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Life Cycle Assessment quantifies the multi-dimensional impact of goods and services and can be handled by Multi-Criteria Decision Analysis. In Multi-Criteria Decision Analysis, Robust Ordinal Regression manages all the compatible preference functions at once when assessing a set of alternatives and a group of preferences on reference alternatives. Robust Ordinal Regression is thus a versatile method of reducing the cognitive effort required by decision makers for eliciting their preference structures in Life Cycle Assessment, although it does not directly operate on noisy alternatives and requires Stochastic Multicriteria Acceptability Analysis to deal with such scenarios. We propose integrating a dimensionality reduction technique, Principal Component Analysis, and Robust Ordinal Regression methods, to reduce the problem dimensionality and ensure the actual problem features are considered. A generated dataset, a dataset from literature and a Life Cycle Assessment case study are used to test the effectiveness of the proposed methods.
Elia Balugani; Francesco Lolli; Martina Pini; Anna Maria Ferrari; Paolo Neri; Rita Gamberini; Bianca Rimini. Dimensionality reduced robust ordinal regression applied to life cycle assessment. Expert Systems with Applications 2021, 178, 115021 .
AMA StyleElia Balugani, Francesco Lolli, Martina Pini, Anna Maria Ferrari, Paolo Neri, Rita Gamberini, Bianca Rimini. Dimensionality reduced robust ordinal regression applied to life cycle assessment. Expert Systems with Applications. 2021; 178 ():115021.
Chicago/Turabian StyleElia Balugani; Francesco Lolli; Martina Pini; Anna Maria Ferrari; Paolo Neri; Rita Gamberini; Bianca Rimini. 2021. "Dimensionality reduced robust ordinal regression applied to life cycle assessment." Expert Systems with Applications 178, no. : 115021.
In this study two phase change materials (PCMs) mixed with sand were evaluated for distributed latent heat thermal energy storage (LHTES) coupled with a novel Flat-Panel ground heat exchanger (GHE) for shallow geothermal applications. N-Octadecane and a commercial paraffin-based PCM were mixed (30% v/v) separately with sand, which is commonly used as backfilling material for GHE. Both two mixtures underwent 16 thermal cycles and specimen’s temperatures and their variation over time were analyzed to evaluate phase change stability and supercooling. Grain size laser diffraction and pore analysis were performed together with optical microscopy, environmental scanning electron microscopy coupled with X-Ray spectrometry (ESEM-EDS) and Fourier transform infrared spectroscopy (FTIR) analysis to evaluate PCMs-sand dynamic interaction over time and temperature. Results shown that sand addition halves n-Octadecane phase change time, although leading to a limited supercooling equal to 1 °C. Sand addition to commercial PCM leaded to a similar increasing in heat transfer, however in absence of supercooling phenomena. These performances were constant through 16 thermal cycles. Therefore, PCMs mixing in sand as mixture for GHEs backfilling material can be considered a strategy to enhance thermal storage of backfilling material, by increasing the underground thermal energy storage and then the exploitation carried out by shallow geothermal applications.
Silvia Barbi; Francesco Barbieri; Simona Marinelli; Bianca Rimini; Sebastiano Merchiori; Barbara Larwa; Michele Bottarelli; Monia Montorsi. Phase change material-sand mixtures for distributed latent heat thermal energy storage: Interaction and performance analysis. Renewable Energy 2021, 169, 1066 -1076.
AMA StyleSilvia Barbi, Francesco Barbieri, Simona Marinelli, Bianca Rimini, Sebastiano Merchiori, Barbara Larwa, Michele Bottarelli, Monia Montorsi. Phase change material-sand mixtures for distributed latent heat thermal energy storage: Interaction and performance analysis. Renewable Energy. 2021; 169 ():1066-1076.
Chicago/Turabian StyleSilvia Barbi; Francesco Barbieri; Simona Marinelli; Bianca Rimini; Sebastiano Merchiori; Barbara Larwa; Michele Bottarelli; Monia Montorsi. 2021. "Phase change material-sand mixtures for distributed latent heat thermal energy storage: Interaction and performance analysis." Renewable Energy 169, no. : 1066-1076.
