The Use of a Factory Simulation to Evaluate a Flexible Control Structure for Integrated Manufacturing

Once a control structure for an integrated manufacturing system is decided upon, manufacturing activities are limited by that structure. A flexible control structure is presented as an approach for accommodating a variety of manufacturing activities, without being limited to a single control structure. A flexible control structure is one that allows multiple types of control structure in the manufacturing process. For example, both hierarchical and non-hierarchical structures may be used in a flexible structure. The properties of a flexible control structure are discussed from the point of view of graph theory. Control structures for automated manufacturing are difficult to evaluate without actually setting up a pilot production system. Since this is often not possible for reasons of expense or equipment availability, it would be advantageous to be able to simulate alternative control structures for their various characteristics. In this research, flexible control is demonstrated with a factory simulation of an automated on-line/post-process inspection system. Factory simulations present special problems when used for evaluation purposes. An approach to using a factory simulation is developed, and alternative control structures are evaluated with respect to their fault tolerance characteristics. The results of this research indicate that flexible control may be cost effective when a large variety of manufacturing activities must be accommodated, but further research is needed to confirm precisely how wide a range and what types of activities would justify this approach

Rethinking Item Importance in Session-based Recommendation

Session-based recommendation aims to predict users' based on anonymous sessions. Previous work mainly focuses on the transition relationship between items during an ongoing session. They generally fail to pay enough attention to the importance of the items in terms of their relevance to user's main intent. In this paper, we propose a Session-based Recommendation approach with an Importance Extraction Module, i.e., SR-IEM, that considers both a user's long-term and recent behavior in an ongoing session. We employ a modified self-attention mechanism to estimate item importance in a session, which is then used to predict user's long-term preference. Item recommendations are produced by combining the user's long-term preference and current interest as conveyed by the last interacted item. Experiments conducted on two benchmark datasets validate that SR-IEM outperforms the start-of-the-art in terms of Recall and MRR and has a reduced computational complexity

A Deep Reinforcement Learning Framework for Rebalancing Dockless Bike Sharing Systems

Bike sharing provides an environment-friendly way for traveling and is booming all over the world. Yet, due to the high similarity of user travel patterns, the bike imbalance problem constantly occurs, especially for dockless bike sharing systems, causing significant impact on service quality and company revenue. Thus, it has become a critical task for bike sharing systems to resolve such imbalance efficiently. In this paper, we propose a novel deep reinforcement learning framework for incentivizing users to rebalance such systems. We model the problem as a Markov decision process and take both spatial and temporal features into consideration. We develop a novel deep reinforcement learning algorithm called Hierarchical Reinforcement Pricing (HRP), which builds upon the Deep Deterministic Policy Gradient algorithm. Different from existing methods that often ignore spatial information and rely heavily on accurate prediction, HRP captures both spatial and temporal dependencies using a divide-and-conquer structure with an embedded localized module. We conduct extensive experiments to evaluate HRP, based on a dataset from Mobike, a major Chinese dockless bike sharing company. Results show that HRP performs close to the 24-timeslot look-ahead optimization, and outperforms state-of-the-art methods in both service level and bike distribution. It also transfers well when applied to unseen areas

Entanglement wedge cross section inequalities in AdS/BCFT

The entanglement wedge cross section in holographic picture provides a geometrical description of the entanglement for mixed states. In this paper we study the inequalities for the entanglement wedge cross section in AdS/BCFT duality. In the presence of the boundary in conformal field theory (CFT), the dual entanglement wedge cross section exhibits abundant phase structures since the extremal surface may end on the brane. We present a universal treatment which is applicable for all the possible phases such that the inequalities for the entanglement wedge cross section can be proved in an algebraic manner rather than a diagrammatic manner. We show that the entanglement wedge cross section in AdS/BCFT satisfies the same inequalities as in AdS/CFT