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Researcher at the Polytechnic University of Bari (Italy), Dept. of Mechanics, Mathematics, and Management. Member of the Board of Professor of the Ph.D. Course on “Mechanical and management engineering”. From 2015, he has been a contract professor of "Industrial Logistic" for a degree course in Engineering of the Logistic system in the food field managed by the Department of Agrarian Sciences, University of Foggia. His research fields include Sustainable Logistic, the simulation models based on Artificial Neural Network, the environmental assessment in workplaces, and the air pollutant dispersion modeling in an indoor industrial work environment. He is the author of more than 40 scientific publications.
Wastewater treatment (WWT) is a foremost challenge for maintaining the health of ecosystems and human beings; the waste products of the water-treatment process can be a problem or an opportunity. The sewage sludge (SS) produced during sewage treatment can be considered a waste to be disposed of in a landfill or as a source for obtaining raw material to be used as a fertilizer, building material, or alternative fuel source suitable for co-incineration in a high-temperature furnace. To this concern, this study’s purpose consisted of developing a decision model, supported by an Artificial Neural Network (ANN model), allowing us to identify the most effective sludge management strategy in economic terms. Consistent with the aim of the work, the suitable SS treatment was identified, selecting for each phase of the SS treatment, an alternative available on the market ensuring energy and/or matter recovery, in line with the circular water value chain. Results show that the ANN model identifies the suitable SS treatments on multiple factors, thus supporting the decision-making and identifying the solution as per user requirements.
Francesco Facchini; Luigi Ranieri; Micaela Vitti. A Neural Network Model for Decision-Making with Application in Sewage Sludge Management. Applied Sciences 2021, 11, 5434 .
AMA StyleFrancesco Facchini, Luigi Ranieri, Micaela Vitti. A Neural Network Model for Decision-Making with Application in Sewage Sludge Management. Applied Sciences. 2021; 11 (12):5434.
Chicago/Turabian StyleFrancesco Facchini; Luigi Ranieri; Micaela Vitti. 2021. "A Neural Network Model for Decision-Making with Application in Sewage Sludge Management." Applied Sciences 11, no. 12: 5434.
The sewage sludges are the byproducts of the wastewater treatment. The new perspective of the wastewater value chain points to a sustainable circular economy approach, where the residual solid material produced by sewage sludge treatments is a resource rather than a waste. A sewage sludge treatment system consists of five main phases; each of them can be performed by different alternative processes. Each process is characterized by its capability to recover energy and/or matter. In this paper, a state of the art of the sludge-to-energy and sludge-to-matter treatments is provided. Then, a scenario analysis is developed to identify suitable sewage sludge treatments plants that best fit the quality and flowrate of sewage sludge to be processed while meeting technological and economic constraints. Based on the scientific literature findings and experts’ opinions, the authors identify a set of reference initial scenarios and the corresponding best treatments’ selection for configuring sewage sludge treatment plants. The scenario analysis reveals a useful reference technical framework when circular economy goals are pursued. The results achieved in all scenarios ensure the potential recovery of matter and/or energy from sewage sludges processes.
Francesco Facchini; Giovanni Mummolo; Micaela Vitti. Scenario Analysis for Selecting Sewage Sludge-to-Energy/Matter Recovery Processes. Energies 2021, 14, 276 .
AMA StyleFrancesco Facchini, Giovanni Mummolo, Micaela Vitti. Scenario Analysis for Selecting Sewage Sludge-to-Energy/Matter Recovery Processes. Energies. 2021; 14 (2):276.
Chicago/Turabian StyleFrancesco Facchini; Giovanni Mummolo; Micaela Vitti. 2021. "Scenario Analysis for Selecting Sewage Sludge-to-Energy/Matter Recovery Processes." Energies 14, no. 2: 276.
