Network design decisions in supply chain planning

Structuring global supply chain networks is a complex decision-making process. The typical inputs to such a process consist of a set of customer zones to serve, a set of products to be manufactured and distributed, demand projections for the different customer zones, and information about future conditions, costs (e.g. for production and transportation) and resources (e.g. capacities, available raw materials). Given the above inputs, companies have to decide where to locate new service facilities (e.g. plants, warehouses), how to allocate procurement and production activities to the variousmanufacturing facilities, and how to manage the transportation of products through the supply chain network in order to satisfy customer demands. We propose a mathematical modelling framework capturing many practical aspects of network design problems simultaneously. For problems of reasonable size we report on computational experience with standard mathematical programming software. The discussion is extended with other decisions required by many real-life applications in strategic supply chain planning. In particular, the multi-period nature of some decisions is addressed by a more comprehensivemodel, which is solved by a specially tailored heuristic approach. The numerical results suggest that the solution procedure can identify high quality solutions within reasonable computational time

Predicting outcomes in crowdfunding campaigns with textual, visual, and linguistic signals

This paper introduces a neural network and natural language processing approach to predict the outcome of crowdfunding startup pitches using text, speech, and video metadata in 20,188 crowdfunding campaigns. Our study emphasizes the need to understand crowdfunding from an investor’s perspective. Linguistic styles in crowdfunding campaigns that aim to trigger excitement or are aimed at inclusiveness are better predictors of campaign success than firm-level determinants. At the contrary, higher uncertainty perceptions about the state of product development may substantially reduce evaluations of new products and reduce purchasing intentions among potential funders. Our findings emphasize that positive psychological language is salient in environments where objective information is scarce and where investment preferences are taste based. Employing enthusiastic language or showing the product in action may capture an individual’s attention. Using all technology and design-related crowdfunding campaigns launched on Kickstarter, our study underscores the need to align potential consumers’ expectations with the visualization and presentation of the crowdfunding campaign

FUND ALLOCATION METHOD BASED ON A BLOCK OF SHARES

Abstract. In a real investment, stocks are dealt with based on a block of shares. A block of shares is a minimum unit for trading stocks. However, a conventional portfolio selection problem does not consider about a block of shares. If we deal with stocks according to a block of shares, real allocations of funds to each stock should differ among the cases of different amounts of money. Furthermore, a decision maker should be unable to buy less than one block even if the investing ratio for some stock is much smaller. The objective of this paper is to build a portfolio selection model in consideration of the amount of investing funds and a block of shares. Our model is formulated as an integer quadratic programming problem. In general, an integer nonlinear programming problem is difficult to solve for all but the smallest cases. So we also propose the efficiently approximate model employing a Meta-controlled Boltzmann machine

Dirichlet belief networks for topic structure learning

Recently, considerable research effort has been devoted to developing deep architectures for topic models to learn topic structures. Although several deep models have been proposed to learn better topic proportions of documents, how to leverage the benefits of deep structures for learning word distributions of topics has not yet been rigorously studied. Here we propose a new multi-layer generative process on word distributions of topics, where each layer consists of a set of topics and each topic is drawn from a mixture of the topics of the layer above. As the topics in all layers can be directly interpreted by words, the proposed model is able to discover interpretable topic hierarchies. As a self-contained module, our model can be flexibly adapted to different kinds of topic models to improve their modelling accuracy and interpretability. Extensive experiments on text corpora demonstrate the advantages of the proposed model.Comment: accepted in NIPS 201

Hierarchical spatial-temporal state machine for vehicle instrument cluster manufacturing

The vehicle instrument cluster is one of the most advanced and complicated electronic embedded control systems used in modern vehicles providing a driver with an interface to control and determine the status of the vehicle. In this paper, we develop a novel hybrid approach called Hierarchical Spatial-Temporal State Machine (HSTSM). The approach addresses a problem of spatial-temporal inference in complex dynamic systems. It is based on a memory-prediction framework and Deep Neural Networks (DNN) which is used for fault detection and isolation in automatic inspection and manufacturing of vehicle instrument cluster. The technique has been compared with existing methods namely rule-based, template-based, Bayesian, restricted Boltzmann machine and hierarchical temporal memory methods. Results show that the proposed approach can successfully diagnose and locate multiple classes of faults under real-time working conditions

Being Interdisciplinary: Adventures in urban science and beyond

In Being Interdisciplinary, Alan Wilson draws on five decades as a leading figure in urban science to set out a systems approach to interdisciplinarity for those conducting research in this and other fields. He argues that most research is interdisciplinary at base, and that a systems perspective is particularly appropriate for collaboration because it fosters an outlook that sees beyond disciplines. There is a more subtle thread, too. A systems approach enables researchers to identify the game-changers of the past as a basis for thinking outside convention, for learning how to do something new and how to be ambitious, in a nutshell how to be creative. Ultimately, the ideas presented address how to do research. Building on this systems focus, the book first establishes the basics of interdisciplinarity. Then, by drawing on the author’s experience of doing interdisciplinary research, and working from his personal toolkit, it offers general principles and a framework from which researchers can build their own interdisciplinary toolkit, with elements ranging from explorations of game-changers in research to superconcepts. In the last section, the book tackles questions of managing and organising research from individual to institutional scales. Alan Wilson deploys his wide experience – researcher in urban science, university professor and vice-chancellor, civil servant and institute director – to build the narrative. While his experience in urban science provides the illustrations, the principles apply across many research fields