SHARING AND PLANNING OF DISTRIBUTED EQUIPMENT FOR NETWORKED MANUFACTURING

Efficient utilization of the equipment distributed in different enterprises and optimal allocation of these resources is an important concern for networked manufacturing. The third party based equipment sharing approach is put forward to optimize the utilization of distributed equipment for networked manufacturing; Taking advantage of the shared equipment offered by equipment providers by means of lease agreement, the third party carries out production by establishing networked virtual factory. Operational mechanism of the third party based equipment sharing is discussed, and characteristics of this approach in achieving resource allocation are analyzed. Shared equipment planning is formulated as an optimization problem with the objective of maximizing profits for equipment coordinator, a mathematical model for shared equipment planning is developed. Finally a case study is discussed to show the effectiveness of the planning model.

A deep reinforcement learning method for multi-stage equipment development planning in uncertain environments

Equipment development planning(EDP)is usually a long-term process often performed in an environment with high uncertainty.The traditional multi-stage dynamic programming cannot cope with this kind of uncertainty with unpredictable situations.To deal with this problem,a multi-stage EDP model based on a deep reinforcement learning(DRL)algorithm is proposed to respond quickly to any environmental changes within a reasonable range.Firstly,the basic problem of multi-stage EDP is described,and a mathematical planning model is constructed.Then,for two kinds of uncertainties(future capabi lity requirements and the amount of investment in each stage),a corresponding DRL framework is designed to define the environment,state,action,and reward function for multi-stage EDP.After that,the dueling deep Q-network(Dueling DQN)algorithm is used to solve the multi-stage EDP to generate an approximately optimal multi-stage equipment development scheme.Finally,a case of ten kinds of equipment in 100 possible environments,which are randomly generated,is used to test the feasibility and effectiveness of the proposed models.The results show that the algorithm can respond instantaneously in any state of the multistage EDP environment and unlike traditional algorithms,the algorithm does not need to re-optimize the problem for any change in the environment.In addition,the algorithm can flexibly adjust at subsequent planning stages in the event of a change to the equipment capability requirements to adapt to the new requirements.

Suggestion on 9th Five year and 2010 plan for transmission and substation equipment

China’s power transmission equipment industry has formed, through technical innovation and development in past several decades especially in the period from the "6th Five Year Plan" to "8th Five Year Plan", a capability in furnishing equipments for 15,000 MW yearly key power transmission, power networks and substations project with voltage up to 500 kV. These achievements

Weapon system portfolio selection based on structural robustness

The system portfolio selection is a fundamental frontier issue in the development planning and demonstration of weapon equipment.The scientific and reasonable development of the weapon system portfolio is of great significance for optimizing the design of equipment architecture,realizing effective resource allocation,and increasing the campaign effectiveness of integrated joint operations.From the perspective of system-ofsystems,this paper proposes a unified framework called structure-oriented weapon system portfolio selection(SWSPS)to solve the weapon system portfolio selection problem based on structural invulnerability.First,the types of equipment and the relationship between the equipment are sorted out based on the operation loop theory,and a heterogeneous combat network model of the weapon equipment system is established by abstracting the equipment and their relationships into different types of nodes and edges respectively.Then,based on the combat network model,the operation loop comprehensive evaluation index(OLCEI)is introduced to quantitatively describe the structural robustness of the combat network.Next,a weapon system combination selection model is established with the goal of maximizing the operation loop comprehensive evaluation index within the constraints of capability requirements and budget limitations.Finally,our proposed SWSPS is demonstrated through a case study of an armored infantry battalion.The results show that our proposed SWSPS can achieve excellent performance in solving the weapon system portfolio selection problem,which yields many meaningful insights and guidance to the future equipment development planning.