The concept of strong sustainability establishes ecosystem conservation as the basis for socioeconomic development. Despite the increase in the number of studies on this subject, the qualitative approach used in studies on strong sustainability makes the introduction of this theme difficult in the industrial context. The absence of a model of sustainability evaluation in manufacturing based on the concept of strong sustainability was the gap identified by this research. The objective of this study was to develop a model that embeds strong sustainability within the sustainability assessment of manufacturing companies. The research used survey methodology to obtain the opinion of experts on the relevance of sustainability metrics. Information collected from experts was used to calculate the weights of indicators and of the participation of each dimension in strong sustainability. The results indicated that strong sustainability consists of 48% of environmental, 29% of social, and 23% of economic factors. The model has been applied in a study of multiple cases in factories in the automotive sector, two in Brazil and two in Italy. The results revealed that the four companies were rated regular in the strong sustainability scale. However, the sustainability performances of the companies showed different patterns over five years. Furthermore, analysis of the individual performance of the dimensions showed that the economic growth of the two Brazilian factories was superior to the socio-environmental development. The result of the Italian units emphasized different priorities. A firm reached the best result in environmental performance and the other one on the social dimension.
Luiz Pinto; Glória Venturini; Salvatore Digiesi; Francesco Facchini; Geraldo Oliveira Neto. Sustainability Assessment in Manufacturing under a Strong Sustainability Perspective—An Ecological Neutrality Initiative. Sustainability 2020, 12, 9232 .
AMA StyleLuiz Pinto, Glória Venturini, Salvatore Digiesi, Francesco Facchini, Geraldo Oliveira Neto. Sustainability Assessment in Manufacturing under a Strong Sustainability Perspective—An Ecological Neutrality Initiative. Sustainability. 2020; 12 (21):9232.
Chicago/Turabian StyleLuiz Pinto; Glória Venturini; Salvatore Digiesi; Francesco Facchini; Geraldo Oliveira Neto. 2020. "Sustainability Assessment in Manufacturing under a Strong Sustainability Perspective—An Ecological Neutrality Initiative." Sustainability 12, no. 21: 9232.
Due to the increasing demand for water supply of urban areas, treatment and supply plants are becoming important to ensure availability and quality of this essential resource for human health. Enabling technologies of Industry 4.0 have the potential to improve performances of treatment plants. In this paper, after reviewing contributions in scientific literature on I4.0 technologies in dam operations, a study carried out on a Brazilian dam is presented and discussed. The main purpose of the study is to evaluate the economic, environmental, and social advantages achieved through the adoption of Artificial Intelligence (AI) in dam operations. Unlike automation that just respond to commands, AI uses a large amount of data training to make computers able to take the best decision. The current study involved a company that managed six reservoirs for treatment systems supplying water to almost ten million people at the metropolitan area of São Paulo City. Results of the study show that AI adoption could lead to economic gain in figures around US$ 51,000.00 per year, as well as less trips between sites and less overtime extra costs on the main operations. Increasing gates maneuvers agility result in significant environmental gains with savings of about 4.32 billion L of water per year, enough to supply 73,000 people. Also, decreasing operational vehicle utilization results in less emissions. Finally, the AI implementation improved the safety of dam operations, resulting in social benefits such as the flood risk mitigation in cities and the health and safety of operators.
Marcos Geraldo Gomes; Victor Hugo Carlquist Da Silva; Luiz Fernando Rodrigues Pinto; Plinio Centoamore; Salvatore Digiesi; Francesco Facchini; Geraldo Cardoso De Oliveira Neto. Economic, Environmental and Social Gains of the Implementation of Artificial Intelligence at Dam Operations toward Industry 4.0 Principles. Sustainability 2020, 12, 3604 .
AMA StyleMarcos Geraldo Gomes, Victor Hugo Carlquist Da Silva, Luiz Fernando Rodrigues Pinto, Plinio Centoamore, Salvatore Digiesi, Francesco Facchini, Geraldo Cardoso De Oliveira Neto. Economic, Environmental and Social Gains of the Implementation of Artificial Intelligence at Dam Operations toward Industry 4.0 Principles. Sustainability. 2020; 12 (9):3604.
Chicago/Turabian StyleMarcos Geraldo Gomes; Victor Hugo Carlquist Da Silva; Luiz Fernando Rodrigues Pinto; Plinio Centoamore; Salvatore Digiesi; Francesco Facchini; Geraldo Cardoso De Oliveira Neto. 2020. "Economic, Environmental and Social Gains of the Implementation of Artificial Intelligence at Dam Operations toward Industry 4.0 Principles." Sustainability 12, no. 9: 3604.
