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This article describes a new interactive design approach integrating the constraints associated with production include manufacturing and assembling. The proposed method, in the form of an algorithm, allows optimisation of product design by minimizing production costs at each iteration, without compromising its functionality. The novelty of this algorithm in terms of modeling and optimisation of production costs in the design phase is its ability to dynamically evaluate the cumulative costs of production as a function of design and procedural choices. The availability of this information first allows the identification of design points and/or procedural points that generate significant production costs, and second, suggests improvements and recommendations that aim to optimize production costs. These experiments were conducted at a smart factory installed in our organisation. The proposed algorithm involves four steps. To optimise production costs, the designer must input all of the required data into the simulation and thereby identify the most significant cost elements to optimise. Then, the designer uses the suggested recommendation list to modify the relevant design and/or manufacturing parameters, thus obtaining the new, optimised production costs. If the first result is unsatisfactory, other iterations can be performed.

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PurposeThis paper proposes a new simultaneous optimization model of the industrial systems design and maintenance. This model aims to help the designer in searching for technical solutions and the product architecture by integrating the maintenance issues from the design stage. The goal is to reduce the life-cycle cost (LCC) of the studied system.Design/methodology/approachLiterature indicates that the different approaches used in the design for maintenance (DFM) methods are limited to the simultaneous characterization of the reliability and the maintainability of a multicomponent system as well as the modeling of the dynamic maintenance. This article proposes to go further in the optimization of the product, by simultaneously characterizing the design, in terms of reliability and maintainability, as well as the dynamic planning of the maintenance operations. This combinatorial characterization is performed by a two-level hybrid algorithm based on the genetic algorithms.FindingsThe proposed tool offers, depending on the life-cycle expectation, the desired availability, the desired business model (sales or rental), simulations in terms of the LCCs, and so an optimal product architecture.Research limitations/implicationsIn this article, the term “design” is limited to reliability properties, possible redundancies, component accessibility (maintainability), and levels of monitoring information.Originality/valueThis work is distinguished by the use of a hybrid optimization algorithm (two-level computation) using genetic algorithms. The first level is to identify an optimal design configuration that takes into account the LCC criterion. The second level consists in proposing a dynamic and optimal maintenance plan based on the maintenance-free operating period (MFOP) concept that takes into account certain criteria, such as replacement costs or the reliability of the system.