Allocation and routing of heterogeneous technicians in consideration of uncertain service results and revisits
研究了家电维修服务中，考虑初级技术人员可能维修失败需高级技术人员二次访问的情况下，如何分配和规划技术人员路径以最小化旅行和等待成本，提出基于图神经网络的混合价值函数近似方法。
Home appliance companies often offer after-sales services, especially repair services, to their customers. A critical challenge in this context is planning the allocation and routing of service technicians to satisfy customers’ service requests. In this paper, we consider technicians with two different skill levels. Senior technicians with extensive experience consistently handle repair tasks successfully, whereas junior technicians, owing to their limited experience, may fail to repair appliances, necessitating a follow-up visit by a senior technician to complete the service. Stochasticity of the problem stems from random service results and arrival of new customers. We formulate the problem as a sequential decision problem, where at the start of each working day, the information of unsatisfied customers is summarized, technicians are allocated to service tasks, and visiting routes are planned. The objective is to minimize the expected total discounted technicians’ travel and customers’ waiting costs. We propose an innovative hybrid value function approximation method, which is based on a graph neural network, that efficiently handles large decision spaces with candidate solutions and a hybrid genetic algorithm. Our approach outperforms two benchmarks in a comprehensive numerical study. We not only achieve a lower cost but also need less technician capacity. We illustrate that with our approach, a company can achieve large cost savings if customers accept a short waiting time.