Scheduling Jobs Families with Learning Effect on the Setup

The present paper aims to address the flow-shop sequence-dependent group scheduling problem with learning effect (FSDGSLE). The objective function to be minimized is the total completion time, that is, the makespan. The workers are required to carry out manually the set-up operations on each group to be loaded on the generic machine. The operators skills improve over time due to the learning effects; therefore the set-up time of a group under learning effect decreases depending on the order the group is worked in. In order to effectively cope with the issue at hand, a mathematical model and a hybrid metaheuristic procedure integrating features from genetic algorithms (GA) have been developed. A well-known problem benchmark risen from literature, made by two-, three- and six-machine instances, has been taken as reference for assessing performances of such approach against the two most recent algorithms presented by literature on the FSDGS issue. The obtained results, also supported by a properly developed ANOVA analysis, demonstrate the superiority of the proposed hybrid metaheuristic in tackling the FSDGSLE problem under investigation

a hybrid metaheuristic approach for minimizing the total flow time in a flow shop sequence dependent group scheduling problem

Production processes in Cellular Manufacturing Systems (CMS) often involve groups of parts sharing the same technological requirements in terms of tooling and setup. The issue of scheduling such parts through a flow-shop production layout is known as the Flow-Shop Group Scheduling (FSGS) problem or, whether setup times are sequence-dependent, the Flow-Shop Sequence-Dependent Group Scheduling (FSDGS) problem. This paper addresses the FSDGS issue, proposing a hybrid metaheuristic procedure integrating features from Genetic Algorithms (GAs) and Biased Random Sampling (BRS) search techniques with the aim of minimizing the total flow time, i.e., the sum of completion times of all jobs. A well-known benchmark of test cases, entailing problems with two, three, and six machines, is employed for both tuning the relevant parameters of the developed procedure and assessing its performances against two metaheuristic algorithms recently presented by literature. The obtained results and a properly arranged ANOVA analysis highlight the superiority of the proposed approach in tackling the scheduling problem under investigation

Modelling of a Debris Flow Event in the Enna Area for Hazard Assessment

Abstract In the paper a modelling of a real debris flow in the Enna area in the south of Italy is described. Starting from the study of the geological framework and the historical background for landslides of the Enna district, the research has focused on the causes triggering the landslides. In order to study the performance of debris flow, the real case of 1 st -2 nd February 2014 which affected Enna city has been modeled. The event caused damage to private buildings and above all the interruption of the main infrastructure connecting Enna city at the motorway, due to the material on the road. The modelling of the real debris flow using a mono-phase model (FLO-2D) was carried out in order to investigate the global dynamic of the event. The study allows to acquire a better knowledge of the hydraulic parameters that can be used in other modelling events for areas with a similar soil composition in order to assess the most appropriate mitigation works, reducing damage to structures and infrastructures

A new data-driven framework to select the optimal replenishment strategy in complex supply chains

- Part of special issue: 10th IFAC Conference on Manufacturing Modelling, Management and Control MIM 2022: Nantes, France, 22-24 June 2022. Edited by Alain Bernard, Alexandre Dolgui, Hichem Haddou Benderbal, Dmitry Ivanov, David Lemoine, Fabio Sgarbossa - Copyright © 2022 The Authors. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/)Motivated by the high variability of markets occurred in the last years, which in turns determined significant uncertainty in lead times and supply chain dynamics, this paper introduces a data-driven framework based on machine learning and metaheuristic optimization to dynamically select the most suitable replenishment strategy for a complex two-echelon (supplier-inventory-factory) supply chain (SC) problem with perishable product and stochastic lead times. Since the supplier dispatches the product (i.e., the raw material) with a fixed expiration date, the product shelf-life strictly depends on the related delivery lead time, which is subject to uncertainty. In addition, a minimum order quantity has to be fulfilled and the time between two consecutive orders cannot be less than one month. The aim of the work is to select the most suitable replenishment strategy able to minimize the average stock level, which is a surrogate cost metric, while respecting a target fill rate. Considering a smoothing order-up-to policy, the data-driven prediction-optimization framework makes use of Artificial Neural Network (ANN) and Particle Swarm Optimization (PSO) to select the best replenishment parameters (i.e., forecasting factor, proportional controller and safety stock factor) able to dynamically enhance the SC economic performance under the fill rate constraint. The ability of the framework under the predictive and the optimization perspective is assessed and a sensitivity analysis on the influence of replenishment parameters is presented as well

Energy analysis of a micro-cogeneration unit fed by biogas as a function of pyrolysis operating parameters

In this study, the biomass degradation and the evolution of chemical species during pyrolysis are analysed with the main aim of evaluating the energy performance of a micro-cogeneration unit fed by biogas. The decomposition of the feedstock material is modelled as a two-stage process: firstly, in the reactor, the biomass is decomposed in a residual solid fraction (char) and a gaseous mixture; then, the condensable gases are divided from permanent gases generating the pyro-oil. The mathematical model proposed in this work has been developed considering the dependence of the pyrolysis process from the temperature and within the interval 500–900 °C. The kinetic of the reactions involved during the pyrolysis was also taken into account. Simulations run in AspenPlus exploiting the R-yield reactor supported by a calculator block. Afterwards, the energy recovery line for the valorisation of the pyro-products has been analysed. The gas fraction obtained at the end of the cycle was firstly characterized and then used to feed a micro-CHP system. Results are very promising, with great potential in terms of thermal recovery; more than 60% of the initially fed biogas and about 30% power output can be derived

