Multi-Objective Optimization of Assembly Manufacturing Factory Setups

Factory setup lifecycles are most often described and prepared manually in CAD environments, based on experience and inputs from several cross-disciplinary processes. Early in the factory setup preparation, a so-called block layout is created. The intention is to describe a high-level view of the intended factory setup and to claim area reservations and allocations. Factory areas are then blocked, i.e., targeted to be used for specific intended resources and processes, later redefined with detailed factory setup layouts. This research presents an automated digital method for assembly manufacturing layout planning, where area utilization and ergonomics can be considered simultaneously in a cross-disciplinary manner. Input datasets are company-specific descriptions of required dimensions for specific area reservations, such as defined dimensions of operator workspace, material façades, aisles, and the sequence to realize the product assembly manufacturing process. To test and develop the digital method, a demonstrator has been developed with adaptation of existing software that simulates and proposes optimized designs of detailed layouts. The method considers productivity, ergonomics, area utilization, and constraints, with a multi-objective optimization approach. In the demonstrator, the input data are sent to the simulation software IPS (Industrial Path Solutions). Based on the input and Lua scripts, the IPS software generates a block layout in compliance with the company’s defined dimensions of area reservations. Communication is then established between the IPS and the software EPP (Ergonomics in Productivity Platform), including intended resource descriptions, assembly manufacturing process, and manikin (digital human) resources. Using multi-objective optimization approaches, the EPP software then calculates layout proposals that are sent iteratively and simulated and rendered in IPS, following rules and regulations defined in the block layout as well as productivity and ergonomics constraints objectives. The developed demonstrator is promising and can optimize multiple parameters and weight the parameters to fine tune the optimal result of the detailed layout. The intention of the demonstrator is to make the preparation cross-disciplinary, transparent and achieve a common preparation of the assembly manufacturing factory setup, thereby facilitating better decisions.This research was made possible by the support of Scania CV AB’s Global Industrial Development and the VF-KDO project (Virtual Factories with Knowledge-Driven Optimization) funded by the Knowledge Foundation via the University of Skövde.