In recent years, the continuous increase of greenhouse gas emissions has led many companies to investigate the activities that have the greatest impact on the environment. Recent studies estimate that around 10% of worldwide CO2 emissions derive from logistical supply chains. The considerable amount of energy required for heating, cooling, and lighting as well as material handling equipment (MHE) in warehouses represents about 20% of the overall logistical costs. The reduction of warehouses’ energy consumption would thus lead to a significant benefit from an environmental point of view. In this context, sustainable strategies allowing the minimization of the cost of energy consumption due to MHE represent a new challenge in warehouse management. Consistent with this purpose, a two-step optimization model based on integer programming is developed in this paper to automatically identify an optimal schedule of the material handling activities of electric mobile MHEs (MMHEs) (i.e., forklifts) in labor-intensive warehouses from profit and sustainability perspectives. The resulting scheduling aims at minimizing the total cost, which is the sum of the penalty cost related to the makespan of the material handling activities and the total electricity cost of charging batteries. The approach ensures that jobs are executed in accordance with priority queuing and that the completion time of battery recharging is minimized. Realistic numerical experiments are conducted to evaluate the effects of integrating the scheduling of electric loads into the scheduling of material handling operations. The obtained results show the effectiveness of the model in identifying the optimal battery-charging schedule for a fleet of electric MMHEs from economic and environmental perspectives simultaneously.
Raffaele Carli; Mariagrazia Dotoli; Salvatore Digiesi; Francesco Facchini; Giorgio Mossa. Sustainable Scheduling of Material Handling Activities in Labor-Intensive Warehouses: A Decision and Control Model. Sustainability 2020, 12, 3111 .
AMA StyleRaffaele Carli, Mariagrazia Dotoli, Salvatore Digiesi, Francesco Facchini, Giorgio Mossa. Sustainable Scheduling of Material Handling Activities in Labor-Intensive Warehouses: A Decision and Control Model. Sustainability. 2020; 12 (8):3111.
Chicago/Turabian StyleRaffaele Carli; Mariagrazia Dotoli; Salvatore Digiesi; Francesco Facchini; Giorgio Mossa. 2020. "Sustainable Scheduling of Material Handling Activities in Labor-Intensive Warehouses: A Decision and Control Model." Sustainability 12, no. 8: 3111.
The goal of Industry 4.0 (I4.0) consists to achieve a high level of operational effectiveness and productivity, as well as a higher level of automation. If on one hand the fourth revolution represents an opportunity for many companies, since the implementation of the smart machines should allow the increasing of the productivity, on the other hand many industries are questioning on the possible implementation of new technologies in their business. Indeed, in most cases can be difficult to evaluate how the I4.0 technologies could impact on the industries’ competitiveness, as well as it can be very complicated to identify the best strategy to be adopted for implementing the I4.0 technologies to specific business cases. Therefore, given the importance of this new industrial (r)evolution and considering the hard prediction of the impact due to adoption of I4.0 technologies in a company, the aim of this paper consists to propose a model to measure the readiness of industries with regard to the implementation of I4.0 paradigms. To develop the proposed model, this work used as basis a structure similar to the one used by Society of Automotive Engineers (SAE) J4000 - Measurement of the implementation of lean manufacturing in an organization - duly modified to encompass the I4.0 principles and concepts. The results shown the effectiveness of the model to support managers for identifying the strategical actions that can be adopted with the scope of improving the company’s readiness level in order to seek the maximum benefits from the adoption of I4.0 paradigms.
Wagner Cezar Lucato; Athos Paulo Tadeu Pacchini; Francesco Facchini; Giovanni Mummolo. Model to evaluate the Industry 4.0 readiness degree in Industrial Companies. IFAC-PapersOnLine 2019, 52, 1808 -1813.
AMA StyleWagner Cezar Lucato, Athos Paulo Tadeu Pacchini, Francesco Facchini, Giovanni Mummolo. Model to evaluate the Industry 4.0 readiness degree in Industrial Companies. IFAC-PapersOnLine. 2019; 52 (13):1808-1813.
