.png){kind=icon}
返回
An Adaptive Memory Programming Framework for the Robust Capacitated Vehicle Routing Problem
.png){kind=icon}
PRE
AI
DOI：10.1287/trsc.2014.0559.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
We present an adaptive memory programming (AMP) metaheuristic to address the robust capacitated vehicle routing problem under demand uncertainty. Contrary to its deterministic counterpart, the robust formulation allows for uncertain customer demands, and the objective is to determine a minimum cost delivery plan that is feasible for all demand realizations within a prespecified uncertainty set. A crucial step in our heuristic is to verify the robust feasibility of a candidate route. For generic uncertainty sets, this step requires the solution of a convex optimization problem, which becomes computationally prohibitive for large instances. We present two classes of uncertainty sets for which route feasibility can be established much more efficiently. Although we discuss our implementation in the context of the AMP framework, our techniques readily extend to other metaheuristics. Computational studies on standard literature benchmarks with up to 483 customers and 38 vehicles demonstrate that the proposed approach is able to quickly provide high-quality solutions. In the process, we obtain new best solutions for a total of 123 benchmark instances.
Keyword:
vehicle routing
robust optimization
adaptive memory programming
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
4.8
论文数:
1.9K
被引数:
8.4K
机构
引用论文
.png){kind=icon}
Critical Evaluation and Thermodynamic Optimization of the Li2O-Al2O3 and Li2O-MgO-Al2O3 Systems
PREAI
分享收藏
查看更多内容