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.

该项目的主要目的是开发一个模型，以预测哪种算法最适合解决组合优化问题的不同实例。大量的乐购行动，例如交付计划，车辆路线问题和配电系统，都涉及组合优化。该模型将旨在满足此类问题的要求大规模零售问题。尽管这些问题已被广泛研究，但关于哪种算法在特定实例上表现最佳的决定，或者尚未解决一类实例。可以使用不同的功能来表征问题，我们将建模此类功能与不同启发式算法的性能之间的关系。我们还将探索这些模型，以解决随着时间而变化的动态功能的问题。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的性能。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可信赖的解决方案，以及我们可能期望提供的解决方案是不可行的或低质量的。与乐购合作。