Chicago/Turabian StyleWagner Cezar Lucato; Athos Paulo Tadeu Pacchini; Francesco Facchini; Giovanni Mummolo. 2019. "Model to evaluate the Industry 4.0 readiness degree in Industrial Companies." IFAC-PapersOnLine 52, no. 13: 1808-1813.
The adoption of Industry 4.0 technologies has become particularly important nowadays for companies in order to optimize their production processes and organizational structures. However, companies sometimes find it difficult to develop a strategic plan that innovates their current business model and develops an Industry 4.0 vision. To overcome the growing uncertainty and dissatisfaction in implementing Industry 4.0, new methods and tools that specifically address dedicated companies’ areas, such as logistics, supply chain management, and manufacturing processes, were developed to provide guidance and support to align companies’ business strategies and operations. In particular, this paper develops and presents the application of a maturity model for Logistics 4.0, focusing on the specific applications of Industry 4.0 in the area of logistics. To do so, extant maturity models, linked to the context of Industry 4.0 implementation in logistics processes, were examined in the main scientific research. Afterward, two companies have been investigated through a survey, built around three fundamental macro-aspects, named (i) the propensity of the company towards Industry 4.0 and Logistics 4.0, (ii) the current use of technologies in the logistics process, and (iii) the investments’ level towards Industry 4.0 technologies for a Logistics 4.0 transition. By doing so, a maturity model for Logistics 4.0 emerged as the main result of our research, able to identify the level of maturity of companies in implementing the Industry 4.0 technologies in their logistics processes. Moreover, the model highlighted the strengths and weaknesses of the two investigated companies with respect to the transition towards Logistics 4.0. On the basis of the obtained results, a roadmap for enhancing the digitalization of logistics processes, according to the principles of the fourth industrial revolution, was finally proposed.
Francesco Facchini; Joanna Oleśków-Szłapka; Luigi Ranieri; Andrea Urbinati. A Maturity Model for Logistics 4.0: An Empirical Analysis and a Roadmap for Future Research. Sustainability 2019, 12, 86 .
AMA StyleFrancesco Facchini, Joanna Oleśków-Szłapka, Luigi Ranieri, Andrea Urbinati. A Maturity Model for Logistics 4.0: An Empirical Analysis and a Roadmap for Future Research. Sustainability. 2019; 12 (1):86.
Chicago/Turabian StyleFrancesco Facchini; Joanna Oleśków-Szłapka; Luigi Ranieri; Andrea Urbinati. 2019. "A Maturity Model for Logistics 4.0: An Empirical Analysis and a Roadmap for Future Research." Sustainability 12, no. 1: 86.
Work-related Musculoskeletal Disorders (WMSDs) have a multifactorial origin: work-related risk factors and individual factors (age, sex, anthropometric characteristics). The purpose of the current study was the risk assessment of upper limb-WMSDs of workers engaged in tasks of anchovies filleting and packaging in a fish industry considering the ergonomic evaluation and the painful symptomatology complained by employees of different age. The activities were analysed by the American Conference Governmental Industrial Hygienists (ACGIH) method, the Strain Index (SI) method, the Rapid Upper Limb Assessment (RULA) method as well as the Occupational Rapid Assessment (OCRA) checklist. Workers answered the Italian version of the Nordic Musculoskeletal Questionnaire (NMQ). The ACGIH method showed that packaging needs greater protection, while filleting requires ergonomic interventions. The SI showed a significant increasing risk for both tasks. The final score identified by RULA, for tasks of fish filleting and packaging, suggested a medium level of action, therefore were required additional observations. By OCRA checklist the final score for both tasks denoted a high risk.
Graziana Intranuovo; Luigi De Maria; Francesco Facchini; Armenise Giustiniano; Antonio Caputi; Francesco Birtolo; Luigi Vimercati. Risk assessment of upper limbs repetitive movements in a fish industry. BMC Research Notes 2019, 12, 1 -7.
AMA StyleGraziana Intranuovo, Luigi De Maria, Francesco Facchini, Armenise Giustiniano, Antonio Caputi, Francesco Birtolo, Luigi Vimercati. Risk assessment of upper limbs repetitive movements in a fish industry. BMC Research Notes. 2019; 12 (1):1-7.
