Francesco Facchini

Prediction of the Vickers Microhardness and Ultimate Tensile Strength of AA5754 H111 Friction Stir Welding Butt Joints Using Artificial Neural Network

A simulation model was developed for the monitoring, controlling and optimization of the Friction Stir Welding (FSW) process. This approach, using the FSW technique, allows identifying the correlation between the process parameters (input variable) and the mechanical properties (output responses) of the welded AA5754 H111 aluminum plates. The optimization of technological parameters is a basic requirement for increasing the seam quality, since it promotes a stable and defect-free process. Both the tool rotation and the travel speed, the position of the samples extracted from the weld bead and the thermal data, detected with thermographic techniques for on-line control of the joints, were varied to build the experimental plans. The quality of joints was evaluated through destructive and non-destructive tests (visual tests, macro graphic analysis, tensile tests, indentation Vickers hardness tests and t thermographic controls). The simulation model was based on the adoption of the Artificial Neural Networks (ANNs) characterized by back-propagation learning algorithm with different types of architecture, which were able to predict with good reliability the FSW process parameters for the welding of the AA5754 H111 aluminum plates in Butt-Joint configuration.

A model-based Decision Support System for multiple container terminals hub management

Paper aims: To develop a model-based Decision Support System (DSS) that allows identifying the best strategy of the inter-/intra-terminal flows of the containers in order to increasing the performance of the hub under economic and environmental perspective. Originality: The adoption of a dry port can effectively solve the congestion problem of a terminal only if an integrated sustainable solution (dry port location and container strategy storage) is identified. Research method: The model is based on a heuristic computational algorithm for non-linear programming. Main findings: The application of DSS to a full-scale numerical case show the model capabilities in identifying the optimal logistic strategies ensuring a low CF and in optimizing the cost due to transport activities. Implications for theory and practice: It is possible to identify different strategies allowing to obtain an eco-friendly solution reducing, at same time, the costs for a given number of containers to be handled.

Electric and LPG forklifts GHG assessment in material handling activities in actual operational conditions

The aim of the present paper is to give a contribution on the debate regarding the environmental impact, in terms of GHG emissions, of material handling activities performed with LPG or electrically powered forklift trucks. A model of the operations performed by the trucks, based upon a decomposition approach into elementary steps, is illustrated and data drawn from technical sheets are employed, in order to evaluate the required times and the associated energy consumption. Recharging cycles of batteries and the emissions associated with recycle / reconditioning process at their end of life are also taken into account. Comparisons are then carried out for a reference storage of units. The results obtained lead to conclude that electrically powered trucks are always advantageous over LPG powered ones with the same rated load capacity in the examined range and under the stated assumptions, both under the environmental aspect and the economic point of view, with the assumed figures of the cost of energy.

A Nonlinear Integer Programming Model for Warehousing Sustainable Logistics

The warehouse management problem is a critical issue in Operation Management. In modern competitive market, many firms are automating their basic warehouse activities in order to be cost effective. However, traditional mechanized warehousing systems (MWS) still represent the 75 % of the overall installations. In MWS, forklifts are adopted for the load handling. The adoption of efficient “internal logistic strategies” could help in reducing time required and costs of warehousing activities. In recent years, many firms adopted green supply chain practices (GSCP) in order to improve their environmental performances while also achieving economic goals (Wu et al. 2015). Furthermore, in planning Smart City logistics, warehousing in port or railway station storage areas is being receiving wide attention since they contribute effectively to a sustainable development of modern cities. Under this perspective, an optimal “internal logistic strategy” allowing to jointly minimizing jointly costs and environmental impacts of warehousing activities has to be adopted. The aim of this study is to develop a Nonlinear Integer Programming Model to solve a storage location assignment problem (SLAP) for optimizing the environmental performance of the internal logistic activities in a warehouse. Suitable storage strategies are identified on the basis of the type of the forklifts adopted (internal combustion or electric engine equipped) as well as the sizes and the weight of the loads to be handled.