Plastic materials account for about 20% of waste electrical and electronic equipment (WEEE). The recycling of this plastic fraction is a complex issue, heavily conditioned by the content of harmful additives, such as brominated flame retardants. Thus, the management and reprocessing of WEEE plastics pose environmental and human health concerns, mainly in developing countries, where informal recycling and disposal are practiced. The objective of this study was twofold. Firstly, it aimed to investigate some of the available options described in the literature for the re-use of WEEE plastic scraps in construction materials, a promising recycling route in the developing countries. Moreover, it presents an evaluation of the impact of these available end-of-life scenarios on the environment by means of the life cycle assessment (LCA) approach. In order to consider worker health and human and ecological risks, the LCA analysis focuses on ecotoxicity more than on climate change. The LCA evaluation confirmed that the plastic re-use in the construction sector has a lower toxicity impact on the environment and human health than common landfilling and incineration practices. It also shows that the unregulated handling and dismantling activities, as well as the re-use practices, contribute significantly to the impact of WEEE plastic treatments.
Maria Angela Butturi; Simona Marinelli; Rita Gamberini; Bianca Rimini. Ecotoxicity of Plastics from Informal Waste Electric and Electronic Treatment and Recycling. Toxics 2020, 8, 99 .
AMA StyleMaria Angela Butturi, Simona Marinelli, Rita Gamberini, Bianca Rimini. Ecotoxicity of Plastics from Informal Waste Electric and Electronic Treatment and Recycling. Toxics. 2020; 8 (4):99.
Chicago/Turabian StyleMaria Angela Butturi; Simona Marinelli; Rita Gamberini; Bianca Rimini. 2020. "Ecotoxicity of Plastics from Informal Waste Electric and Electronic Treatment and Recycling." Toxics 8, no. 4: 99.
The purpose of this article is to identify barriers, drivers, and the structure of the relationships that support industrial symbiosis initiatives in a network of Brazilian manufacturing companies. Two steelmaking plants are the anchor tenants of the network comprising a cement manufacturer, a thermoelectric generation plant, a lead ingots manufacturer, a zinc ingots manufacturer and refractory liner manufacturer, totaling eight relationships. The companies mutually exchange approximately 300,000 tons of by-products per year, comprising coal ash, mill scale, electric arc furnace dust, steam, zinc sludge, lead sludge, and refractory lining leftover, totaling eight dyadic or triadic relationships. The results of the study show that in three relationships, economic barriers exist (excessive processing or logistic cost). In four, internal barriers exist (risk of discontinuity and lack of research). In five, a technical barrier exists, the imbalance between generation and consumption. In seven, the drivers are cost reduction, new products or sources of revenue, and legal requirements. Environmental drivers (increasing the life of deposits or landfills) are present in five relationships. As for the structure, three relationships are one-way, whereas five are closed-loop, that is, involving direct and reverse transfers among partners.
Miguel Afonso Sellitto; Fábio Kazuhiro Murakami; Maria Angela Butturi; Simona Marinelli; Nelson Kadel Jr.; Bianca Rimini. Barriers, drivers, and relationships in industrial symbiosis of a network of Brazilian manufacturing companies. Sustainable Production and Consumption 2020, 26, 443 -454.
AMA StyleMiguel Afonso Sellitto, Fábio Kazuhiro Murakami, Maria Angela Butturi, Simona Marinelli, Nelson Kadel Jr., Bianca Rimini. Barriers, drivers, and relationships in industrial symbiosis of a network of Brazilian manufacturing companies. Sustainable Production and Consumption. 2020; 26 ():443-454.
Chicago/Turabian StyleMiguel Afonso Sellitto; Fábio Kazuhiro Murakami; Maria Angela Butturi; Simona Marinelli; Nelson Kadel Jr.; Bianca Rimini. 2020. "Barriers, drivers, and relationships in industrial symbiosis of a network of Brazilian manufacturing companies." Sustainable Production and Consumption 26, no. : 443-454.
