Ranking Information in Networks

Abstract not provide

BlueCollar: Optimizing Worker Paths on Factory Shop Floors with Visual Analytics

The optimization of a factory\u27s productivity regarding quality and efficiency is an important task in the manufacturing domain. To optimize the productivity, production lines are optimized to have short transportation paths and short processing times at the stations that process intermediate components or the final product. A factory\u27s layout is a key factor in this optimization aspect. This optimization mostly comprises the machine tools\u27 positions with respect to places where supply goods are being delivered and other tools are stationed, often neglecting the paths that workers need to take at the shop floor. This impairs a factory\u27s productivity, as machines may need to wait for workers, who operated another machine and are still on the way due to the long distance between the machines. In this work, we present BlueCollar, a visual analytics approach that supports layout planners to explore and optimize existing factory layouts regarding the paths taken by workers. Planners can visually inspect the paths that workers need to take based on their work schedule and the factory\u27s layout. An estimation of distribution algorithm supports them in choosing which layout elements, e.g., shared tool caches, to relocate. Its intermediate and final results are used to provide visual cues for suitable relocation areas, and to suggest new layouts automatically. We demonstrate our approach through an application scenario based on a realistic prototype layout provided by an external company

An anytime tree search algorithm for the 2018 ROADEF/EURO challenge glass cutting problem

In this article, we present the anytime tree search algorithm we designed for the 2018 ROADEF/EURO challenge glass cutting problem proposed by the French company Saint-Gobain. The resulting program was ranked first among 64 participants. Its key components are: a new search algorithm called Memory Bounded A* (MBA*) with guide functions, a symmetry breaking strategy, and a pseudo-dominance rule. We perform a comprehensive study of these components showing that each of them contributes to the algorithm global performances. In addition, we designed a second tree search algorithm fully based on the pseudo-dominance rule and dedicated to some of the challenge instances with strong precedence constraints. On these instances, it finds the best-known solutions very quickly

Towards Optimally Efficient Search with Deep Learning for Large-Scale MIMO Systems

This paper investigates the optimal signal detection problem with a particular interest in large-scale multiple-input multiple-output (MIMO) systems. The problem is NP-hard and can be solved optimally by searching the shortest path on the decision tree. Unfortunately, the existing optimal search algorithms often involve prohibitively high complexities, which indicates that they are infeasible in large-scale MIMO systems. To address this issue, we propose a general heuristic search algorithm, namely, hyper-accelerated tree search (HATS) algorithm. The proposed algorithm employs a deep neural network (DNN) to estimate the optimal heuristic, and then use the estimated heuristic to speed up the underlying memory-bounded search algorithm. This idea is inspired by the fact that the underlying heuristic search algorithm reaches the optimal efficiency with the optimal heuristic function. Simulation results show that the proposed algorithm reaches almost the optimal bit error rate (BER) performance in large-scale systems, while the memory size can be bounded. In the meanwhile, it visits nearly the fewest tree nodes. This indicates that the proposed algorithm reaches almost the optimal efficiency in practical scenarios, and and thereby it is applicable for large-scale systems. Besides, the code for this paper is available at https://github.com/skypitcher/hats