Shipping pallets often are designed with the assumption that the payload carried is flexible and uniformly distributed on the pallet surface. However, packages on the pallet can act as a series of discrete loads, and the physical interactions among the packages can add stiffness to the pallet/load combination. The term 'load bridging' has been used to describe this phenomenon. The study reported in this paper investigated the relationships of package size, corrugated flute type and pallet stiffness to load bridging and the resulting unit-load deflection. The experimental results indicated that an increase in box size changed the unit-load deflection by as much as 75%. Flute type was found to impact load bridging and the resulting unit-load deflection. Changing the corrugated box flute type from B-flute or BC-flute to E-flute reduces the unit-load deflection by as much as 40%. Also, experimental data indicates that the effect of package size and corrugated board flute type on pallet deflection is the greatest for low stiffness pallets. The results provide information that can be used to design unit loads that use material more efficiently.The average deflection values for each box sizes marked by the different letters are significantly different from each other at α = 0.05. 38J. PARK ET AL.

Abstract-The portfolio management for trading in the stock market poses a challenging stochastic control problem of significant commercial interests to finance industry. To date, many researchers have proposed various methods to build an intelligent portfolio management system that can recommend financial decisions for daily stock trading. Many promising results have been reported from the supervised learning community on the possibility of building a profitable trading system. More recently, several studies have shown that even the problem of integrating stock price prediction results with trading strategies can be successfully addressed by applying reinforcement learning algorithms. Motivated by this, we present a new stock trading framework that attempts to further enhance the performance of reinforcement learning-based systems. The proposed approach incorporates multiple Q-learning agents, allowing them to effectively divide and conquer the stock trading problem by defining necessary roles for cooperatively carrying out stock pricing and selection decisions. Furthermore, in an attempt to address the complexity issue when considering a large amount of data to obtain long-term dependence among the stock prices, we present a representation scheme that can succinctly summarize the history of price changes. Experimental results on a Korean stock market show that the proposed trading framework outperforms those trained by other alternative approaches both in terms of profit and risk management.Index Terms-Financial prediction, intelligent multiagent systems, portfolio management, Q-learning, stock trading.

This study developed gate-to-gate life cycle inventory (LCI) data for the repair of 48 by 40 inch (1,219 by 1,016 millimeter [mm]) stringer-class wood pallets in the United States. Data were collected from seven wood pallet repair facilities. Approximately 1.98 FBM (foot, board measure) (4.67E-03 cubic meters) of lumber were used for repairing each 48 by 40 inch (1,219 by 1,016 mm) stringer-class wood pallet, the majority (97%) recovered from damaged pallets received by the pallet repair facilities. Repair equipment powered by electricity made the largest contribution to greenhouse gas (GHG) emissions. Steel nails used for the pallet repair had the largest contribution to GHG emissions among the material inputs, while use of recovered lumber yielded the largest GHG emissions credits. Overall, the repair process for a 48 by 40 inch (1,219 by 1,016 mm) stringer-class wood pallet had GHG credits rather than a positive GHG emission due to the GHG offsets from co-products. Keywords:industrial ecology life cycle assessment (LCA) lumber recycling greenhouse gas (GHG) emissions wood palletConflict of interest statement: The authors have no conflict to declare.

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In this paper, we introduce LiveGantt as a novel interactive schedule visualization tool that helps users explore highly-concurrent large schedules from various perspectives. Although a Gantt chart is the most common approach to illustrate schedules, currently available Gantt chart visualization tools suffer from limited scalability and lack of interactions. LiveGantt is built with newly designed algorithms and interactions to improve conventional charts with better scalability, explorability, and reschedulability. It employs resource reordering and task aggregation to display the schedules in a scalable way. LiveGantt provides four coordinated views and filtering techniques to help users explore and interact with the schedules in more flexible ways. In addition, LiveGantt is equipped with an efficient rescheduler to allow users to instantaneously modify their schedules based on their scheduling experience in the fields. To assess the usefulness of the application of LiveGantt, we conducted a case study on manufacturing schedule data with four industrial engineering researchers. Participants not only grasped an overview of a schedule but also explored the schedule from multiple perspectives to make enhancements.