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.

Purpose: Frequent sudden-onset disasters which have threatened the survival of human and the development of society force the public to pay an increasing attention to emergency management. A challenging task in the process of emergency management is emergency dispatch of reliefs. An emergency dispatch model considering the urgency of the requirement for reliefs in different disaster areas is proposed in this paper to dispatch reliefs reasonably and reduce the effect of sudden-onset disasters. Design/methodology/approach: Firstly, quantitative assessment on the urgency of the requirement for reliefs in different disaster areas is done by an evaluation method based on Fuzzy Comprehensive Evaluation and improved Evidence Reasoning which is proposed in this paper. And then based the quantitative results, an emergency dispatch model aiming to minimize the response time, the distribution cost and the unsatisfied rate of the requirement for reliefs is proposed, which reflects the requests of disaster areas under emergency, including the urgency of requirement, the economy of distribution and the equity of allocation. Finally, the Genetic Algorithm is improved based on the adaptive crossover and mutation probability function to solve the emergency dispatch model. Findings and Originality/value: A case that the Y hydraulic power enterprise carries on emergency dispatch of reliefs under continuous sudden-onset heavy rain is given to illustrate the availability of the emergency dispatch model proposed in this paper. The results show that the emergency dispatch model meets the distribution priority requirement of disaster area with the higher urgency, so thatreliefs are supplied more timely. Research limitations/implications: The emergency dispatch model faced to large scale sudden-onset disasters is complex. The quantity of reliefs that disaster area requires and the running time of vehicles are viewed as available information, and the problem that how to obtain the information is not considered. Practical implications: The emergency dispatch model considering the urgency of the requirement for reliefs in different disaster areas is applicable to a realistic emergency dispatch of reliefs under sudden-onset disasters and the research results is the foundation of further research on the problem of emergency management. Originality/value: This paper proposes emergency dispatch model considering the urgency of the requirement for reliefs in different disaster areas which can meet the realistic requirement that the disaster area with the higher urgency has an priority to obtain the reliefs and make reliefs be distributed timely.