Integrated decision-making for repair order acceptance and service resource allocation that distinguishes between in-warranty and out-of-warranty

Manufacturers classify customers into in-warranty and out-of-warranty customers based on their warranty status at the time of product failure. However, previous studies on the decision-making optimization of orders and service resources for repair services ignored the differences between in-warranty and out-of-warranty customers, which will affect the implementation of manufacturers' repair service businesses. Therefore, this paper proposes an integrated decision-making model for order acceptance and resource allocation for repair services that distinguishes between in-warranty and out-of-warranty and considers the manufacturer's limited service resources. Considering the conflicts of interest between the manufacturer, in-warranty and out-of-warranty customers, the model aims to maximize the satisfaction of in-warranty and out-of-warranty customers and the manufacturer's profit. According to the expectations of different customers and the benefits of the manufacturer, the optimal decision scheme can be derived by determining the accepted orders, adjusting the resource allocation scheme and the service sequence of the accepted orders. In addition, to obtain the optimal decision scheme more efficiently, a memetic algorithm based on the nondominated neighbor immune algorithm is proposed, in which two local search operators are designed to improve the search efficiency. Three hundred instances were used to test this model, and extensive experimental results show that the proposed algorithm is more effective than other widely used algorithms. Finally, the benefits that the proposed integrated decision-making model can bring to manufacturers are demonstrated through numerical experiments.

Automated summary

This paper proposes an integrated decision-making model for order acceptance and resource allocation for repair services, considering the differences between in-warranty and out-of-warranty customers. It aims to maximize the satisfaction of customers and the profit of manufacturers. A memetic algorithm is introduced to efficiently obtain the optimal decision scheme.