Chicago/Turabian StyleGraziana Intranuovo; Luigi De Maria; Francesco Facchini; Armenise Giustiniano; Antonio Caputi; Francesco Birtolo; Luigi Vimercati. 2019. "Risk assessment of upper limbs repetitive movements in a fish industry." BMC Research Notes 12, no. 1: 1-7.
In recent years the maritime freight transport has rapidly increased, causing congestion in many port areas. In some cases, in order to improve the capacity and the reliability of the temporary storage, a solution, recommended by industry officials, is the expansion of the terminal capacity. When this solution is not available, the ‘dry port’ area represents an effective alternative. The adoption of a dry port, if on one hand leads to benefits on terminal congestion, on the other hand requires resources and investments due to the transport of the container from port to dry port and vice versa. In the evaluation of the strategy to be adopted different aspects shall be evaluated to estimate time required for the container handling inside and outside the terminal on the basis of the congestion degree. In this paper, to support decision makers in identifying the best strategy to be adopted, a mathematical model allowing to identify the number of containers to be stocked in port and/or in dry port is defined considering the intra-/inter-terminal handling of the containers, in order to minimize the overall running costs and of the carbon footprint. The model, based on a computational algorithm for non-linear programming, is able to provide the number of containers to be stocked in port and/or in dry port, ensuring an effective strategy dependent on ‘road’ and ‘non-road' material handling equipment adopted, on the number and size of containers, as well as on the distance from port to dry port. Results obtained from numerical experiments show that, on the basis of the running cost and the carbon footprint of the container handling activities, it is possible to identify the most economic and eco-friendly container handling configuration. The case study of the Port of Bari (Italy) is investigated. In this case, given the overall number of containers to be stocked and the distance between port and dry port, the solutions found by the model identify a configuration able to ensure a reduction of 7% and 11% of the running cost and of the carbon footprint, respectively, when compared to the configuration in which all containers are stored in the port.
F. Facchini; S. Digiesi; G. Mossa. Optimal dry port configuration for container terminals: A non-linear model for sustainable decision making. International Journal of Production Economics 2019, 219, 164 -178.
AMA StyleF. Facchini, S. Digiesi, G. Mossa. Optimal dry port configuration for container terminals: A non-linear model for sustainable decision making. International Journal of Production Economics. 2019; 219 ():164-178.
Chicago/Turabian StyleF. Facchini; S. Digiesi; G. Mossa. 2019. "Optimal dry port configuration for container terminals: A non-linear model for sustainable decision making." International Journal of Production Economics 219, no. : 164-178.
In last years, the market penetration of the Additive Manufacturing (AM) processes in aerospace industry is continuously growing, if on one hand the advantages of AM process are indisputable under technological perspective, on the other hand the costs due to AM process are quite variable and, in many cases, identifying a preliminary cost estimation is very difficult. Indeed, engineering and manufacturing costs are strongly dependent by complexity and by specificity of the part to be manufactured. The purpose of this paper consists in developing a cost model based on a computational algorithm that allows to quickly asses the overall cost due to design and production of part by means of one of the most recently AM technology (Wire+Arc AM). Consistently, the model is adopted for evaluate and compare the process costs due to production of a batch of aerospace parts, adopting both Wire+Arc AM (WAAM) and traditional machining technologies. The results of the experimental study conducted, show that the most cost-effective technology, between WAAM and traditional machining, is strongly depending on batch size to be manufactured.
F. Facchini; A. De Chirico; G. Mummolo. Comparative Cost Evaluation of Material Removal Process and Additive Manufacturing in Aerospace Industry. Recent Developments in Stochastic Methods and Applications 2019, 47 -59.
AMA StyleF. Facchini, A. De Chirico, G. Mummolo. Comparative Cost Evaluation of Material Removal Process and Additive Manufacturing in Aerospace Industry. Recent Developments in Stochastic Methods and Applications. 2019; ():47-59.
Chicago/Turabian StyleF. Facchini; A. De Chirico; G. Mummolo. 2019. "Comparative Cost Evaluation of Material Removal Process and Additive Manufacturing in Aerospace Industry." Recent Developments in Stochastic Methods and Applications , no. : 47-59.
