Abstract
A product development framework called LeanDfX has been conceived at INEGI, aiming at organizing the product design and development process benefitting from lean thinking and DfX paradigms. The design of the metallic structure for an automated guided vehicle (AGV) focusing on its static, dynamic and fatigue characteristics was a recent opportunity to enhance and further develop the framework through the consideration and integration into the process of several existing tools such as FMEA (failure mode and effect analysis), QFD (quality function deployment) or fuzzy logic. This paper describes the integration of those tools in the LeanDfX framework and an application to the design of an AGV structure. The methodology presented involves systematic consideration of a substantial number of design requirements and more detailed product specification characterization. Such a number might be seen as delaying the development process, but the present case study showed that the inverse was true, thanks to the structured systematic approach and timely elimination of less desirable alternatives.

Abstract
This study focuses on the characterization of the production process of a composite sandwich panel for an aircraft structure. Two curing alternatives were compared, namely hot-press and autoclave. A holistic assessment was conducted applying the Total Efficiency Framework, which combines both process efficiency and environmental performance analyses into a single index score to support manufacturing decision. The study provides inventory data, collected at laboratory scale regarding materials, energy consumption, and process operation for composite panel production, which are seldom available. This foreground data was used to quantify the process efficiency, based on lean design tool, and to estimate the potential environmental impacts, using Life Cycle Assessment methodology to determine the eco-efficiency of the production process. The results suggested that the autoclave curing outperforms the hot-press alternative in terms of efficiency, eco-efficiency, and environmental perfor-mance. Regarding the total efficiency index results for maximum productivity, the results show a difference of 12% between the two alternatives, indicating potential competitive advantages in an industrial setting.

Abstract
This paper deals with a multi-objective location-routing problem (MO-LRP) and follows the idea of sectorization to simplify the solution approaches. The MO-LRP consists of sectorization, sub-sectorization, and routing sub-problems. In the sectorization sub-problem, a subset of potential distribution centres (DCs) is opened and a subset of customers is assigned to each of them. Each DC and the customers assigned to it form a sector. Afterward, in the sub-sectorization stage customers of each DC are divided into different sub-sector. Then, in the routing sub-problem, a route is determined and a vehicle is assigned to meet demands. To solve the problem, we design two approaches, which adapt the sectorization, sub-sectorization and routing sub-problems with the non-dominated sorting genetic algorithm (NSGA-II) in two different manners. In the first approach, NSGA-II is used to find non-dominated solutions for all sub-problems, simultaneously. The second one is similar to the first one but it has a hierarchical structure, such that the routing sub-problem is solved with a solver for binary integer programming in MATLAB optimization toolbox after solving sectorization and sub-sectorization sub-problem with NSGA-II. Four benchmarks are used and based on a comparison between the obtained results it is shown that the first approach finds more non-dominated solutions. Therefore, it is concluded that the simultaneous approach is more effective than the hierarchical approach for the defined problem in terms of finding more non-dominated solutions. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021.

Abstract
This paper addresses a new Mixed-model Assembly Line Sequencing Problem in the Footwear industry. This problem emerges in a large company, which benefits from advanced automated stitching systems. However, these systems need to be managed and optimised. Operators with varied abilities operate machines of various types, placed throughout the stitching lines. In different quantities, the components of the various shoe models, placed in boxes, move along the lines in either direction. The work assumes that the associated balancing problems have already been solved, thus solely concentrating on the sequencing procedures to minimise the makespan. An optimisation model is presented, but it has just been useful to structure the problems and test small instances due to the practical problems' complexity and dimension. Consequently, two methods were developed, one based on Variable Neighbourhood Descent, named VND-MSeq, and the other based on Genetic Algorithms, referred to as GA-MSeq. Computational results are included, referring to diverse instances and real large-size problems. These results allow for a comparison of the novel methods and to ascertain their effectiveness. We obtained better solutions than those available in the company.

Abstract
In this study, two novel stochastic models are introduced to solve the dynamic sectorization problem, in which sectors are created by assigning points to service centres. The objective function of the first model is defined based on the equilibration of the distance in the sectors, while in the second one, it is based on the equilibration of the demands of the sectors. Both models impose constraints on assignments and compactness of sectors. In the problem, the coordinates of the points and their demand change over time, hence it is called a dynamic problem. A new solution method is used to solve the models, in which expected values of the coordinates of the points and their demand are assessed by using the Monte Carlo simulation. Thus, the problem is converted into a deterministic one. The linear and deterministic type of the model, which is originally non-linear is implemented in Python's Pulp library and in this way the generated benchmarks are solved. Information about how benchmarks are derived and the obtained solutions are presented.

Abstract
In the last years, the paradigm of the Portuguese footwear industry has improved drastically to become one of the main world players. In fact, a lot has changed, from low-cost mass production to serving clients consisting of small retail chains, where orders are small and models are varied. In order to deal with such modifications, the footwear industry started investing in technological solutions. The industrial case presented in this paper fits that purpose. The goal is to contribute to the solution of complex scheduling problems arising in the new mixed-model flexible automatic stitching systems of an important footwear factory. The project starts by building an optimization model. Although the model has its own usefulness, the CPLEX program is only capable of reaching optimal solutions for small problem instances. Therefore, a recent metaheuristic, the Imperialist Competitive Algorithm (ICA), has been chosen to tackle larger problems. The ICA is capable of finding optimal results for smaller instances and achieving adequate solutions for real problems in short periods of time. Moreover, ICA improves the results obtained so far by the method currently used in the factory.