved by the reduction of transportation cost and labor time. If one vehicle only runs from the base to the cross docking point, each vehicle will be operated in two shifts, and the vehicle operation can be efficiently implemented. This research model is based on the assumption that the 3 types of ratios between the traffic volume of the vehicles starting at the base and the vehicles waiting at the cross docking point are set to the first ratio of 30% to 70%, the second ratio of 50% to 50% and the final ratio of 70% to 30%. As a result of the study, The delivery time€in the cross docking point is much higher than that in present on the condition that the cargo volume in the D2 area is more than 50%. Likewise, the delivery time is slightly higher€on the condition that€the cargo volume€is less than 50%. Time is reduced in terms of 50% model like AS-IS model.

A cross docking operation involves multiple inbound trucks that deliver items from suppliers to a distribution center and multiple outbound trucks that ship items from the distribution center to customers. Based on customer demands, an inbound truck may have its items transferred to multiple outbound trucks. Similarly, an outbound truck can receive its consignments from multiple inbound trucks. The objective of this study is to find the best truck spotting sequence for both inbound and outbound trucks in order to minimize total operation time of the cross docking system under the condition that multiple visits to the dock by a truck to unload or load its consignments is allowed. The allocations of the items from inbound trucks to outbound trucks are determined simultaneously with the spotting sequences of both the inbound and outbound trucks.

The binding affinity constants (p(Od)50) and molecular docking scores (DS) between porcine odorant binding proteins pOBP (1HQP) and pPBP (1GM6) as receptor and a series of tetrahydrofuran-2-yl (A & B) analogues as substrate, and their interactions were discussed quantitatively using three-dimensional quantitative structure-activity relationship (3D-QSAR) models. The statistical qualities of the optimized CoMFA models for pOBP were better than those of the CoMSIA models. The binding affinity constants and DS between substrate and receptor molecules were dependent upon steric and hydrophobic interaction. The DS constants of the substrates into the binding site of OBP (1HQP) were bigger than those of PBP (1GM6). The resulting contour maps produced by the optimized CoMFA model were used to identify the structural features relevant to the binding affinity in binding site of pOBP.

Many logistics enterprises have made efforts to achieve low costly and high efficient logistics network. The cross-docking system can be a good solution for them. However, it requires tight schedule and all-night operation inevitably for realization of ideal cross-docking. These causes the difficulty of the attainment of daily delivery target and the leave of delivery service persons. In this paper, we develop the line-haul and shuttle service compromised cross-docking model in order to solve the problems practically. We apply the storage process with the cross-docking system and the direct cross-docking between line-haul and shuttle services. The simulation model validates the shorter delivery time by the developed model than the present model.

Cross docking is a warehouse management concept in which items delivered to a distribution facility by inbound trucks are immediately sorted out and reorganized based on customer demands and are routed and loaded into outbound trucks for delivery to customers without actually being held in inventory in the distribution facility. In this research, the design of distribution facility for cross docking systems was studied. The objective of this research is to find the minimum number of receiving docks and shipping docks, respectively, in order to meet the daily demand of the distribution center. Two solution approaches are employed in modeling and solving the problem The first approach is mathematical modeling and the second approach is a simulation. The logic developed in the simulation model is expected to apply to the real world situation.

A cross docking operation involves multiple trucks (known as inbound trucks) that deliver items from suppliers to a distribution center and multiple trucks (known as outbound trucks) that ship items from the distribution center to customers. Based on customer demands, an inbound truck may have its items transferred to multiple outbound trucks. Similarly, an outbound truck can receive its consignments from multiple inbound trucks. A unique characteristic of a cross docking system is the absence or prohibition of long term storage of items at the distribution center. Items delivered to the distribution center from suppliers are shipped to customers as soon as possible without being placed in storage in the distribution center. The objective of this paper is to develop the optimal operational strategy for finding the best truck docking sequence for both inbound and outbound trucks in order to minimize total operation time where a temporary storage area is not available in a cross docking system.

크로스도킹은 물류센터의 운영 개념으로써 입고트럭에 의해 배달된 물품이 재고로써 보관됨이 없이 즉시 고객의 수요에 따라 재분류되어 출고트럭에 적재되어 고객에게 배달되는 프로세스로 구성된다. 본 연구에서는 임시보관 장소를 보유한 크로스도킹 시스템의 총 운영시간을 최소화하기 위한 입고 트럭과 출고 트럭의 일정계획 수립을 위한 수학적 모델을 개발하였다. 본 연구에서 개발한 모델의 적용으로 물류센터 내에서의 자재 취급 빈도 및 시간이 감소하여 제품 손상을 최소화

크로스도킹이란 창고나 물류센터에 하역된 물품이 저장됨이 없이 도착지별로 재분류되어서 직출하되는 물류 시스템이다. 크로스도킹은 물류비용의 큰 비중을 차지하는 보관비용을 감소시킬 수 있을 뿐만 아니라 고객의 요구에 빠른 대응을 할 수 있다는 장점을 지니고 있다. 크로스도킹이 성공적으로 수행되기 위해서는 창고나 물류센터의 입고에서부터 출고까지의 모든 작업들이 계획적이고 원활하게 수행되어져야만 한다. 본 연구에서는 임시보관 장소를 보유하지 않은 크로스도킹 시스

The docking and recharging system for a mobile robot must guarantee the ability of the mobile robot to perform its tasks continuously without human intervention. In this paper, two docking mechanisms are proposed with localization error-compensation capability for the auto recharging system. Friction forces or magnetic forces are used between the docking parts of the docking module and those of the docking station. In addition, an auto recharging system is developed to control the power. Since the system is modularized, it can easily be adapted to other robots.