The concepts of resilience and sustainability appear multi-dimensional and correlated, depending on the context. Operational sustainability practices can enhance the resilience of a firm, and support its growth. This study aims at analyzing the impact of a sustainability strategy, measured by means of a sustainability maturity index (SMI), on the financial performance of a company. Since the SMI is strictly correlated to resilience capabilities, the performed analysis represents a first level integration of the sustainability and resilience indicators in a common framework. A data sample from 53 organizations was collected through structured interviews and analyzed to identify possible relationships between the SMI and the financial performance indexes. The analysis does not support commonly reported arguments: we show that profitability does not show a significant relationship with sustainable strategic intent. Interestingly, firm country of origin, size of the organization, and market focus, likewise, do not have a significant relationship with SMI. Arguably, multi-dimensional company performance, including both financial and non-financial measures, should be considered to assess the impact of sustainability practices. Moreover, further investigations are needed to capture firms’ nonfinancial indicators of performance that are related to sustainability and resilience, for building up a unified framework enabling trade-off analysis.
Elia Balugani; Maria Angela Butturi; Delroy Chevers; David Parker; Bianca Rimini. Empirical Evaluation of the Impact of Resilience and Sustainability on Firms’ Performance. Sustainability 2020, 12, 1742 .
AMA StyleElia Balugani, Maria Angela Butturi, Delroy Chevers, David Parker, Bianca Rimini. Empirical Evaluation of the Impact of Resilience and Sustainability on Firms’ Performance. Sustainability. 2020; 12 (5):1742.
Chicago/Turabian StyleElia Balugani; Maria Angela Butturi; Delroy Chevers; David Parker; Bianca Rimini. 2020. "Empirical Evaluation of the Impact of Resilience and Sustainability on Firms’ Performance." Sustainability 12, no. 5: 1742.
Complexity measurement based on the Shannon information entropy is widely used to evaluate variety and uncertainty in supply chains. However, how to use a complexity measurement to support control actions is still an open issue. This article presents a method to calculate the relative complexity, i.e., the relationship between the current and the maximum possible complexity in a Supply Chain. The method relies on unexpected information requirements to mitigate uncertainty. The article studies two real-world Supply Chains of the footwear industry, one competing by cost and quality, the other by flexibility, dependability, and innovation. The second is twice as complex as the first, showing that competitive priorities influence the complexity of the system and that lower complexity does not ensure competitivity.
Miguel A. Sellitto; Francesco Lolli; Bianca Rimini; Elia Balugani. Complexity Measurement in Two Supply Chains with Different Competitive Priorities. IFAC-PapersOnLine 2019, 52, 1699 -1704.
AMA StyleMiguel A. Sellitto, Francesco Lolli, Bianca Rimini, Elia Balugani. Complexity Measurement in Two Supply Chains with Different Competitive Priorities. IFAC-PapersOnLine. 2019; 52 (13):1699-1704.
Chicago/Turabian StyleMiguel A. Sellitto; Francesco Lolli; Bianca Rimini; Elia Balugani. 2019. "Complexity Measurement in Two Supply Chains with Different Competitive Priorities." IFAC-PapersOnLine 52, no. 13: 1699-1704.
The training of suppliers and inbound quality inspectors is a common strategy to increase the quality performance of the supply chain but, under budget constraints, these actors compete for a limited amount of training hours. The proposed model aims to allocate the available training hours so as to minimise a total quality cost function composed of prevention, appraisal, and failure costs; it also sets the inspection rates defining the inspection policies assigned to suppliers. The relationship between decision variables and costs is expressed through organisational and individual learning-forgetting curves, for suppliers and quality inspectors respectively, and the effect of the training hours on quality improvement is measured in terms of failure rates. To the best of our knowledge, a total quality cost model with such decision variables is new in the related literature, as it is a model including both organisational and individual learning-forgetting phenomena. A nonlinear optimisation approach was adopted to solve this complex problem. The experimental section includes a decision trees analysis of simplified scenarios in order to interpret the model functioning, as well as a complex numerical example to extrapolate managerial insights.
Francesco Lolli; Elia Balugani; Rita Gamberini; Bianca Rimini. Quality cost-based allocation of training hours using learning-forgetting curves. Computers & Industrial Engineering 2019, 131, 552 -564.
AMA StyleFrancesco Lolli, Elia Balugani, Rita Gamberini, Bianca Rimini. Quality cost-based allocation of training hours using learning-forgetting curves. Computers & Industrial Engineering. 2019; 131 ():552-564.
Chicago/Turabian StyleFrancesco Lolli; Elia Balugani; Rita Gamberini; Bianca Rimini. 2019. "Quality cost-based allocation of training hours using learning-forgetting curves." Computers & Industrial Engineering 131, no. : 552-564.
