A sampling-based matheuristic for the continuous-time stochastic inventory routing problem with time-windows
研究了客户需求在周期内连续下降的随机库存路径问题，提出基于自适应大邻域搜索的数学启发式算法，通过抽样处理随机性，帮助供应商制定补货计划以降低总成本。
This article considers a continuous-time variant of the stochastic inventory routing problem. In most of the articles present in the literature related to inventory routing, customers reveal their demands at the end of each period, which is when the inventory level is calculated. In the variant of the problem at hand, the demand that each customer experiences on each period, results in a continuous decrease of the inventory for the customer during the period. This characteristic strongly affects the quantities that can be delivered to each customer and, if the deliveries are not sufficient or arrive too late, they can cause some stock-out situations within the periods. In the inventory routing problem, the vendor manages all the replenishment decisions of the vendees and, therefore, creates delivery plans for a planning horizon, aiming to reduce the routing, consistency, inventory and lost-sales costs. We formulate the problem as a two-stage mathematical program. Customers experience a continuous stochastic demand within each period and present information about inventory level and capacity at each period, as well as the time windows in which deliveries can take place. To solve this problem, we develop a matheuristic solution approach based on an adaptive large neighborhood search algorithm. In addition, we evaluate the impact of applying recourse actions to deal with expected lost sales during the planning horizon. We compare the performance of the algorithm with adapted variants of the multiple scenario approach and the branch and regret algorithms from the literature. Samples based on the stochastic demands are considered in the algorithms, to find robust solutions that can minimize the objective function. We evaluate the solutions by means of a sample average estimator procedure, and we compare the efficiency of the algorithms as well as the impact of different levels of stochasticity.