In this paper, a tree-like structure of Key Performance Indicators (KPIs) is proposed to describe the Performance Measurement System (PMS) of a lean production system. The KPIs and their supporting measurement elements are identified and categorized in a multi-level hierarchy designed to give answers at strategic, tactical, and operational level. Examples of the dependencies between high-level decision variables, KPIs, and their measurement elements are presented with reference to the Bosch Production System (BPS) of a multinational manufacturer leader in the automotive industry. Moreover, the role and the impact of the KPIs selection in supporting, addressing, and evaluating the implementation of smart manufacturing projects in the fourth industrial revolution (I4.0) is shown.
G. Ante; F. Facchini; G. Mossa; S. Digiesi. Developing a key performance indicators tree for lean and smart production systems. IFAC-PapersOnLine 2018, 51, 13 -18.
AMA StyleG. Ante, F. Facchini, G. Mossa, S. Digiesi. Developing a key performance indicators tree for lean and smart production systems. IFAC-PapersOnLine. 2018; 51 (11):13-18.
Chicago/Turabian StyleG. Ante; F. Facchini; G. Mossa; S. Digiesi. 2018. "Developing a key performance indicators tree for lean and smart production systems." IFAC-PapersOnLine 51, no. 11: 13-18.
Salvatore Digiesi Digiesi; Francesco Facchini; Giorgio Mossa; Giovanni Mummolo. Minimizing and Balancing Ergonomic Risk of Workers of an Assembly Line by Job Rotation: a MINLP Model. International Journal of Industrial Engineering and Management 2018, 9, 129 -138.
AMA StyleSalvatore Digiesi Digiesi, Francesco Facchini, Giorgio Mossa, Giovanni Mummolo. Minimizing and Balancing Ergonomic Risk of Workers of an Assembly Line by Job Rotation: a MINLP Model. International Journal of Industrial Engineering and Management. 2018; 9 (3):129-138.
Chicago/Turabian StyleSalvatore Digiesi Digiesi; Francesco Facchini; Giorgio Mossa; Giovanni Mummolo. 2018. "Minimizing and Balancing Ergonomic Risk of Workers of an Assembly Line by Job Rotation: a MINLP Model." International Journal of Industrial Engineering and Management 9, no. 3: 129-138.
This study aims to analyze the optimal warehouse layout for agricultural and food collecting centers that help small–medium farms to trade in the short food supply chain, by choosing among longitudinal, transversal, and fishbone layout. The developed model allows for the identification of the warehouse ensuring the least impact through inbound material handling, under both an economic and an environmental perspective. The analysis was carried out by using an analytical model to minimize the travelling time of the goods from picking to delivery area. The model considers the different turnover index from which four hypotheses were formulated to implement the results. The Carbon Footprint (CF) and Management Costs (MCs) were calculated by the picking time performance. Findings: Results show that the optimal warehouse layout can be identified after a careful consideration of the turnover indexes. However, for seasonality, the optimal design might be missed across the seasons. Practical implications: the analysis hereby presented is related to those collecting centers aiming to gather conspicuous amounts of seasonal food.
Francesco Facchini; Gianluigi De Pascale; Nicola Faccilongo. Pallet Picking Strategy in Food Collecting Center. Applied Sciences 2018, 8, 1503 .
AMA StyleFrancesco Facchini, Gianluigi De Pascale, Nicola Faccilongo. Pallet Picking Strategy in Food Collecting Center. Applied Sciences. 2018; 8 (9):1503.
Chicago/Turabian StyleFrancesco Facchini; Gianluigi De Pascale; Nicola Faccilongo. 2018. "Pallet Picking Strategy in Food Collecting Center." Applied Sciences 8, no. 9: 1503.
Keywords Sustainable logistics; Container terminal; Dry port; Carbon footprint; Material handling
Francesco Facchini; Francesco Boenzi; Salvatore Digiesi; Giovanni Mummolo. A model-based Decision Support System for multiple container terminals hub management. Production 2018, 28, 1 .
