ABSTRACT

This article introduces a solution approach for the Stochastic Capacitated Vehicle Routing Problem (SCVRP) with uncertain demands, called Robust Simulation-Based (RoSi) approach. RoSi aims at designing route plans that can be more or less robust based on a decision-maker weight, i.e. solutions that resist demand changes with marginal additional (recourse) cost. For that, RoSi combines simulation with heuristics. It transforms a complex SCVRP into a set of deterministic ones, where well-known heuristics can be applied, computing a set of feasible solutions. These solutions are assessed by Monte Carlo simulation, and the one that deals better with demand fluctuation is selected as the final solution. The efficiency of RoSi is compared with those of three methods in the literature: Integer Linear Programming (ILP) model, Stochastic Programming with Recourse (SPR) model, and Robust Bi-Objective (RoBi) approach through numerical experiments. The results show that RoSi outperforms these methods in most scenarios.

摘要

本文介绍了一种具有不确定需求的随机电容式车辆路由问题 (SCVRP) 的解决方法，称为基于鲁棒模拟 (RoSi) 的方法。RoSi 旨在根据决策者的权重设计可以或多或少稳健的路线计划，即以边际附加（追索）成本抵抗需求变化的解决方案。为此，RoSi 将模拟与启发式相结合。它将复杂的 SCVRP 转换为一组确定性的 SCVRP，其中可以应用众所周知的启发式方法，计算一组可行的解决方案。这些解决方案通过蒙特卡罗模拟进行评估，并选择能够更好地应对需求波动的解决方案作为最终解决方案。RoSi 的效率与文献中的三种方法的效率进行了比较：整数线性规划 (ILP) 模型，具有追索权的随机规划 (SPR) 模型，以及通过数值实验实现的稳健双目标 (RoBi) 方法。结果表明，RoSi 在大多数情况下都优于这些方法。