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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.

Effectively managing a supply chain requires visibility to detect unexpected variations in the dynamics of the supply chain environment at an early stage. This paper proposes a methodology that captures the dynamics of the supply chain, predicts and analyzes future behavior modes, and indicates potentials for modifications in the supply chain parameters in order to avoid or mitigate possible oscillatory behaviors. Neural networks are used to capture the dynamics from the system dynamic models and analyze simulation results in order to predict changes before they take place. Optimization techniques based on genetic algorithms are applied to find the best setting of the supply chain parameters that minimize the oscillations. A case study in the electronics manufacturing industry is used to illustrate the methodology.

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