组合重优化问题规约的自动生成：一项愿景
Automatic Generation of Combinatorial Reoptimisation Problem Specifications: A Vision
AI总结：
针对优化问题解需随上下文调整的重优化挑战，提出利用MDE从原始问题规约系统推导组合重优化问题，给出问题与策略分类及基于GIPS工具的概念验证实现。
摘要：
当一个优化问题被求解后，若上下文因素发生变化，其解可能需要调整。这一挑战也被称为重优化，已在铁路乘务员重新排班、护士重新排班或飞机恢复等多个问题领域得到研究。该挑战需要重新求解一个修改后的问题，以确保调整后的解在新的上下文下是最优的。然而，新的优化问题与原始问题存在显著差异：（i）我们希望仅对原始解进行最小程度的修改，以降低影响；（ii）我们可能无法更改原始解的某些部分（例如，因为它们涉及过去的分配）；（iii）我们需要推导从原始解到新解的变更脚本。本文提出，模型驱动工程（Model-Driven Engineering, MDE）——尤其是使用声明式建模语言和模型转换来对优化问题进行高层规约——为从原始优化问题规约系统推导重优化问题提供了新的机遇。我们聚焦于组合重优化问题，对变化的问题及推导对应重优化规约的策略进行了初步分类。我们介绍了基于GIPS（基于图的（混合）整数线性规划问题规约）工具的初步概念验证实现，并将其应用于一个示例资源分配问题：为教学场次分配助教。
英文摘要：
Once an optimisation problem has been solved, the solution may need adaptation when contextual factors change. This challenge, also known as reoptimisation, has been addressed in various problem domains, such as railway crew rescheduling, nurse rerostering, or aircraft recovery. This requires a modified problem to be solved again to ensure that the adapted solution is optimal in the new context. However, the new optimisation problem differs notably from the original problem: (i) we want to make only minimal changes to the original solution to minimise the impact; (ii) we may be unable to change some parts of the original solution (e.g., because they refer to past allocations); and (iii) we need to derive a change script from the original solution to the new solution. In this paper, we argue that Model-Driven Engineering (MDE) - in particular, the use of declarative modelling languages and model transformations for the high-level specification of optimisation problems - offers new opportunities for the systematic derivation of reoptimisation problems from the original optimisation problem specification. We focus on combinatorial reoptimisation problems and provide an initial categorisation of changing problems and strategies for deriving the corresponding reoptimisation specifications. We introduce an initial proof-of-concept implementation based on the GIPS (Graph-Based (Mixed) Integer Linear Programming Problem Specification) tool and apply it to an example resource-allocation problem: the allocation of teaching assistants to teaching sessions.
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