Optimisation of the control function for multiple automated interacting production stations is a complex problem, even for skilled and experienced operators or process planners. When using mathematical optimisation techniques, it often becomes necessary to use simulation models to represent the problem because of the high complexity (i.e. simulation-based optimisation). Standard optimisation techniques are likely to either exceed the practical time frame or under-perform compared to the manual tuning by the operators or process planners. This paper presents the Constructive cooperative coevolutionary (C3) algorithm, which objective is to enable effective simulation-based optimisation for the control of automated interacting production stations within a practical time frame. C3 is inspired by an existing cooperative coevolutionary algorithm. Thereby, it embeds an algorithm that optimises subproblems separately. C3 also incorporates a novel constructive heuristic to find good initial solutions and thereby expedite the optimisation. In this work, two industrial optimisation problems, involving interaction production stations, with different sizes are used to evaluate C3. The results illustrate that with C3, it is possible to optimise these problems within a practical time frame and obtain a better solution compared to manual tuning.

This article investigates multi-objective optimization of the robot trajectories and position-based operation-coordination of complex multi-robot systems, such as press lines, to improve the production rate and obtaining smooth motions to avoid excessive wear of the robots’ components. Different functions for handling the multiple objectives are evaluated on realworld press lines, including both scalarizing single-objective functions and Pareto-based multi-objective functions. Additionally, the Multi-Objective Constructive Cooperative Coevolutionary (moC3) algorithm is proposed, for Pareto-based optimization, which uses a novel constructive initialization of the subpopulations in a co-adaptive fashion. It was found that Paretobased optimization performs better than the scalarizing single-objective functions. Furthermore, moC3 gives substantially better results compared to manual online tuning, as currently used in the industry. Optimizing robot trajectories and operation-coordination of complex multi-robot systems using the proposed method with moC3 significantly improves productivity and reduces maintenance. This article hereby addresses the lack of systematic methods for effectively improving the productivity of press lines.

Today most industries aim at reducing energy consumption to become sustainable and environment-friendly. The automotive industry, with mass production and large volumes, is one such example. With many robots working round the clock, there is great potential to save energy. In this climate there is a need for robot simulation models that can be used for motion and task execution optimisation and which are aimed lowering energy consumption. This paper presents an energy consumption model for 2D-belt robots for press line tending in the automotive sector. The energy model is generic for 2D-belt robots and is entirely based on component specifications (e.g., dimensions, masses, inertia). An implementation and validation against a real 2D-belt tending robot used in the automotive industry is performed and presented. The purpose and usefulness of the energy model is also demonstrated by two application cases; the investigation of potential energy reductions achieved by reducing the weight of gripper tools, and by using mechanical brakes when the robot is idle.