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组合问题在当今世界的许多领域都无处不在：提供优化的解决方案可以在许多领域带来可观的经济效益，例如物流，包装，设计和调度（人员或流程）。在一个典型的场景中，实例（例如，一组要交付的货物）经常以连续的流到达，并且需要快速生成解决方案。虽然有许多众所周知的方法来开发优化算法，但大多数都存在一个问题，这个问题现在在人工智能的范围内变得越来越明显：系统仅限于在与设计过程中遇到的数据类似的数据上表现良好，并且在遇到原始编程之外的情况时无法适应。如果优化人员在一次性过程中进行培训，然后部署，则系统保持静态，尽管优化发生在动态世界中，不断变化的实例特征，不断变化的用户需求以及影响解决方案质量的操作环境变化（例如工厂故障或城市交通）。这种变化可能是渐进的，也可能是突然的。在最好的情况下，这会导致系统提供次优性能，而在最坏的情况下，系统完全不适合目的。此外，一个不适应的系统浪费了一个明显的机会，随着时间的推移，它解决了越来越多的实例，以提高自己的性能。本提案的目标突破是开发一个动态优化系统，不断适应其运行机制和算法随着时间的推移，以保持适合的目的-从目前的一次性设计和部署方法的优化器设计的根本转变。系统将：-超越简单的被动反应，成为主动的，因为它将预测即将到来的实例的性质，并推测潜在的未来场景。响应于这些预测，它将自主地预生成和/或重新配置合适的算法，然后创建从实例到求解器的适当映射，以便为这些未来场景做好准备。它还将响应用户的请求，根据用户对自己业务和行业的深入了解，生成具有特定特征的实例和与之匹配的求解器。随着时间的推移，自动改进自己的行为，不断更新算法和方法，因为它从解决越来越多的实例的经验中学习。通过使用各种算法组合，支持针对多个用户目标和要求的优化，从在很短时间内生成可接受的解决方案到运行时间长但提供最高质量的解决方案。为了取得成功，我们将在构建主动，持续自适应系统和优化/算法选择方面取得新的进展，通过与机器学习的最新工具集成来增强。任何试图在动态环境中优化流程的企业都将受益，在动态环境中，客户需求变化，业务需求变化，运营环境会发生意外变化。相关的应用领域包括（但不限于）劳动力调度、物流和基础设施设计

Combinatorial problems are ubiquitous across many sectors in today's world: delivering optimised solutions can lead to considerable economic benefits in many fields such as logistics, packing, design and scheduling (of either people or processes). In a typical scenario, instances (for example, a set of goods to deliver) arrive frequently in a continual stream and a solution needs to be quickly produced. Although there are many well-known approaches to developing optimisation algorithms, most suffer from a problem that is now becoming apparent across the breadth of Artificial Intelligence: systems are limited to performing well on data that is similar to that encountered in their design process, and are unable to adapt when encountering situations outside of their original programming.For real-world optimisation this is particularly problematic. If optimisers are trained in a one-off process then deployed, the system remains static, despite the fact that optimisation occurs in a dynamic world of changing instance characteristics, changing user-requirements and changes in operating environments that influence solution quality (e.g. breakdowns in a factory or traffic in a city). Such changes may be either gradual, or sudden. In the best case this leads to systems that deliver sub-optimal performance, while at worst, systems that are completely unfit for purpose. Moreover, a system that does not adapt wastes an obvious opportunity to improve its own performance over time as it solves more and more instances.The targeted breakthrough of this proposal is to develop a dynamic optimisation system that continually adapts its operating mechanism and its algorithms over time to remain fit-for-purpose - a radical switch from the current one-off design and deployment approach to design of optimisers. The system will:- Go beyond simply being reactive to being proactive in that it will predict the nature of upcoming instances and speculate about potential future scenarios. In response to these predictions, it will autonomously pre-generate and/or reconfigure suitable algorithms, followed by creation of appropriate mappings from instance to solver, in order to pre-prepare for these future scenarios. It will also respond to user requests to generate instances with specific characteristics and solvers to match them, based on the user's in-depth knowledge of their own business and sector.- Autonomously improve its own behaviour over time, continually updating its algorithms and methods as it learns from its experience of solving more and more instances.- Support optimisation with respect to multiple user objectives and requirements via its use of a diverse portfolios of algorithms, that range from those which generate acceptable solutions in a very short time to those that have long running time but deliver the highest possible quality.To succeed we will make novel advances in building proactive, continually self-adapting systems and in optimisation/algorithm-selection, enhanced by integration with the latest tools from machine-learning. Benefits will be realised by any business that attempts to optimise their processes in dynamic environments, in which customer demands vary, business requirements change, and the operating environment is subject to unexpected changes. Relevant application domains include (but are not limited to) workforce scheduling, logistics and infrastructure design