Today, almost everybody has a smartphone and applications have been developed to help users to take decisions (e.g. which hotel to choose, which museum to visit, etc). In order to improve the recommendations of the mobile application, it is crucial to elicit the preference structures of the user. As problems are often based on several criteria, multicriteria decision aiding methods are most adequate in these cases, and past works have proposed indirect eliciting approaches for multicriteria decision aiding methods. However, they often do not aim of reducing as much as possible the cognitive efforts required by the user. This is prerequisite of mobile applications as they are used by everybody. In this work, the weights to assign to the evaluation criteria in a PROMETHEE-based ranking approach are unknown, and therefore must be elicited indirectly either from a partial ranking provided by the user or from the selection of his/her most preferred alternative into a subset of reference alternatives. In the latter case, the cognitive effort required by the decision-maker is minimal. Starting from a linear optimisation model aimed at searching for the most discriminating vector of weights, three quadratic variants are proposed subsequently to overcome the issues arising from the linear model. An iterative quadratic optimisation model is proposed to fit the real setting in which the application should operate, where the eliciting procedure must be launched iteratively and converge over time to the vector of weights, which are the weights that the user implicitly assigns to the evaluation criteria. Finally, three experiments are performed to confirm the effectiveness and the differences between the proposed models.
Francesco Lolli; Elia Balugani; Alessio Ishizaka; Rita Gamberini; Maria Angela Butturi; Samuele Marinello; Bianca Rimini. On the elicitation of criteria weights in PROMETHEE-based ranking methods for a mobile application. Expert Systems with Applications 2018, 120, 217 -227.
AMA StyleFrancesco Lolli, Elia Balugani, Alessio Ishizaka, Rita Gamberini, Maria Angela Butturi, Samuele Marinello, Bianca Rimini. On the elicitation of criteria weights in PROMETHEE-based ranking methods for a mobile application. Expert Systems with Applications. 2018; 120 ():217-227.
Chicago/Turabian StyleFrancesco Lolli; Elia Balugani; Alessio Ishizaka; Rita Gamberini; Maria Angela Butturi; Samuele Marinello; Bianca Rimini. 2018. "On the elicitation of criteria weights in PROMETHEE-based ranking methods for a mobile application." Expert Systems with Applications 120, no. : 217-227.
Inventory control is one of the main activities in industrial plant management. Both process owners and line workers interact daily with stocks of components and finite products, and an effective management of these inventory levels is a key factor in an efficient manufacturing process. In this paper the algorithms k-means and Ward’s method are used to cluster items into homogenous groups to be managed with uniform inventory control policies. This unsupervised step reduces the need for computationally expensive inventory system control simulations. The performance of this methodology was found to be significant but was strongly impacted by the intermediate feature transformation processes.
E. Balugani; F. Lolli; R. Gamberini; B. Rimini; A. Regattieri. Clustering for inventory control systems. IFAC-PapersOnLine 2018, 51, 1174 -1179.
AMA StyleE. Balugani, F. Lolli, R. Gamberini, B. Rimini, A. Regattieri. Clustering for inventory control systems. IFAC-PapersOnLine. 2018; 51 (11):1174-1179.
Chicago/Turabian StyleE. Balugani; F. Lolli; R. Gamberini; B. Rimini; A. Regattieri. 2018. "Clustering for inventory control systems." IFAC-PapersOnLine 51, no. 11: 1174-1179.
Given the relevance of stochastic modelling in supply chain analysis and the advances in queuing theory in different subject fields (such as in-plant manufacturing systems), the present paper deals with the application of a Bernoulli model to the simple but representative (and frequently cited) case of a single-vendor single-buyer supply chain with (s, S)-inventory policy, where the maximum capacity S has to be restored in the intermediate warehouse whenever the inventory position reaches or drops below the reorder point s. An analytical stochastic model is presented which considers two two sources of uncertainty, supply uncertainty and demand uncertainty. The model is analytically formulated and solved in closed-form in order to compute some significant performance measures and, consequently, support tactical decisions (such as the definition of the design parameters s and S). Then, the model setting up is discussed. Specifically, the problem of relating demand and supply probabilities to the behaviour exhibited by the buyer and (particularly) the vendor is addressed. Finally, some numerical results are provided for illustration.
