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Current logistics and transportation (L&T) systems include heterogeneous fleets consisting of common internal combustion engine vehicles as well as other types of vehicles using "green" technologies, e.g., plug-in hybrid electric vehicles and electric vehicles (EVs). However, the incorporation of EVs in L&T activities also raise some additional challenges from the strategic, planning, and operational perspectives. For instance, smart cities are required to provide recharge stations for electric-based vehicles, meaning that investment decisions need to be made about the number, location, and capacity of these stations. Similarly, the limited driving-range capabilities of EVs, which are restricted by the amount of electricity stored in their batteries, impose non-trivial additional constraints when designing efficient distribution routes. Accordingly, this paper identifies and reviews several open research challenges related to the introduction of EVs in L&T activities, including: (a) environmental-related issues; and (b) strategic, planning and operational issues associated with "standard" EVs and with hydrogen-based EVs. The paper also analyzes how the introduction of EVs in L&T systems generates new variants of the well-known Vehicle Routing Problem, one of the most studied optimization problems in the L&T field, and proposes the use of metaheuristics and simheuristics as the most efficient way to deal with these complex optimization problems.

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By adopting multimodal logistics, global firms can significantly reduce lead time and inventory carrying costs, which may constitute major improvements in both profit and customer service. This paper presents dynamic programming formulations of multimodal logistics systems, which result in deterministic acyclic networks that may be solved using Dijkstra's algorithm. Using these models one can find both a least cost route and a least lead time route subjected to lead time and total cost, respectively. By combining these models, they may be used to make strategic decisions and to allow for system control in response to disruption. The presented model has both the analytical foundation and flexibility for application to a number of situations.Reference to this paper should be made as follows: Grasman, S.E. (2006) 'Dynamic approach to strategic and operational multimodal routing decisions', Int.