AMA StyleFrancesco Facchini, Francesco Boenzi, Salvatore Digiesi, Giovanni Mummolo. A model-based Decision Support System for multiple container terminals hub management. Production. 2018; 28 ():1.
Chicago/Turabian StyleFrancesco Facchini; Francesco Boenzi; Salvatore Digiesi; Giovanni Mummolo. 2018. "A model-based Decision Support System for multiple container terminals hub management." Production 28, no. : 1.
European maritime ports play a fundamental role in maritime transport of containers. One of the more common problem of these ports is related to the berth congestion; in these cases solutions recommended by industry officials is to expand terminal capacity; when this solution is not available the ‘Dry port’ represents an effective alternative. The dry port is an external area directly connected with one or several seaports by rail and/or road transport. The adoption of a dry port leads to benefits on terminal congestion; depending on the distance of the dry port from the port as well as on the amount of containers stocked in the dry port area, the transport of the containers from port to dry port requires resources. In this paper, a model that allows optimizing the inter-/ intra-terminal flows of the containers is proposed. The model aims at the minimization of costs and environmental impact due to the handling of containers. A full case study concerning a multi-terminal maritime system located in Port of Bari is developed.
F. Facchini; S. Digiesi; L. Ranieri. A MODEL FOR ECONOMIC AND ENVIRONMENTAL EVALUATION OF INTER-/INTRA-TERMINAL CONTAINERS FLOWS. DEStech Transactions on Engineering and Technology Research 2018, 1 .
AMA StyleF. Facchini, S. Digiesi, L. Ranieri. A MODEL FOR ECONOMIC AND ENVIRONMENTAL EVALUATION OF INTER-/INTRA-TERMINAL CONTAINERS FLOWS. DEStech Transactions on Engineering and Technology Research. 2018; (icpr):1.
Chicago/Turabian StyleF. Facchini; S. Digiesi; L. Ranieri. 2018. "A MODEL FOR ECONOMIC AND ENVIRONMENTAL EVALUATION OF INTER-/INTRA-TERMINAL CONTAINERS FLOWS." DEStech Transactions on Engineering and Technology Research , no. icpr: 1.
ANN Modelling to Optimize Manufacturing Process | InTechOpen, Published on: 2018-02-28. Authors: Luigi Alberto Ciro De Filippis, Livia Maria Serio, Francesco Facchini, et
Luigi Alberto Ciro De Filippis; Livia Maria Serio; Francesco Facchini; Francesco Facchini And Giovanni Mummolo. ANN Modelling to Optimize Manufacturing Process. Advanced Applications for Artificial Neural Networks 2018, 1 .
AMA StyleLuigi Alberto Ciro De Filippis, Livia Maria Serio, Francesco Facchini, Francesco Facchini And Giovanni Mummolo. ANN Modelling to Optimize Manufacturing Process. Advanced Applications for Artificial Neural Networks. 2018; ():1.
Chicago/Turabian StyleLuigi Alberto Ciro De Filippis; Livia Maria Serio; Francesco Facchini; Francesco Facchini And Giovanni Mummolo. 2018. "ANN Modelling to Optimize Manufacturing Process." Advanced Applications for Artificial Neural Networks , no. : 1.
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.
Francesco Boenzi; Salvatore Digiesi; Francesco Facchini; Giovanni Mummmolo. Electric and LPG forklifts GHG assessment in material handling activities in actual operational conditions. 2017 IEEE International Conference on Service Operations and Logistics, and Informatics (SOLI) 2017, 127 -132.
AMA StyleFrancesco Boenzi, Salvatore Digiesi, Francesco Facchini, Giovanni Mummmolo. Electric and LPG forklifts GHG assessment in material handling activities in actual operational conditions. 2017 IEEE International Conference on Service Operations and Logistics, and Informatics (SOLI). 2017; ():127-132.
Chicago/Turabian StyleFrancesco Boenzi; Salvatore Digiesi; Francesco Facchini; Giovanni Mummmolo. 2017. "Electric and LPG forklifts GHG assessment in material handling activities in actual operational conditions." 2017 IEEE International Conference on Service Operations and Logistics, and Informatics (SOLI) , no. : 127-132.
