A decision support system is designed in this paper for supporting the adoption of green logistics within scheduling problems, and applied to real-life services cases. In comparison to other green logistics models, this system deploys time-varying travel speeds instead of a constant speed, which is important for calculating the CO2 emission accurately. This system adopts widely used instantaneous emission models in literature which can predict second-by-second emissions. The factors influencing emissions in these models are vehicle types, vehicle load and traffic conditions. As vehicle types play an important role in computing the amount of emissions, engineers vehicles number plates are mapped to specified emission formulas. This feature currently is not offered by any commercial software. To visualise the emissions of a planned route, a Heat Map view is proposed. Furthermore, the differences between minimising CO2 emission compared to minimising travel time are discussed under different scenarios. The field scheduling problem is formulated as a vehicle routing and scheduling problem, which considers CO2 emissions in the objective function, heterogeneous fleet, time window constraints and skill matching constraints, different from the traditional time-dependent VSRP formulation. In the scheduler, this problem is solved by metaheuristic methods. Three different metaheuristics are compared. They are Tabu search algorithms with random neighbourhood generators and two variants of Variable Neighbourhood search algorithms: variable neighbourhood descent (VND) and reduced variable neighbourhood search (RVNS). Results suggest that RVNS is a good trade-off between solution qualities and computational time for industrial application.

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