Abstract The consultant assignment and routing problem is to simultaneously assign consultant supplies to client demands and determine the best traveling routes for consultants. Constraints to be considered include skill requirement, capacity limitation, fixed demand, and a maximum number of travel legs. This paper further takes into consideration priority matching, which restricts that clients can only be assigned to consultants with appropriate priority levels. In order to solve this problem, we present a decomposition algorithm named RMIP and a MIP-based neighborhood search algorithm. In addition, we extend an existing MILP formulation and compare our algorithms with it. The effectiveness and efficiency of the proposed algorithms are evaluated on a hundred synthetic instances and twelve real-life instances. Computational results show that the modified MILP formulation is only suitable for solving small-scale instances and a part of the medium-scale instances. For large-scale and real-life instances, the proposed two algorithms are significantly superior to the MILP formulation in solution quality and computational time.

中文翻译：

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具有优先级匹配的顾问分配和路由问题

摘要 顾问指派与路径问题是根据客户需求同时分配顾问用品，确定顾问的最佳出行路线。要考虑的约束条件包括技能要求、容量限制、固定需求和最大旅行段数。本文进一步考虑了优先级匹配，这限制了客户只能分配给具有适当优先级的顾问。为了解决这个问题，我们提出了一种名为RMIP的分解算法和一种基于MIP的邻域搜索算法。此外，我们扩展了现有的 MILP 公式并将我们的算法与其进行了比较。在一百个合成实例和十二个真实实例上评估了所提出算法的有效性和效率。计算结果表明，改进的 MILP 公式仅适用于解决小规模实例和部分中等规模实例。对于大规模和现实生活中的实例，所提出的两种算法在解决方案质量和计算时间方面明显优于 MILP 公式。