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This paper introduces a new rich vehicle routing problem faced by companies that consider alternative-fuel vehicle (AFV) adoption into a service fleet consisting of gasoline or diesel vehicles. The service operation addressed here differs from delivery operations in that a vehicle has to stop for extended periods of time while its driver serves customers. We discuss measuring the impact of AFV adoption on fleet operations from multiple perspectives and formulate four objective functions to represent the defined performance metrics in a generalized mixed-integer linear programming model. The model can accommodate various AFV types with respect to driving range, refueling time, and availability of refueling stations. We develop a variable neighborhood search heuristic to solve large-scale problems efficiently. Results from the research show that the classical vehicle routing objective of minimizing total vehicle miles traveled does not work well in this emerging problem; instead, an objective such as minimizing carbon emissions or fuel costs provides more desirable solutions. The results also show that in service fleets, refueling time has lesser impact on fleet performance compared to service station availability or vehicle range. From a managerial standpoint, this indicates that investment in range extension or establishing service stations is more important than investment in faster refueling capability.

Besides emotional symptoms ADH symptoms had a significant predictive value on mindfulness.

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Green, or, alternative-fuel vehicles provide opportunities to improve fleet operators' financial and environmental bottom lines. They also present challenges due to limited driving ranges, limited refueling infrastructure, and lengthy refueling times. This paper addresses a green vehicle routing problem of homogeneous service fleets with stated challenges, and develops an Iterated Beam Search algorithm for its solution. The developed algorithm can employ different lower and upper bounding strategies, and can work as an exact or a heuristics algorithm. A dominance strategy is also developed to improve efficiency of the algorithm. Computational experiments on various-size test instances show that the algorithm's performance is comparable to existing approaches.