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Manufacturing processes have a high impact on global energy consumption. Machine tool's environmental impact is typically dominated by the energy absorbed during the use phase. Energy efficiency is progressively considered as an additional performance index in comparing alternative machines, process planning, and machining strategies. For this purpose, this paper proposes a simulation approach that estimates the energy used by a machine tool in producing a generic workpiece by general milling operations. The developed tool simulates the execution of a standard ISO part program, basing on an explicit geometric and mechanistic representation of the cutting process, coupled with an energy model of the machine tool reproducing the power consumption of spindle, axes, and auxiliary units. Energy models were identified by an experimental characterization procedure that can be easily adopted in industrial contexts. The simulator was validated comparing the estimated energy with measurements performed on different cutting tests, evaluating also its computational effort. Moreover, the simulator performances were compared to alternative energy evaluation methods proposed in the literature.

Abstract Global supply chains require integrated cost optimisation of sourcing, production and distribution through flexible networks of suppliers, manufacturers and logistics operators. The minimisation of environmental impacts of freight flows must be considered for sustainable development and should turn into a competitive advantage. To these aims, the paper presents a model integrating supply, production networks and sustainable freight transportation for strategic and tactical decision-making. Bill-of-Materials constraints are included in the model. The objective function considers sourcing, production and transportation costs as well as carbon dioxide emissions as environmental impacts of transport over a multimodal network. Computational experiments demonstrate the effectiveness of the model for cost-emissions analysis.

Over the last decade, significant attention has been devoted to Closed-Loop Supply Chain (CLSC) design problems. As such, this review aims at assessing whether the current modelling approaches for CLSC problems can support the transition towards a Circular Economy at a supply chain level. The paper comprehensively assesses the extent to which existing modelling approaches evaluate the performance of supply chains across the complete spectrum of sustainability dimensions. Also, the capability of the current approaches of incorporating strategic, tactical, and operational decisions is considered, along with adopted solution methodologies. As a result, a comprehensive analysis was performed on 254 selected articles. This paper emphasises how most of the current literature in the field is affected by a disconnection between supply chain design and the founding principles of Circular Economy. Specifically, the CLSC literature exhibits a reductionist interpretation of the Circular Economy. CLSC studies focusing on all three dimensions of sustainability are relatively rare, and performance measurement approaches appear to be very much focused on monetary issues. While methodological contributions appear adequate to focus on the non-deterministic nature of CLSC design problems, there is paucity of empirically-grounded research. Coherently, a research agenda is proposed, in order to address the mentioned gaps and increase the relevance of this research field to practice.

An integrated software tool environment is presented, and a methodology is proposed for the operational support of the local authority, for analysis of the impact of transport measures in terms of network energy consumption and pollutant emissions. It is based on work done by the European Union within the save program (speci®c actions for vigorous energy eciency)ÐSlam project (supporting local authorities methodology). As background, the Slam project is described, with the principal aspects and needs of environmental and trac network management. The central section de®nes a methodology able to support technicians in recognizing the trac asset and decision makers in evaluating interventions on urban transport infrastructures or technological systems. The role of the di€erent models and their interactions with the transport telematics services currently active on the Florence (Italy) network is discussed. Finally, the procedure for calculating the trac impacts on energy consumption is described with the help of a test case, the evaluation of a dedicated bus corridor in Florence. # 1999 Published by Elsevier Science Ltd. All rights reserved.

An electric KERS for internal combustion engine vehicles is designed. o The design uses components available on the market minimizing the weight and the size. o The performances are evaluated by a mathematical model in Simulink environment®. Two urban driving cycles and a diffused passenger car are considered as scenario. A sensitivity analysis is performed to study the effect of each element on performance.

Society is becoming more conscious on the need to preserve the environment. Sustainable development schemes have grown rapidly as a tool for managing, predicting and improving the growth path in different regions and economy sectors. We introduce a novel and simple mathematical model of ordinary differential equations (ODEs) in order to obtain a dynamical description for each one of the sustainability components (economy, social development and environment conservation), together with their dependence with demographic dynamics. The main part in the modeling task is inspired by the works by Cobb, Douglas, Brander and Taylor. This is completed through some new insights by the authors. A model application is presented for three specific geographical rural regions in Caldas (Colombia).

Mobility and congestion are critical concerns for every city, be it large or small,due to the economic and environmental challenges that they pose. Manyanalysts have advocated addressing these issues by encouraging newmultimodal services and fostering the deployment of more efficient ondemandmobility services. In this work, we focus on car sharing, a mode oftransportation that is gaining increasing popularity with its promise to reducetraffic congestion, parking demands and pollution in our cities. There are twomain classes of car sharing services: station-based car sharing or free floatingcar-sharing. In the former, shared vehicles are picked up and dropped off atdesignated (and reserved) locations within the service area, called stations. Infree floating car sharing, instead, cars can be picked up and dropped offanywhere within the service area, as long as parking is permitted at thatlocation. The two approaches have each advantages and disadvantages.Free floating car sharing offers a lot of flexibility to customers, but in citieswere finding a parking spot is troublesome, the reserved parking spaceoffered by station-based car sharing may appeal more.

Car sharing holds a promise of reducing traffic congestion and pollution in cities as well as of boosting the use of public transport when used as a last-mile solution in a multimodal transportation scenario. Despite this huge potential, several problems related to the deployment and operations of car sharing systems have yet to be fully addressed. In this work, we focus on station-based car sharing and we define an optimization problem for the deployment of its stations. The goal of this problem is to find the minimum cost deployment (in terms of number of stations and their capacity) that can guarantee a pre-defined level of service to the customers (in terms of probability of finding an available car/parking space). This problem combines insights from queueing theory (used to model the stochastic demand for cars/parking spaces at the stations) with a variant of the classical set covering problem. For its evaluation, we use a trace of more than 100,000 pickup and drop-off events at a free-floating car sharing service in The Netherlands, which are used to model the input demand of the car sharing system. Our results show that the proposed solution is able to strike the right balance between cost minimisation and quality of service, outperforming three alternative schemes used as benchmarks.

Keeping temperature change below 2°C will require leaving large reserves of fossil fuels unextracted. We assess alternative policies to achieve this goal in a world divided into two regions, one regulated with emissions pricing and one unregulated, supplied by multiple resource pools with different extraction costs and a carbon-free backstop whose costs decline over time. Global emissions can be reduced by combinations of three policies: (1) increasing the size of the regulated coalition, (2) raising the tax (or tightening the cap) within the regulated coalition, and (3) accelerating cost reductions of the clean technology. We ask how combinations achieving the same cumulative CO2 emissions (the “carbon budget”) affect the discounted surplus of regulated and unregulated consumers and the wealth of the extractors. We first evaluate these policy alternatives in a two-pool, theoretical model with high and low-cost extractors and, once tradeoffs are illuminated, in a calibrated oil market model. A global CO2 tax is both cost-effective and the best policy for regulated consumers. Extractors and unregulated consumers, however, prefer a technology-only policy. When the regulating coalition is subglobal, support for the clean substitute is desired and often required. But the preferred policy portfolio of the coalition involves a tighter cap and less technology policy than is globally optimal, leaving room for bargaining over who pays for clean innovation.