Compared to fossil fuel-based internal combustion vehicles, electric vehicles with lower local pollution and noise are becoming more and more popular in urban logistic distribution. When electric vehicles are involved, high-quality delivery depends on energy consumption. This research proposes an electric vehicle routing problem considering time windows under energy consumption uncertainty. A mixed-integer programming model is established. The robust optimization method is adopted to deal with the uncertainty. Based on the modification of adaptive large neighborhood search algorithm, a metaheuristic procedure, called novel hybrid adaptive large neighborhood search, is designed to solve the problem, and some new operators are proposed. The numerical experiments show that the proposed metaheuristic can obtain high-performance solutions with high efficiency for large-scale instances. Furthermore, the robust solution based on the proposed model can achieve a satisfactory tradeoff between performance and risk.

This paper considers the problem that a depot replenishes several shelters by aerial and land transportation modes for disaster relief. There are two distinguishing features of our problem: one is routing decisions determine replenishment lead times; the other is that we introduce dual sourcing policy into the inventory routing problem. A robust optimization model is proposed to determine the optimal replenishment quantity, replenishment mode, and transportation routes. Then, we decompose the problem into a routing master-problem and a set of inventory sub-problems. A tractable closed-form solution for sub-problem is derived. We further develop an adaptive large neighborhood search algorithm to solve the problem. To demonstrate the feasibility of the algorithm, we conduct a series of numerical experiments on the benchmark test suite with different scales and compare the performance of the proposed algorithm with a genetic algorithm.

Traditional inventory routing problems ignore the time consumption in transportation. In this paper, an inventory routing problem with air–land transportation and route-dependent lead times is studied. The model is based on the rolling horizon framework which can serve as a “here-and-now” approximation for multi-period inventory routing problems. A planning horizon crossing strategy is proposed to consider the effect of the single-period decision on long-term planning. The box uncertainty set is used to depict demands. A tractable closed-form robust solution for optimal replenishment quantity is derived. An adaptive variable neighborhood search algorithm is developed for this problem. A novel shaking phase is proposed, and the performance of shaking operators is evaluated in numerical experiments. Results also validate the effectiveness of the robust solution and the planning horizon crossing strategy.