Research On Collaborative Manufacturing Execution Platform And Key Techniques For Project Production

Manufacturing Execution System (MES) is considered as the bridge between ERP system of enterprise level and PCS system of job shop, which has achieved the switch from production management to production control in a manufacturing enterprise. With the development of MES techniques, project production enterprises, which with characteristic of unitary and little batch, have applied domain production management and scheduling system as well as. Recently, as drastic market competition drives project production enterprises to combine the develop mode and batch one, enterprises get higher requirement on integration, collaboration and agility of MES. Thus, it is an important problem to how to effectively construct the MES, which meet requirements of project production.According to trends of MES, features and requirements of project production was researched based on the home and aboard research work of manufacturing execution platform and and its key technologies. Computer Integration Manifacturing technology and artificial intelligence technology are applied in project production process to resolve the problems of long production period, high rejection rate and cost, and improve the parallelism of development and batch production. The main points are summarized as follows:The architecture of collaborative manufacturing execution platform based on multi-agent was established. In order to realize integration and cooperation of project production process, the multi-agent technology was introduced. A multi-agent distributed self-control mode was designed for manufacturing system. Moreover, the agent structure, communication mode and cooperation model were proposed. To support the architecture of collaborative manufacturing execution platform, the organizational pattern of manufacturing resources on VMC and Web Services and the expression pattern of manufacturing informantion on PSL and XML were designed.The ontology-based domain knowledge integration of project production was reaserched. To meet the requirement of optimization integration and reuse of knowledge, the model and ontology-based formal expression of domain knowledge were introduced. Moreover, a knowledge integration device based on ontology of design knowledge, which consists of ontology construction, ontology mapping and ontology merging at the core, was proposed. To achieve the ontology integration process, an algorithm of ontology mapping was designed. Finally, an application case was presented to illustrate the validity of the integration mechanism.The Business coordination and conflict resolution methods for project production were studied. In order to resolve the problem of pre-project coordination, a case-based reasoning task decomposition method and contract net-based task allocation protocol were introduced. Futhermore, a hybrid access control model was presented to resolve business conflicts caused by incompatible information resource sharing. The HAC model, which combines the PBAC model with the role-based one, limits the operation to public resource in a concrete task, to guarantee the confidentiality and the integrality of the collaborative environment. An authorized evaluation method was introduced for the resource access of across administration domains in collaborative environment, in order to judge, by the confidence value, whether to the dynamic authorization to users or not. Finally, the simulation experiment to the algorithm was carried on. The result of the simulation experiment meets requirements of the application so that the validity of the conflict resolution mechanism was proved.To deal with the problem of dynamic production scheduling in project production, a hybrid variable neighborhood search (H-VNS) algorithm was presented by combining dynamic characters of shop floor. Through apply the particle swarm optimization (PSO) to VNS, the low efficiency of local search was controlled and the local optimization was assured in neighborhood structure sets. In the scheduling period, the global optimization of solution was assured too by applying the improved VNS. In the rescheduling point, the analytic value of some important parameters in the impoved VNS was solved by imposing the trained B-P neural network. Finally, experimental results illustrate the effectiveness of the proposed hybrid algorithm in a variety of shop floor conditions.The prototype system is developed based on the above-mentioned key technologies and actual production requirements for the manufacturing execution platform, and the collaborative manufacturing execution platform is proved feasible and efficient.