In a closed-loop supply chain for electronic products, the manufacturer’s priority is to enhance the residual value of the collected end-of-use product and decide whether to outsource this business to a retailer, a third-party service, or retain it exclusively. In this paper, we constructed three models to study the decision mechanism in a closed-loop supply chain, with different players selected to collect the used product. By comparing the three models, we characterized the conditions under which the manufacturer will benefit most, and we then aimed to determine the best choice for the manufacturer. Our findings show that, when the retailer and the third-party service provider provide equal performance in collecting the used product, the manufacturer will give priority to the third-party service provider if they choose to outsource this business. If the reverse flows managed by the retailer result in a higher payoff for the manufacturer, then the manufacturer will choose to outsource this business to the retailer who will also benefit.

For the product R&D process, it is a challenge to effectively and reasonably assign tasks and estimate their execution time. This paper develops a method system for efficient task assignment in product R&D. The method system consists of three components: similar tasks identification, tasks' execution time calculation, and task assignment model. The similar tasks identification component entails the retrieval of a similar task model to identify similar tasks. From the knowledge-based view, the tasks' execution time calculation component uses the BP neural network to predict tasks' execution time according to the previous similar tasks and the Task-Knowledge-Person (TKP) network. When constructing the BP neural network, the satisfaction degree of knowledge and the execution time are set as the input and output, respectively. Considering the uncertain factors associated with the whole R&D process, the task assignment model component serves as a robust optimization model to assign tasks. Then, an improved genetic algorithm is developed to solve the task assignment model. Finally, the results of numerical experiment are reported to validate the effectiveness of the proposed methods.