Davide Castellano; Elisa Gebennini; Andrea Grassi; Teresa Murino; Bianca Rimini. Stochastic modeling of a single-vendor single-buyer supply chain with (s, S)-inventory policy. IFAC-PapersOnLine 2018, 51, 974 -979.
AMA StyleDavide Castellano, Elisa Gebennini, Andrea Grassi, Teresa Murino, Bianca Rimini. Stochastic modeling of a single-vendor single-buyer supply chain with (s, S)-inventory policy. IFAC-PapersOnLine. 2018; 51 (11):974-979.
Chicago/Turabian StyleDavide Castellano; Elisa Gebennini; Andrea Grassi; Teresa Murino; Bianca Rimini. 2018. "Stochastic modeling of a single-vendor single-buyer supply chain with (s, S)-inventory policy." IFAC-PapersOnLine 51, no. 11: 974-979.
A case study on continuous process control based on fuzzy logic and supported by expert knowledge is proposed. The aim is to control the coal-grinding operations in a cement manufacturing plant. Fuzzy logic is based on linguistic variables that emulate human judgment and can solve complex modeling problems subject to uncertainty or incomplete information. Fuzzy controllers can handle control problems when an accurate model of the process is unavailable, ill-defined, or subject to excessive parameter variations. The system implementation resulted in productivity gains and energy consumption reductions of 3% and 5% respectively, in line with the literature related to similar applications.
Miguel A. Sellitto; Elia Balugani; Rita Gamberini; Bianca Rimini. A Fuzzy Logic Control application to the Cement Industry. IFAC-PapersOnLine 2018, 51, 1542 -1547.
AMA StyleMiguel A. Sellitto, Elia Balugani, Rita Gamberini, Bianca Rimini. A Fuzzy Logic Control application to the Cement Industry. IFAC-PapersOnLine. 2018; 51 (11):1542-1547.
Chicago/Turabian StyleMiguel A. Sellitto; Elia Balugani; Rita Gamberini; Bianca Rimini. 2018. "A Fuzzy Logic Control application to the Cement Industry." IFAC-PapersOnLine 51, no. 11: 1542-1547.
The Assembly Line Balancing Problem (ALBP) represents one of the most explored research topics in manufacturing. However, only a few contributions have investigated the effect of the combined abilities of humans and machines in order to reach a balancing solution. It is well-recognized that human beings learn to perform assembly tasks over time, with the effect of reducing the time needed for unitary tasks. This implies a need to re-balance assembly lines periodically, in accordance with the increased level of human experience. However, given an assembly task that is partially performed by automatic equipment, it could be argued that some subtasks are not subject to learning effects. Breaking up assembly tasks into human and automatic subtasks represents the first step towards more sophisticated approaches for ALBP. In this paper, a learning curve is introduced that captures this disaggregation, which is then applied to a stochastic ALBP. Finally, a numerical example is proposed to show how this learning curve affects balancing solutions.
F. Lolli; E. Balugani; R. Gamberini; B. Rimini; V. Rossi. A human-machine learning curve for stochastic assembly line balancing problems. IFAC-PapersOnLine 2018, 51, 1186 -1191.
AMA StyleF. Lolli, E. Balugani, R. Gamberini, B. Rimini, V. Rossi. A human-machine learning curve for stochastic assembly line balancing problems. IFAC-PapersOnLine. 2018; 51 (11):1186-1191.
Chicago/Turabian StyleF. Lolli; E. Balugani; R. Gamberini; B. Rimini; V. Rossi. 2018. "A human-machine learning curve for stochastic assembly line balancing problems." IFAC-PapersOnLine 51, no. 11: 1186-1191.
Martina Fabbri; Giulia Guidotti; Michelina Soccio; Nadia Lotti; Marco Govoni; Emanuele Giordano; Massimo Gazzano; Rita Gamberini; Bianca Rimini; Andrea Munari. Novel biocompatible PBS-based random copolymers containing PEG-like sequences for biomedical applications: From drug delivery to tissue engineering. Polymer Degradation and Stability 2018, 153, 53 -62.