Purpose: The aim of this study is to identify the best Material Handling Equipment (MHE) to minimize the carbon footprint of inbound logistic activities, based on the type of the warehouse (layout, facilities and order-picking strategy) as well as the weight of the loads to be handled.Design/methodology/approach: A model to select the best environmental MHE for inbound logistic activities has been developed. Environmental performance of the MHE has been evaluated in terms of carbon Footprint (CF). The model is tested with a tool adopting a VBA macro as well as a simulation software allowing the evaluation of energy and time required by the forklift in each phase of the material handling cycle: picking, sorting and storing of the items.Findings: Nowadays, it is not possible to identify ‘a priori’ a particular engine equipped forklift performing better than others under an environmental perspective. Consistently, the application of the developed model allows to identify the best MHE tailored to each case analyzed. Originality/value: This work gives a contribution to the disagreement between environmental performances of forklifts equipped with different engines. The developed model can be considered a valid support for decision makers to identify the best MHE minimizing the carbon footprint of inbound logistic activities.
Francesco Facchini; Giovanni Mummolo; Giorgio Mossa; Salvatore Digiesi; Francesco Boenzi; Rossella Verriello. Minimizing the carbon footprint of material handling equipment: Comparison of electric and LPG forklifts. Journal of Industrial Engineering and Management 2016, 9, 1035 .
AMA StyleFrancesco Facchini, Giovanni Mummolo, Giorgio Mossa, Salvatore Digiesi, Francesco Boenzi, Rossella Verriello. Minimizing the carbon footprint of material handling equipment: Comparison of electric and LPG forklifts. Journal of Industrial Engineering and Management. 2016; 9 (5):1035.
Chicago/Turabian StyleFrancesco Facchini; Giovanni Mummolo; Giorgio Mossa; Salvatore Digiesi; Francesco Boenzi; Rossella Verriello. 2016. "Minimizing the carbon footprint of material handling equipment: Comparison of electric and LPG forklifts." Journal of Industrial Engineering and Management 9, no. 5: 1035.
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.
Luigi Alberto Ciro De Filippis; Livia Maria Serio; Francesco Facchini; Giovanni Mummolo; Antonio Domenico Ludovico. Prediction of the Vickers Microhardness and Ultimate Tensile Strength of AA5754 H111 Friction Stir Welding Butt Joints Using Artificial Neural Network. Materials 2016, 9, 915 .
AMA StyleLuigi Alberto Ciro De Filippis, Livia Maria Serio, Francesco Facchini, Giovanni Mummolo, Antonio Domenico Ludovico. Prediction of the Vickers Microhardness and Ultimate Tensile Strength of AA5754 H111 Friction Stir Welding Butt Joints Using Artificial Neural Network. Materials. 2016; 9 (11):915.
Chicago/Turabian StyleLuigi Alberto Ciro De Filippis; Livia Maria Serio; Francesco Facchini; Giovanni Mummolo; Antonio Domenico Ludovico. 2016. "Prediction of the Vickers Microhardness and Ultimate Tensile Strength of AA5754 H111 Friction Stir Welding Butt Joints Using Artificial Neural Network." Materials 9, no. 11: 915.
Francesco Boenzi; Salvatore Digiesi; Francesco Facchini; Giorgio Mossa; Giovanni Mummolo. Greening Activities in Warehouses: A Model for Identifying Sustainable Strategies in Material Handling. Proceedings of the 27th International DAAAM Symposium 2016 2016, 1, 980 -988.
AMA StyleFrancesco Boenzi, Salvatore Digiesi, Francesco Facchini, Giorgio Mossa, Giovanni Mummolo. Greening Activities in Warehouses: A Model for Identifying Sustainable Strategies in Material Handling. Proceedings of the 27th International DAAAM Symposium 2016. 2016; 1 ():980-988.
Chicago/Turabian StyleFrancesco Boenzi; Salvatore Digiesi; Francesco Facchini; Giorgio Mossa; Giovanni Mummolo. 2016. "Greening Activities in Warehouses: A Model for Identifying Sustainable Strategies in Material Handling." Proceedings of the 27th International DAAAM Symposium 2016 1, no. : 980-988.