A modern smart factory runs a manufacturing procedure using a collection of programmable machines. Typically, materials are ferried between these machines using a team of mobile robots. To embed a manufacturing procedure in a smart factory, a factory operator must a) assign its processes to the smart factory's machines and b) determine how agents should carry materials between machines. A good embedding maximizes the smart factory's throughput; the rate at which it outputs products. Existing smart factory management systems solve the aforementioned problems sequentially, limiting the throughput that they can achieve. In this paper we introduce ACES, the Anytime Cyclic Embedding Solver, the first solver which jointly optimizes the assignment of processes to machines and the assignment of paths to agents. We evaluate ACES and show that it can scale to real industrial scenarios.

Modern automated factories increasingly run manufacturing procedures using a matrix of programmable machines, such as 3D printers, interconnected by a programmable transport system, such as a fleet of tabletop robots. To embed a manufacturing procedure into a smart factory, an operator must: (a) assign each of its processes to a machine and (b) specify how agents should transport parts between machines. The problem of embedding a manufacturing process into a smart factory is termed the Smart Factory Embedding (SFE) problem. State-of-the-art SFE solvers can only scale to factories containing a couple dozen machines. Modern smart factories, however, may contain hundreds of machines. We fill this hole by introducing the first highly scalable solution to the SFE, TSACES, the Traffic System based Anytime Cyclic Embedding Solver. We show that TS-ACES is complete and can scale to SFE instances based on real industrial scenarios with more than a hundred machines.

Dataset discovery can be performed using search (with a query or keywords) to find relevant data. However, the result of this discovery can be overwhelming to explore. Existing navigation techniques mostly focus on linkage graphs that enable navigation from one data set to another based on similarity or joinability of attributes. However, users often do not know which data set to start the navigation from. RONIN proposes an alternative way to navigate by building a hierarchical structure on a collection of data sets: the user navigates between groups of data sets in a hierarchical manner to narrow down to the data of interest. We demonstrate RONIN, a tool that enables user exploration of a data lake by seamlessly integrating the two common modalities of discovery: data set search and navigation of a hierarchical structure. In RONIN, a user can perform a keyword search or joinability search over a data lake, then, navigate the result using a hierarchical structure, called an organization , that is created on the fly. While navigating an organization, the user may switch to the search mode, and back to navigation on an organization that is updated based on search. This integration of search and navigation provides great power in allowing users to find and explore interesting data in a data lake.