Wooyeon Yu

Scheduling of inbound and outbound trucks in cross docking systems with temporary storage

Cross docking is a warehouse management concept in which items delivered to a warehouse by inbound trucks are immediately sorted out, reorganized based on customer demands, routed and loaded into outbound trucks for delivery to customers without the items being actually held in inventory at the warehouse. If any item is held in storage, it is usually for a brief period of time that is generally less than 24 hours. This way, the turnaround times for customer orders, inventory management cost, and warehouse space requirements are reduced. One of the objectives for cross docking systems is how well the trucks can be scheduled at the dock and how the items in inbound trucks can be allocated to the outbound trucks to optimize on some measure of system performance. The objective of this research is to find the best truck docking or scheduling sequence for both inbound and outbound trucks to minimize total operation time when a temporary storage buffer to hold items temporarily is located at the shipping dock. The product assignment to trucks and the docking sequences of the inbound and outbound trucks are all determined simultaneously.

Research Issues in Smart Vehicles and Elderly Drivers: A Literature Review

This article reviewed both studies on general smart car technologies and human–computer interaction (HCI)/human–vehicle interaction studies that were published in journals and conferences so that the current status of research can be identified and future research directions can be suggested. Furthermore, previous studies on elderly drivers were reviewed, as these drivers could be the most vulnerable social group in terms of new technology acceptance. A total of 257 articles for HCI research and 45 articles for elderly drivers were selected and reviewed from 11,267 collected articles (2010–2014). According to the results, most articles were mainly related to safety and adaptive features (e.g., driver’s state recognition, vehicle surrounding monitoring, driver action-suggestion), and infotainment research in terms of HCI (e.g., information technology devices–vehicle interaction, vehicle–vehicle interaction) was relatively insufficient despite its high research demand. According to the results of the literature review and technological trends analysis based on previous technical road maps, from HCI/human factors engineering (HFE) perspectives, research related to “Assistance systems,” “Physiological & mental state recognition,” “Position sensor technology,” “Behavior recognition,” and “Infotainment” was suggested to HCI/HFE researchers for further research. In particular, HCI/HFE researchers need to focus on research on acceptable levels of automation, observing new driving behaviors, investigation of driver characteristics to develop personalized services, and new technology acceptance to develop and improve smart cars in the future.

Arterial Road Incident Detection Based on Time-Moving Average Method in Bluetooth-Based Wireless Vehicle Reidentification System

AbstractIncident detection algorithms, which are an essential part of traffic management systems, have been studied for several decades, but the research focus has primarily been on algorithms for incident detection on freeways and other free-flowing roads. When applied on arterial roads, the achievement of stable performance and scalability are major challenges when developing an effective incident detection algorithm. In this research, the authors propose an incident detection algorithm that utilizes travel time and traffic volume samples generated from a Bluetooth-based wireless vehicle reidentification system that has been implemented on arterial roads. The proposed algorithm is based on a moving average over time, which can recognize sample travel time and traffic volume patterns resulting from incidents. The use of a moving average overcomes limitations resulting from sparse travel time sample data collected. Within the algorithm, a threshold strategy is applied that makes the algorithm easy to impl...

An Arterial Incident Detection Procedure Utilizing Real-Time Vehicle Reidentification Travel Time Data

Our road and highway system has become a source of multiple types of “big data.” One such type of data is travel time data obtained from vehicle reidentification systems, a type that is increasingly available due to the implementation of existing and relatively new technologies such as license-plate recognition and Bluetooth-based wireless vehicle identification. Travel time data obtained in real time from such systems are used to update estimated travel times displayed on variable message signs, and research has also been conducted that utilizes travel time data as inputs to incident detection algorithms. Implementation of such systems and prior research has primarily focused on freeways and other free-flowing roads. However, such systems for travel time data collection are also being implemented on arterials. In this research an incident detection procedure that utilizes point-to-point travel time data obtained from an arterial vehicle reidentification system is developed and evaluated. Historical travel time data provided by a Bluetooth-based travel time data collection system and reported incident data are utilized to evaluate the procedure. The results show that the procedure provides a good balance of detection and false alarm rates.

Truck Scheduling for Cross Docking Systems with Multiple Receiving and Shipping Docks

The objective of this study is to find the best docking sequences for trucks at each receiving and shipping dock as well as assignments of inbound and outbound trucks to docks to minimise the makespan of the cross docking system with multiple docks. The unloading sequences of products from inbound trucks, product routing, and routing sequences are also determined simultaneously. To solve the cross docking problem, two approaches - a mathematical model and two heuristic algorithms - were developed. Due to time complexity of the mathematical model, the lower bound of the makespan is developed to compare the performance of the heuristics. The results shows that the makespans found from the heuristics are very close to the lower bounds of the problems.

Design and Implementation of Robotic Parcel-Sorting Systems

With the recent increase in e-business, post offices or door-to-door parcel delivery companies have witnessed a corresponding increase in the number of parcels handled. Most of these parcels are still being handled manually, which results in an increased workload for employees and lowers efficiency. Korea Post has considered a smart-robot manipulation system to automate parcel-sorting procedures that remain manual. This study examined parcel-sorting methods using a robotic system with the aim of reducing the workloads of deliverymen and enhancing parcel-sorting efficiency. We developed a methodology for the design of a robotic parcel-sorting system, and the system was then tested and implemented in accordance with this methodology. The results of this study are expected to reduce parcel workloads of deliverymen and improve parcel-sorting efficiency.