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In this paper, we presented an ε-accurate approach to conduct a continuous optimization on the pollution routing problem (PRP). First, we developed an ε-accurate inner polyhedral approximation method for the nonlinear relation between the travel time and travel speed. The approximation error was controlled within the limit of a given parameter ε, which could be as low as 0.01% in our experiments. Second, we developed two ε-accurate methods for the nonlinear fuel consumption rate (FCR) function of a fossil fuel-powered vehicle while ensuring the approximation error to be within the same parameter ε. Based on these linearization methods, we proposed an ε-accurate mathematical linear programming model for the continuous PRP (ε-CPRP for short), in which decision variables such as driving speeds, travel times, arrival/departure/waiting times, vehicle loads, and FCRs were all optimized concurrently on their continuous domains. A theoretical analysis is provided to confirm that the solutions of ε-CPRP are feasible and controlled within the predefined limit. The proposed ε-CPRP model is rigorously tested on well-known benchmark PRP instances in the literature, and has solved PRP instances optimally with up to 25 customers within reasonable CPU times. New optimal solutions of many PRP instances were reported for the first time in the experiments.

The electric vehicle routing problem with time window (EVRPTW) is an extension of the traditional vehicle routing problem with time window (VRPTWs), where new features of electric vehicles are considered, such as limited battery capacities, lack of infrastructures, and long charging time. In this study, new technical formulations were presented for vehicle route selection and charging station visit, which reduces the formulation complexity without using duplicated dummy nodes or arcs. Besides, a new linearization method was developed that employs a set of secant lines to surrogate the concave nonlinear charging function with linear constraints. This method defines the charging time as a continuous variable and uses fewer variables than existing formulation in literature. A mixedinteger linear programming (MILP) model was developed for the EVRPTW and computational experiments on Solomon's VRPTW instances were conducted to verify the proposed model. The experimental results were compared with the results using traditional routing models, which showed that the proposed model can result in better EVs logistics schedules with higher charging time utilizations. One benchmark problem was updated with new best solution.

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