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Reduction of energy consumption is important for reaching a sustainable future. This paper presents a novel method for optimizing the energy consumption of robotic manufacturing systems. The method embeds detailed evaluations of robots' energy consumptions into a scheduling model of the overall system. The energy consumption for each operation is modelled and parameterized as function of the operation execution time, and the energy-optimal schedule is derived by solving a mixed-integer nonlinear programming problem. The objective function for the optimization problem is then the total energy consumption for the overall system. A case study of a sample robotic manufacturing system is presented. It shows that there exists a possibility for a significant reduction of the energy consumption, in comparison to state-of-the-art scheduling approaches.

Reduction of energy consumption is important for reaching a sustainable future. This paper presents a novel method for optimizing the energy consumption of robotic manufacturing systems. The method embeds detailed evaluations of robots' energy consumptions into a scheduling model of the overall system. The energy consumption for each operation is modeled and parameterized as function of the operation execution time, and the energy-optimal schedule is derived by solving a mixed-integer nonlinear programming problem. The objective function for the optimization problem is then the total energy consumption for the overall system. A case study of a sample robotic manufacturing system and an experiment on an industrial robot are presented. They show that there exists a real possibility for a significant reduction of the energy consumption in comparison to state-of-the-art scheduling approaches

The sequential behavior of a manufacturing system results from several constraints introduced during the product, manufacturing, and control logic development. This paper proposes methods and algorithms for automatically representing and visualizing this behavior from various perspectives throughout the development process. A new sequence planning approach is introduced that uses self-contained operations to model the activities and execution constraints. These operations can be represented and visualized from multiple perspectives using a graphical and formal language called Sequences of Operations (SOPs). The operations in a manufacturing system are related to each other in various ways, due to execution constraints expressed by operation pre-and post-conditions. These operation relations include parallel, sequence, arbitrary order, alternative, and hierarchy relations. Based on the SOP language, these relations are identified and visualized in various SOPs and sequences. A software tool, Sequence Planner, has been developed, for organizing the operations into SOPs that visualize only relevant operations and relations. Note to Practitioners-This paper proposes methods and algorithms for a new sequence planning approach in which sequences are automatically created based on the relations among operations instead of having to be manually constructed. Using various views, the sequences of operations related to, for example, part flow, robot operations, and operator tasks, can be visualized. The use of various views helps the user better understand the relations between cell control and mechanical design, and between product design and total system behavior.

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This paper compares two languages, Systems Modeling Language(SysML) based on Unified Modeling Language and Sequence Planner Language, where both are used for systems engineering applications. As the modern manufacturing industries pass through a challenging period in storing and exchanging huge amounts of information/data, a common platform that is helpful in putting different parts together is of major interest. This paper presents an analysis of these two languages focusing on their behavioral constructs and details their advantages and disadvantages. This paper concludes by mentioning the points that are lacking in SysML, which could be solved by combining it with Sequence Planner Language and using the combined approach for system engineering applications.