Adaptive Large Neighborhood Search with a Constant-Time Feasibility Test for the Dial-a-Ride Problem
针对拨召问题，提出一种自适应大邻域搜索算法，其核心创新是能在常数时间内精确判断请求插入是否可行，从而找到更优的车辆路线方案。
In the dial-a-ride problem, user-specified transport requests from origin to destination points have to be served by a fleet of homogeneous vehicles. The problem variant we consider aims at finding a set of minimum-cost routes satisfying constraints on vehicle capacity, time windows, maximum route duration, and maximum user ride times. We propose an adaptive large neighborhood search (ALNS) for its solution. The key novelty of the approach is an exact amortized constant-time algorithm for evaluating the feasibility of request insertions in the repair steps of the ALNS. In addition, we use two optional improvement techniques: a local-search-based intraroute improvement of routes of promising solutions using the Balas–Simonetti neighborhood and the solution of a set covering model over a subset of all routes generated during the search. With these techniques, the proposed algorithm outperforms the state-of-the-art methods in terms of solution quality. New best solutions are found for several benchmark instances. The online appendix is available at https://doi.org/10.1287/trsc.2018.0837 .