Complex and delocalized manufacturing industries require high levels of integration between production and transportation in order to effectively implement lean and agile operations. There are, however, limitations in research and applications simultaneously embodying further sustainability dimensions. This paper presents a methodological framework based on optimization and simulation to integrate aggregate optimized plans for production and multimodal transportation with detailed dynamic distribution plans affected by demand uncertainty. The objective function of the optimization model considers supply, production, transportation, and CO ₂ emission costs, as well as collaboration over the multimodal network. Bill-of-materials and capacity constraints are included. A feedback between simulation and optimization is used to plan requirements for materials and components. Computational experiments are based on realistic instances. Results demonstrate that the framework can be effectively used to analyze cost-CO ₂ emission tradeoffs, effects of demand uncertainty, and collaborative distribution strategies on economic and environmental performance of the supply chain.

Natural events, climate change and urbanization are pushing the pressure on disaster relief operations. Decision science and ICT technologies can be effectively used to face humanitarian logistics issues. This concerns both man-made threats (accidents) and natural hazards such as e.g. floods, storms, earthquakes and volcanic eruptions. The purpose of this paper is to analyse the operation strategies for rescue in emergency situations deployed by specialized rescue teams. Usually standard procedures are applied for rescue operations. These procedures can fail in disaster relief situations where an abnormal number of rescue operations are to be fulfilled. It is important to study mechanisms able to give more flexibility to these procedures and to study the effectiveness of the procedures in planning phase. At this aim we used discrete event simulation as decision support for planning different strategies of action to apply in emergency and risk situations. In particular several scenarios have been developed as simulation models combining different initial hypothesis with the aim to build a generalized and flexible procedure to apply in different scenarios. As a result, the simulation is able to allocate efficiently different resources under emergency situations (multiple scenarios for specific events). Tests and sensitivity analysis have been performed using instances related to a GIS of the Italian Sicily region and a typical set of facilities and rescue team. The simulation system works considering the different typologies of vehicles and staff to choose the best solution available in that specific time. The work is part of a national research project aimed to develop cloud-based systems and sensor networks for multi-risk management. The developed simulation system can provide crucial help to the rescuers in order to planning the best relief strategy for mitigate the effects of natural and industrial disasters