AMA StyleMartina Fabbri, Giulia Guidotti, Michelina Soccio, Nadia Lotti, Marco Govoni, Emanuele Giordano, Massimo Gazzano, Rita Gamberini, Bianca Rimini, Andrea Munari. Novel biocompatible PBS-based random copolymers containing PEG-like sequences for biomedical applications: From drug delivery to tissue engineering. Polymer Degradation and Stability. 2018; 153 ():53-62.
Chicago/Turabian StyleMartina Fabbri; Giulia Guidotti; Michelina Soccio; Nadia Lotti; Marco Govoni; Emanuele Giordano; Massimo Gazzano; Rita Gamberini; Bianca Rimini; Andrea Munari. 2018. "Novel biocompatible PBS-based random copolymers containing PEG-like sequences for biomedical applications: From drug delivery to tissue engineering." Polymer Degradation and Stability 153, no. : 53-62.
The paper deals with the problem of assigning jobs to operators in contexts where the operators are not fixed on a single position, but rotate, by travelling on foot, between different stations. The objective is to jointly consider the need for minimising the operators’ walking costs, expressed as both unproductive times and physiological costs, and the ergonomic risk of the scheduled jobs and their combinations. A new optimisation-based methodology is presented by developing a systematic procedure for input data analysis and an original mixed-integer linear programming model which minimises the cost of walking (or the total metabolic cost) by considering workplace safety and physiological needs. Finally, the proposed optimisation approach has been applied to a case study from the plastic industry. The obtained results allow to draw some interesting conclusions about the impact of ergonomic aspects on the optimal assignment of jobs to operators. Moreover, the importance of reducing unproductive times (i.e. walking times) and, if possible, improving the design of manual tasks (e.g. lifting operations) is highlighted by showing that even small ergonomic investments may lead to significant cost savings.
Elisa Gebennini; Luca Zeppetella; Andrea Grassi; Bianca Rimini. Optimal job assignment considering operators’ walking costs and ergonomic aspects. International Journal of Production Research 2018, 56, 1249 -1268.
AMA StyleElisa Gebennini, Luca Zeppetella, Andrea Grassi, Bianca Rimini. Optimal job assignment considering operators’ walking costs and ergonomic aspects. International Journal of Production Research. 2018; 56 (3):1249-1268.
Chicago/Turabian StyleElisa Gebennini; Luca Zeppetella; Andrea Grassi; Bianca Rimini. 2018. "Optimal job assignment considering operators’ walking costs and ergonomic aspects." International Journal of Production Research 56, no. 3: 1249-1268.
Elisa Gebennini; Andrea Grassi; Cesare Fantuzzi; Bianca Rimini. Discrete time model of a two-station one-buffer serial system with inventory level-dependent operation. Computers & Industrial Engineering 2017, 113, 46 -63.
AMA StyleElisa Gebennini, Andrea Grassi, Cesare Fantuzzi, Bianca Rimini. Discrete time model of a two-station one-buffer serial system with inventory level-dependent operation. Computers & Industrial Engineering. 2017; 113 ():46-63.
Chicago/Turabian StyleElisa Gebennini; Andrea Grassi; Cesare Fantuzzi; Bianca Rimini. 2017. "Discrete time model of a two-station one-buffer serial system with inventory level-dependent operation." Computers & Industrial Engineering 113, no. : 46-63.
Francesco Lolli; Alessio Ishizaka; Rita Gamberini; Bianca Rimini; Elia Balugani; Laura Prandini. Requalifying public buildings and utilities using a group decision support system. Journal of Cleaner Production 2017, 164, 1081 -1092.
AMA StyleFrancesco Lolli, Alessio Ishizaka, Rita Gamberini, Bianca Rimini, Elia Balugani, Laura Prandini. Requalifying public buildings and utilities using a group decision support system. Journal of Cleaner Production. 2017; 164 ():1081-1092.
Chicago/Turabian StyleFrancesco Lolli; Alessio Ishizaka; Rita Gamberini; Bianca Rimini; Elia Balugani; Laura Prandini. 2017. "Requalifying public buildings and utilities using a group decision support system." Journal of Cleaner Production 164, no. : 1081-1092.
Martina Fabbri; Luis García-Fernández; Blanca Vázquez; Michelina Soccio; Nadia Lotti; Rita Gamberini; Bianca Rimini; Andrea Munari; Julio San Román. Micro-structured 3D-electrospun scaffolds of biodegradable block copolymers for soft tissue regeneration. European Polymer Journal 2017, 94, 33 -42.
