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Cross docking is a warehouse management concept in which items delivered to a warehouse by delivery trucks are immediately sorted out and reorganized based on customer demands and are routed and loaded into shipping trucks for delivery to customers without actually being held in inventory in the warehouse. If any item is to be held in storage, it is only for a brief period of time that is generally less than twenty-four hours. This way, the turnaround times for customer orders, inventory management cost, and warehouse space requirements are reduced. Because accuracy in material management is required in such operations, a cross docking operation is heavily dependent on accurate flow of information. Depending on the facility and operating conditions or strategies employed, it is possible to generate various cross docking scenarios or models. In this research, thirty-two different models are identified based on the number of docks available at the site, the dock holding pattern for trucks, and the existence of temporary storage. Of the thirty-two models identified, this research is focused on three. All three models assume there is a separate truck receiving dock and a separate truck shipping dock. It is also assumed that the items contained in a receiving truck and the items needed for a shipping truck are known in advance. Receiving Yard Receiving Area: Unloading Scanning

In this research, a truck scheduling problem for a cross-docking system with multiple receiving and shipping docks is studied. Until recently, single-dock cross-docking problems are studied mostly. This research is focused on the multiple-dock problems. The objective of the problem is to determine the best docking sequences of inbound and outbound trucks to the receiving and shipping docks, respectively, which minimize the maximal completion time. We propose a new hybrid genetic algorithm to solve this problem. This genetic algorithm improves the solution quality through the population scheme of the nested structure and the new product routing heuristic. To avoid unnecessary infeasible solutions, a linked-chromosome representation is used to link the inbound and outbound truck sequences, and locus-pairing crossovers and mutations for this representation are proposed. As a result of the evaluation of the benchmark problems, it shows that the proposed hybrid GA provides a superior solution compared to the existing heuristics.

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In this paper, we reviewed both studies on general smart car technologies and HCI/HVI studies that were published in journals and conferences, so that we can identify the current status of research and suggest future research directions. Furthermore, we reviewed previous studies on elderly drivers as they 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 (from 2010 to 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., IT devices-vehicle interaction, vehicle-vehicle interaction) was relatively insufficient despite its high research demand. According to the results of the literature Downloaded by [New York University] at 03:59 29 July 2015 A c c e p t e d M a n u s c r i p t 2 review and technological trends analysis based on previous technical roadmaps, from HCI/HFE perspectives, research related to 'Assistance systems', 'Physiological & mental state recognition', 'Position sensor technology', 'Behavior recognition', and 'Infotainment' were suggested to HCI/HFE researchers for the 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.