# Optimal Configuration of Modular Strongrooms Using Multi-Attribute Decision Making
## 1 Introduction  
Optimization is a key stage in the configuration of complex products. Complex products usually have a modular architecture and are formed by combining standard modules. The modular product system also includes the production of parts and modules in sufficiently large series, an acceptable number of technologies, tools, and accessories, and reduced amounts of information. Also, the construction of available modules and their components has already been optimized, and the technological processes of production and assembly have been standardized and optimized from the aspect of processing modes, costs, processing time, etc. Therefore, that part of the optimization process was not considered in this paper. Configuration optimization has the task of performing multi-criteria evaluation of products at the macro level in terms of cost, quality, and production time and is solved by multi-criteria decision-making methods [1]. Strongrooms are intended for storing money, valuables, securities, and confidential documents. The basic function they must satisfy is resistance to attacks using burglary tools (burglary resistance) [1, 2, 3]. Instead of the massive construction of strongrooms, Modular Strongrooms (MSR) are increasingly used today because they have several advantages [1, 2, 3]:  
* They have thinner walls, higher usable volume, and lower weight;* The possibility of remodeling already existing rooms into a strongroom;
* Abbreviated time to build a strongroom. The Modular Strongroom (MSR) is a complex and demanding product from a technical and technological viewpoint. It is designed according to the individual request of the customer who chooses the Resistance Grade according to the standard [4] and defines the available dimensions of the space in which it is necessary to place such a product. The configuration is achieved by a combination of standard modules whose number and dimensions must be adjusted in each new project. Knowledge-Based Engineering (KBE) is a set of methods and algorithms that incorporate design knowledge into CAD models by considering dimensioning formulas, design rules, and manufacturing restrictions, thereby transforming design problems into configuration problems [5]. Automatic configuration of products in CAD systems is enabled by the integration of KBE and CAD. There are different ways of integrating, embedding, and coupling KBEs to CAD systems [6]. Various case studies on design automation through KBE-CAD integration can be found in the literature. [PERSON] [6] presented a case study of a locating jig that demonstrated the basic availability of integrating KBE with Autodesk Inventor. [PERSON] and [PERSON] [7] developed a knowledge-based parametric CAD modeling system for spur gear design that incorporates AGMA standards and parametric modeling in CAD. [PERSON] [8] implemented a constraint-based configuration system with a CAD system using the example of a locating fixture. [PERSON] et al. [9] developed a design automation system that automatically generates a motor hoisting crane design based on user requirements. [PERSON] [10] bridged the gap between constraint satisfaction problems (CSP) and CAD systems, providing practical guidance and examples for implementing configuration design tasks using Autodesk Inventor. [PERSON] et al. [11] focused on the automated sizing of automotive steering ball joints and incorporated expert knowledge and feature-based 3D CAD modeling to streamline the sizing process. [PERSON] et al. [2] developed a platform for the automatic configuration of MSR, through the integration of an MS Excel spreadsheet and a CAD system (Autodesk Inventor 2021). The disadvantage of this approach is that it generates only one MSR variant. Since an MSR is a complex product, and multiple configurations can be generated to satisfy customer requirements (CRs), it was necessary to develop software that will generate all feasible configurations and choose the optimal configuration with the aim of better utilization of resources.