AMA StyleMartina Fabbri, Luis García-Fernández, Blanca Vázquez, Michelina Soccio, Nadia Lotti, Rita Gamberini, Bianca Rimini, Andrea Munari, Julio San Román. Micro-structured 3D-electrospun scaffolds of biodegradable block copolymers for soft tissue regeneration. European Polymer Journal. 2017; 94 ():33-42.
Chicago/Turabian StyleMartina Fabbri; Luis García-Fernández; Blanca Vázquez; Michelina Soccio; Nadia Lotti; Rita Gamberini; Bianca Rimini; Andrea Munari; Julio San Román. 2017. "Micro-structured 3D-electrospun scaffolds of biodegradable block copolymers for soft tissue regeneration." European Polymer Journal 94, no. : 33-42.
Several papers have studied inventory classification in order to group items with a view to facilitating their management. The generated classes are then coupled with the specific reorder policies composing the overall inventory control system. However, the effectiveness of inventory classification and control system is strictly interrelated. That is to say, different classification approaches could show different performance if applied to a different set of reorder policies, and vice versa. Furthermore, when the cost structure is subjected to uncertainty, a pure cost-based analysis of the inventory control system could be corrupted. This paper presents a multicriteria framework for the concurrent selection of the item classification approach and the inventory control system through a discrete-event simulation approach. The key performance indicators provided by the simulator (i.e., average holding value, average number of backorders, and average number of emitted orders) are indicative of the multidimensional effectiveness of the adopted inventory control system when coupled with a specific classification approach. By this way, a multicriteria problem arises, where the alternatives are given by exhaustively coupling the item classes, which are generated by different classification approaches, with the reorder policies composing the inventory system. An analytical hierarchy process is then used for selecting the best alternative, as well as for evaluating the effect of the weights assigned to the key performance indicators through a sensitivity analysis. This approach has been validated in a real case study with a company operating in the field of electrical resistor manufacturing, with a view of facilitating the management of items showing intermittent demand.
F. Lolli; A. Ishizaka; R. Gamberini; Bianca Rimini. A multicriteria framework for inventory classification and control with application to intermittent demand. Journal of Multi-Criteria Decision Analysis 2017, 24, 275 -285.
AMA StyleF. Lolli, A. Ishizaka, R. Gamberini, Bianca Rimini. A multicriteria framework for inventory classification and control with application to intermittent demand. Journal of Multi-Criteria Decision Analysis. 2017; 24 (5-6):275-285.
Chicago/Turabian StyleF. Lolli; A. Ishizaka; R. Gamberini; Bianca Rimini. 2017. "A multicriteria framework for inventory classification and control with application to intermittent demand." Journal of Multi-Criteria Decision Analysis 24, no. 5-6: 275-285.
F. Lolli; E. Balugani; R. Gamberini; B. Rimini. Stochastic assembly line balancing with learning effects. IFAC-PapersOnLine 2017, 50, 5706 -5711.
AMA StyleF. Lolli, E. Balugani, R. Gamberini, B. Rimini. Stochastic assembly line balancing with learning effects. IFAC-PapersOnLine. 2017; 50 (1):5706-5711.
Chicago/Turabian StyleF. Lolli; E. Balugani; R. Gamberini; B. Rimini. 2017. "Stochastic assembly line balancing with learning effects." IFAC-PapersOnLine 50, no. 1: 5706-5711.
Francesco Lolli; Alessio Ishizaka; Rita Gamberini; Elia Balugani; Bianca Rimini. Decision Trees for Supervised Multi-criteria Inventory Classification. Procedia Manufacturing 2017, 11, 1871 -1881.
AMA StyleFrancesco Lolli, Alessio Ishizaka, Rita Gamberini, Elia Balugani, Bianca Rimini. Decision Trees for Supervised Multi-criteria Inventory Classification. Procedia Manufacturing. 2017; 11 ():1871-1881.
Chicago/Turabian StyleFrancesco Lolli; Alessio Ishizaka; Rita Gamberini; Elia Balugani; Bianca Rimini. 2017. "Decision Trees for Supervised Multi-criteria Inventory Classification." Procedia Manufacturing 11, no. : 1871-1881.