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

这个项目的主要目标是开发一个模型来预测从一组算法中哪一个最适合解决组合优化问题的不同实例。Tesco的大量业务，如送货计划、车辆路线问题和配送系统，都涉及到组合优化。本模型将设计满足这类问题要求的大规模零售问题。虽然这些问题已经得到了广泛的研究，但哪种算法在特定实例或实例类上表现最好仍然没有解决。问题可以使用不同的特征来表征，我们将对这些特征与不同启发式算法的性能之间的关系进行建模。我们还将探索这些具有随时间变化的动态特征的问题的模型。该项目将开发新的分析方法，以帮助解释算法在不同问题实例中的性能。所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，以及何时我们可能期望提供的解决方案不可行的或质量低。与乐购合作。

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.