A Prediction Model for Algorithm Selection in Solving Combinatorial Optimisation Problems.

解决组合优化问题的算法选择的预测模型。

• 批准号: 2608381
• 金额: --
• 依托单位: Lancaster University
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2608381
• 关键词: Prediction; Model; Algorithm; Selection; Solving

The main objective of this project is to develop a model to predict which from a set of algorithms is most suitable for solving different instances of combinatorial optimisation problems. A large number of Tesco operations, such as delivery planning, vehicle routing problems and distribution systems, involve combinatorial optimisation. This model will be designed to meet the requirements of such problems large-scale retail problems. While these problems have been widely studied, the decision about which algorithm performs best on a particular instance, or class of instances is still unresolved. Problems can be characterised using different features, and we will model the relationship between such features and the performance of different heuristic algorithms. We will also explore these models for problems with dynamic features that change with time. The project will develop new analysis methods to help explain the performance of the algorithms for different problem instances. The techniques developed will be able to explain to decision-makers under which conditions we can expect those algorithms to provide trustworthy solutions and when we may expect that the solutions provided to be infeasible or of low quality. In partnership with Tesco.

该项目的主要目标是开发一个模型，以预测从一组算法是最适合解决组合优化问题的不同情况。大量的乐购业务，如交付计划，车辆路径问题和分销系统，涉及组合优化。该模型的设计将满足此类问题的要求大型零售问题。虽然这些问题已经被广泛研究，但关于哪种算法在特定实例或实例类上表现最好的决定仍然没有得到解决。问题的特点可以使用不同的功能，我们将模拟这些功能和不同的启发式算法的性能之间的关系。我们还将探索这些模型，以解决随时间变化的动态特征问题。该项目将开发新的分析方法，以帮助解释不同问题实例的算法性能。开发的技术将能够向决策者解释在哪些条件下我们可以期望这些算法提供值得信赖的解决方案，以及何时我们可以期望提供的解决方案是不可行的或低质量的。与Tesco合作。