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To gain competitive leverage, firms that design and develop complex products seek to optimize the organization structure in their new product development (PD) projects. The overlapped process is a fundamental feature of concurrent engineering, which not only reduces project duration but also increases coordination, communication, and interaction between organizational units. In order to reduce complexity of coordination, this paper proposes a design structure matrix (DSM) method for measuring interaction strength and clustering organizational units. This paper analyzes the impact of overlapping on interaction strength between teams performing these overlapped activities. Further, evolution DSM and sensitivity DSM are constructed for representing evolution degree and sensitivity degree. Based on the concepts of overlapping ratio and relative coordination frequency, this paper proposes a quantitative model using the evolution DSM and sensitivity DSM to measure the interaction strength between teams performing overlapped activities. The two-stage clustering criterion model is proposed for clustering numerical DSM, of which the first-stage clustering criterion is the maximization of the added average interaction strength of the selected organizational units and the second-stage clustering criterion is the minimization of the total coordination time of the PD project. An industrial example is provided to illustrate the proposed model. Results indicate that the clustered numerical DSM can reduce coordination time significantly. The model yields and reinforces several managerial insights, including: how to analyze the interaction strength based on overlapping, the impact of interaction strength on clusters and coordination time.Index Terms-Concurrent engineering, clustering analysis, design structure matrix (DSM), interaction strength, organization design, overlapping, product development (PD), project management.

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In wireless sensor networks, optimizing the network lifetime is an important issue. Most of the existing works define network lifetime as the time when the first sensor node exhausts all of its energy. However, such time is not necessarily important. This is because when a sensor node dies, the whole network is likely to work properly. In this article, we first make an overall consideration of the demand of applications and define the network lifetime in three aspects. Then, we construct a performance evaluation framework for routing protocols. To achieve the optimization of network lifetime in all defined aspects, we propose a reinforcement-learning-based routing protocol. Reinforcement-learning-based routing protocol takes advantage of the intelligent algorithm of reinforcement learning to search for the optimal routing path for data transmission. In the definition of reward function, factors such as link distance, residual energy, and hop count to the sink are taken into account to cut down the total energy consumption, balance the energy consumption, and improve the packet delivery. Simulation results demonstrate that compared with energy-aware routing, BEER, Q-Routing, and MRL-SCSO, reinforcement-learning-based routing protocol optimizes the network lifetime in three aspects and improves the energy efficiency.

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