Energy analysis of a micro-cogeneration unit fed by biogas as a function of pyrolysis operating parameters

In this study, the biomass degradation and the evolution of chemical species during pyrolysis are analysed with the main aim of evaluating the energy performance of a micro-cogeneration unit fed by biogas. The decomposition of the feedstock material is modelled as a two-stage process: firstly, in the reactor, the biomass is decomposed in a residual solid fraction (char) and a gaseous mixture; then, the condensable gases are divided from permanent gases generating the pyro-oil. The mathematical model proposed in this work has been developed considering the dependence of the pyrolysis process from the temperature and within the interval 500–900 °C. The kinetic of the reactions involved during the pyrolysis was also taken into account. Simulations run in AspenPlus exploiting the R-yield reactor supported by a calculator block. Afterwards, the energy recovery line for the valorisation of the pyro-products has been analysed. The gas fraction obtained at the end of the cycle was firstly characterized and then used to feed a micro-CHP system. Results are very promising, with great potential in terms of thermal recovery; more than 60% of the initially fed biogas and about 30% power output can be derived

application of a mapping tool to plan energy saving at a neighborhood scale

Abstract This study proposes the application of a model for the evaluation of the overall energy demand of existing urban neighborhoods, which can be useful when planning energy enhancement strategies at urban scale. The application of this model can be interconnected with the use of a GIS software tool, thus providing the opportunity to perform the energy mapping of city neighborhoods. In the proposed model, the overall energy demand of existing urban neighborhoods is evaluated by considering the three most energy intensive sectors: buildings, transport and urban lighting. However, in this paper the application of the model is only focused on the assessment of the energy demand in the building sector. The proposed methodology is applied to a neighborhood of the municipality of Catania in Southern Italy. The preliminary results are reported in this study: first, the existing energy consumption for space heating and electric appliances is assessed, then the effectiveness of a series of energy-saving strategies is considered, thus providing a tool to implement effective energy planning policies at urban scale

uhi effects and strategies to improve outdoor thermal comfort in dense and old neighbourhoods

Abstract Modelling techniques have received growing attention as a tool to investigate the thermal comfort within a city, on the basis of which decision makers can set-up appropriate mitigation strategies. This research aims at studying the effectiveness of strategies for reducing the urban heat island-associated effects in dense and old neighborhoods considering, in particular, green roofs, cool roofs, cool pavements, green areas and urban renewal actions. Computer simulation was selected as the major methodology in this research; ENVI-met software was used under different scenarios for a case study consisting in an old neighborhood in the city of Avola. The investigation focused on evaluating the efficacy of each strategy for a condition corresponding to a typical summer heat wave. The results highlight that the cool pavements allow relevant improvements at the height of 1.50 m, with a temperature decrease up 1.15°C, whereas the other scenarios, given the relatively high density of the buildings, are able to improve outdoor conditions only at higher elevations. Reported results represent a guideline for the choice of UHI mitigation method that can help stakeholders involved in new urban assessment of old neighborhoods in Mediterranean climate

Landscape planning and ecological networks: part A: a rural system in Nuoro, Sardinia

Urban-rural landscape planning research is nowadays focusing on strategies and tools that support practitioners in designing integrated spaces starting from the analysis of local areas, where human and natural pressures interfere. A prominent framework is provided by the ecological networks, whose design regards the combination of a set of green areas or patches (the nodes) interconnected through environmental corridors (the edges). Ecological networks are useful for biodiversity protection and enhancement, as they are able to counteract fragmentation, and to create or strengthen relations and exchanges among otherwise isolated elements. Biodiversity evolution, indeed, depends on the quantity and quality of spatial cohesion of natural areas. In this paper, we aim at designing an ecological network for the periurban area on the town of Nuoro in central Sardinia. The narrative unfolds in two parts. Part A is presented in this paper and includes its methodological premises, i.e. biodiversity conservation and ecological network analysis and design, and the introductory elements of a spatial analysis on a pilot ecological network of one hundred patches. We locate patches by focusing on the ecosystems supported by the target vegetal species holm oak (Quercus ilex) and cultivated or wild olive (Olea europaea var. sativa, O. europaea var. sylverstis). These are very common plants species in the municipality and some animal species are active as seed dispersal. The reminder, i.e. Part B, of the essay is presented in an homonymous paper that focuses on the illustration of the network analysis conceived as a monitoring system and, in future perspective, as a planning support system

Echinococcosis — Rare Locations and Uncommon Clinical Manifestations

Echinococcosis is a zoonotic infection caused by tiny tapeworms of the genus Echinocococcus. Cystic Echinococcosis, also known as hydatid disease, is caused by Echinococcus granulosus and rarely by Echinococcus multilocularis