Towards an Affordance-Based Ad-Hoc Suitability Network for Indoor Manufacturing Transportation Processes
Received: 30 June 2017 / Revised: 19 August 2017 / Accepted: 31 August 2017 / Published: 5 September 2017
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In manufacturing companies, productivity and efficiency are the main priorities, besides an emphasis on quality issues. The outcome of this research contributes to increasing production quality and efficiency in manufacturing. The article deals with indoor manufacturing environments and the transportation processes of production
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In manufacturing companies, productivity and efficiency are the main priorities, besides an emphasis on quality issues. The outcome of this research contributes to increasing production quality and efficiency in manufacturing. The article deals with indoor manufacturing environments and the transportation processes of production assets—referred to as smart transportation. The authors modelled the objects present in the indoor manufacturing environment with ontologies including their affordances and spatial suitability. To support flexible production and dynamic transportation processes have to be tailored towards the ‘needs’ of the production asset. Hence, the authors propose an approach utilizing an ad-hoc suitability network to support the “optimal” path computation for transportation processes. The objective is to generate a graph for routing purposes for each individual production asset, with respect to the affordances of the indoor space for each production asset, and measurements of a sensor network. The generation of the graph follows an ad-hoc strategy, in two ways. First, the indoor navigation graph is created exactly when a path needs to be found—when a production asset shall be transported to the next manufacturing step. Secondly, the transportation necessities of each production asset, as well as any disturbances present in the environment, are taken into account at the time of the path calculation. The novelty of this approach is that the development of the navigation graph—including the weights—is done with affordances, which are based on an ontology. To realize the approach, the authors developed a linked data approach based on manufacturing data and on an application ontology, linking the indoor manufacturing environment and a graph-based network. The linked data approach is finally implemented as a spatial graph database containing walkable corridors, production equipment, assets and a sensor network. The results show the optimal path for transportation processes with respect to affordances of the indoor manufacturing environments. An evaluation of the computational complexity shows that the affordance-based ad-hoc graphs are thinner and thus reduce the computational complexity of shortest path calculations. Hence, we conclude that an affordance-based approach can help to decrease computational efforts for calculating “optimal” paths for transportation purposes.