Human labor still plays a crucial role in many work contexts. However, increases in production rate require increases in human workloads. The authors propose two integer programming models aiming at finding both optimal break and job rotation schedules in work-environments characterized by low load manual tasks with high frequency of repetition. In such a work environment a major risk consists of work-related musculoskeletal disorders. Workload risk and acceptability are evaluated by the OCRA index (ISO 11228-3:2007). Models proposed are applied to an assembly line from the automotive industry. Results obtained revealed the effectiveness of the models as they proved to be adequate tools to jointly address the reduction and balancing of human workloads among employees, which are consistent with acceptable workload limits and required production levels.

The concept of ‘Sustainability’ is increasingly used to describe a paradigm upon which future policies must be based. In the view of a urban sustainable development, a strategic role in the governance of the smart City is played by waste integrated management systems (IWMS). The raising complexity of a IWMS relies on the high number of design and management variables and relationships related to collection, treatments and disposal phases. Waste management practices can affect greenhouse gas emission by influencing energy consumption, methane generation, carbon sequestration and non-energy related manufacturing emission. A decision support system can be modelled aiming to evaluate and minimize the carbon footprint of a IWMS. The model proposed goes beyond the existing technical and organizational solutions outlining the different options in a much broader view concerning both waste collection and treatments.

Abstract: Many contributions could be found in scientific literature about logistics and inventory management of spare parts, but there is a lack of attention on the sustainable issues. This paper investigates how the single-product replenishment problem can be formulated and solved including sustainable concerns in the inventory model. The problem has been studied in case of stochastic variability of the consumption rate of repairable spare parts. The optimal order quantity is derived by minimising a sustainable logistic cost function in which economic and environmental costs of both the options of repairing and purchasing new spare parts are considered. Traditional logistic costs of spare parts (purchase, ordering, transport, holding and repair) as well as environmental costs due to transport, production, and disposal of replaced ones are considered. Furthermore, since demand of spare parts is uncertain and assumed to be an independent stochastic variable in each period of the supply lead time, shortage costs due to stock out events are considered. The optimal means of transport, the optimal repair policy, as well as the corresponding optimal value of the sustainable economic order quantity (SOQ) are calculated. A numerical example derived from field industrial data is presented to test the model capabilities. Results are discussed and compared with classical EOQ model solutions

oil, and coal. Sources of PAHs include emissions from industrial activities such as primary aluminum and coke production, petrochemical industries, rubber tire and cement manufacturing, bitumen and asphalt industries. Some studies outline a significant correlation between mortality by lung cancer in humans and exposure to PAHs. Monitoring activities to assess the human exposure to airborne PAHs in workplaces are expensive and time consuming since they require many samplers and analytical methods derived from analytical chemistry. The aim of this study is to develop a tool that, through the prediction of the PAHs concentrations in work environment, suggests a sampling strategy able to improving the reliability of measurements and reduce costs of environmental monitoring. Different workplaces, using a multi zone modeling simulation software, have been analyzed; for each case the concentration of the pollutants emitted in the indoor environment have been detected. The output data of the simulator have been adopted to train an Artificial Neural Network (ANN). The trained ANN is able to provide, through computational logic, a reliable forecast about the concentrations of different species of pollutants statistically distributed in the environment based both on characteristics of the workplace (as room dimensions, surfaces of aeration, etc.) that on type of contaminant source and on intensity of emission. This allows to determine the minimum number and the correct location of the samplers to perform the environmental monitoring. The jointly use of the simulation software and the ANN allowed to develop a tool characterized by advantages of both the technologies. In fact, the simulation software allows to test a wide variety of cases and, for each of them, provides reliable measures of the pollutants concentration at different points. The ANN, instead, is trained by means of aggregates values of the characteristic variables obtained from simulation, thus providing the number and the position of the samplers to be adopted as well as the typology of pollutants (belonging PAHs groups) to be measured. The main advantages of the toll are that it requires a limited number of input data, provide outputs in a very limited computational time, and, above all, it allows to reduce cost of sampler activity by reducing the number of sampling points.

Environmental impacts of the road vehicles manufacturing and usage have been widely studied but very limited considerations have been made about the new vehicles distribution from the assembly plants to the final users. In this paper a methodology is proposed in order to evaluate the external costs of new passenger cars distribution and to estimate the achievable reduction that could be obtained by adopting different transportation modes. Starting from the location of the assembly plants and the models of passenger cars produced in 2013 in Europe, the external costs associated to the flows from the assembly plants to the selected European countries are evaluated. Potential environmental savings are estimated and discussed.

