Ensuring energy security, stopping climate change, and improving urban air quality are the three main challenges of this century that are being addressed by governments globally. The transportation sector contributes significantly to these issues. Sustainable transport involves the consideration of three dimensions—economic development, environmental preservation, and social development—and one of its aspects is the focus on the transition towards electric vehicles. The sustainable transportation is a complex system with multiple relationships and feedback between their elements. Understanding this complexity in an integrated and holistic manner is a challenge that must be addressed. To obtain insight into this complexity, we perform a systematic review of studies in which the demand and supply components of the passenger transportation sector were modelled in an integrated manner along with the dimensions of sustainability. All the reviewed previous studies had taken into consideration both the economic and environmental dimensions; however, only a few had also considered social development. The holistic and systematic integration of the dimensions of sustainability along with their relationships and feedback would facilitate a better understanding of the transportation sector and promote the development of better policies for improving the diffusion of passenger electric vehicles.

US manufacturers’ capabilities have consistently been degrading over the past few decades with no end in sight. To ebb this tide and assist the decision-makers in designing better configurations and system capabilities, this paper proposes the use of smart manufacturing systems (SMS) to develop a framework that generates and evaluates pre-implementation near optimal manufacturing configurations and provide guidance to establishing appropriate information messaging protocols between system components using hybrid simulation modelling. The framework calls for the implementation of computer-based tools including an expert system to identify the required machines and their corresponding characteristics, a business process modelling and notation tool to capture the process plan in an object-oriented fashion and an agent unified modelling language tool to guide the application of message sequence diagram and state charts, in addition, a hybrid simulation tool to assist in determining the correct configuration, an agent-based modelling tool to capture the behaviour of the required machines and a discrete event simulation tool to mimic the process flow. The application of the proposed framework to a manufacturing case study shows positive outcomes to support upper management in the planning phase of establishing a SMS or evaluating an existing one.

An agent-based modeling framework is developed and employed to replicate the interactions among urban farms. The objectives are to efficiently manage an urban farm’s food, energy, and water resources, decrease food waste, and increase the food availability for the local community. A case study of eleven farms was investigated in Vancouver, Canada to study the linkages between the resources in the urban food, energy, and water nexus. Each urban farm in the simulation belonged to a community microgrid generating electricity from solar and wind. The local farms aimed to provide fresh produce for their respective local communities. However, at some points, they lacked supply, and at other points, there was excess supply, leading to food waste. Food waste can be converted into fertilizers or bioenergy. However, an alternative solution must be employed due to the natural resources required for production, efficiently managing resources, and adhering to sustainability guidelines. In this paper, an optimization framework was integrated within the agent-based model to create a micro supply chain. The supply chain directly linked the producers with the consumers by severing the links involved in a traditional food supply. Each urban farm in the study collaborated to reduce food wastage and meet consumer demands, establishing farmer-to-farmer exchange in transitional agriculture. The optimization-based micro supply chain aimed to minimize costs and meet the equilibrium between food supply and demand. Regular communication between the farms reduced food waste by 96.9% over 16 weeks. As a result, the fresh food availability increased for the local community, as exemplified by the consumer purchases over the same period. Moreover, the simulation results indicated that the renewable energy generation at the community microgrids aided in the generation of 22,774 Mwh from solar and 2568 Mwh from wind. This has the potential to significantly reduce CO₂ emissions in areas that heavily rely on non-renewable energy sources.

In this research study, we investigate the ability of deep learning neural networks to provide a mapping between features of a parallel distributed discrete-event simulation (PDDES) system (software and hardware) to a time synchronization scheme to optimize speedup performance. We use deep belief networks (DBNs). DBNs, which due to their multiple layers with feature detectors at the lower layers and a supervised scheme at the higher layers, can provide nonlinear mappings. The mapping mechanism works by considering simulation constructs, hardware, and software intricacies such as simulation objects, concurrency, iterations, routines, and messaging rates with a particular importance level based on a cognitive approach. The result of the mapping is a synchronization scheme such as breathing time buckets, breathing time warp, and time warp to optimize speedup. The simulation-optimization technique outlined in this research study is unique. This new methodology could be realized within the current parallel and distributed simulation modeling systems to enhance performance.