We study a bi-objective problem called the Minimum Latency-Distance Problem (mldp) that aims to minimise travel time and latency of a single-vehicle tour designed to serve a set of client requests. This tour is a Hamiltonian cycle for which we aim to simultaneously minimise the total travel time of the vehicle and the total waiting time (i.e., latency) of the clients along the tour. This problem is relevant in contexts where both client satisfaction and company profit are prioritise. We propose two heuristic methods for approximating Pareto fronts for mldp: SMSA that is based on a classic multi-objective algorithm and EiLS that is based on a novel evolutionary algorithm with intelligent local search. We report computational experiments on a set of artificially generated problem instances using an exact method and the two proposed heuristics, comparing the obtained fronts in terms of various quality metrics.

中文翻译：

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最小等待时间问题的双目标研究

我们研究了一个称为“最小延迟距离问题”（mldp）的双目标问题，该问题旨在最大程度地减少旨在满足一组客户要求的单车游览的旅行时间和延迟。这次旅行是一个汉密尔顿周期，我们的目标是在旅行中同时最小化车辆的总行驶时间和客户的总等待时间（即等待时间）。在客户满意度和公司利润都被优先考虑的情况下，此问题是相关的。我们提出了两种启发式方法来近似mldp的帕累托前沿：基于经典多目标算法的SMSA和基于具有智能本地搜索功能的新型进化算法的EiLS。我们使用一组精确的方法和两种提议的启发式方法，报告了一组人工生成的问题实例的计算实验，并根据各种质量指标对获得的前沿进行了比较。