Nowadays fossil resources still play a major role in the energy production and they are responsible for most of the greenhouse gases (GHG) emissions. The increasing global energy demand requires governments, at both high and local level, planning and forecasting their energy demands in order to meet such needs in the most sustainable way, reducing GHG emissions as already stated in international agreement. Cities play a key role in moving towards a sustainable development, since they are the major energy and resource consumers. Starting from 2008, a group of European cities autonomously sets an ambitious target in seeking to reduce their carbon footprint at least by 20% by 2020 [Covenant of Major - Sustainable Energy Action Plan]. In order to help policy maker reaching the goal, in this study a simulation model of one of these cities (Bari, southern Italy) based on system dynamics has been developed. The model considers the regional energy demand and shows the effects of different strategies on carbon emission performance. The goal here is to provide local decision makers with a holistic view in order to give in-depth understanding and leverage the feedback interrelationship of urban energy system. The simulation model allows to test “what-if” scenarios and analyzes the expected results of implementing certain adjustment and control policies. Results provide essential information for the city’s future energy and carbon emission profiles.

Sustainability in urban development, economic growth and human well-being are critical issues faced all over the world. In the last decade urban planning and management had accomplished, or will do it, innovations in order to meet the challenges posed by sustainable development. Reduction of 20% of GHG emission, the achievement of 20% energy demand by renewable energy together with an increase of 20% of energy efficiency are targets foreseen by EU. In this scenario a strategic role is played by municipal waste integrated management system (MWIMS). The matter is becoming increasingly important as a results of the growth of urbanization rate: the raising complexity of a MWIMS relies on the high number of design and management variables and relationships pertaining to collection, treatments and disposal phases. Waste management practices can sway greenhouse gas emission by affecting energy consumption, methane generation, carbon sequestration and non-energy related manufacturing emission. In this context, a sustainable waste management system allows a reduction of negative impacts on environment. The purpose of such a study is to propose a decision-making framework aiming to minimize the carbon footprint of a MWIMS. The model goes beyond the existing technical and organizational solutions outlining the different options in a much broader view concerning both waste collection and treatments. A mixed integer linear programming model , has been applied to a full case study concerning Bari. The study is carried out within the research project RES NOVAE (Reti, Edifici, Strade ‐ Nuovi Obiettivi Virtuosi per l’Ambiente e l’Energia). The strength of the framework results in supporting public decision-making, a complex process due to the number of decision variables and their implications on economic performance. Results exhibit the effectiveness of the model also in pointing out opportunities non yet evaluated.

Polycyclic Aromatic Hydrocarbons (PAHs) are generated from the combustion of fuels such as gas, oil, and coal. In the scientific literature several studies outline a significant correlation between mortality by lung cancer in humans and exposure to PAHs. Sources of PAHs include emissions from industrial activities such as primary aluminium and coke production, petrochemical industries, rubber tire and cement manufacturing, bitumen and asphalt industries. At current, monitoring activities to assess the workers exposure to airborne PAHs are based on sampling and diagnostic methods derived from analytical chemistry; methods are based on a "trial and error" approach and are time consuming and frequently characterized by high costs. The aim of this study is the develop of a guidelines proposal for indoor air quality monitoring PAHs in order to identify efficient and effective sampling strategies allowing to jointly reduce the number of sampling points and to obtain reliable measurements at reasonable costs. We propose an approach for preliminary air pollution exposure risk assessment based on factors taking into accounts the characteristics of the workplace (as size, aeration surfaces, etc.), of the production process, the distance between PAHs sources and position of the workers exposed, and on other easy-to-detect information. The guidelines allow performing a preliminary reliable risk assessment providing an immediate perception of the workers exposure risk level and drive the user in identifying the optimal sampling strategy (minimum number and the correct location of the samples) without requiring chemical expertise. The guidelines have to be considered as recommendations and not as standards. Guidelines can be the basis for further developments leading to standards which will contribute in health and safety of workplaces.

Transport plays a key role in inventory management since it affects logistic costs as well as environmental performance of the supply chain. Expected value and variability of supply lead time depend on the transportation means adopted, and influence the optimal values of order quantity, reorder level, and safety stock to be adopted. Fast transportation means allow reducing expected value of the lead time; they are characterized by the highest costs of externalities (i.e. air pollutant emission, noise, congestion, accidents). On the contrary, slow transportation means require high inventory level due to large order quantity; in this case costs of externalities tend to decrease. The Sustainable Order Quantity (SOQ) [1] allows identifying optimal order quantity, reorder level, safety stock as well as transportation means which minimize the sum of the logistic and environmental costs in case of stochastic variability of product demand. In this paper, the authors propose a new SOQ analytical model considering stochastic variability of supply lead time (LT). A solution procedure is suggested for solving the proposed model. The approach is applied to a real industrial case study in order to evaluate the benefits of applying it if compared with the traditional one.