Balancing time,cost,and quality is crucial in intelligent manufacturing.However,finding the optimal value of production parameters is a challengingnon-deterministic polynomial(NP)-hard problem.In the actual production...Balancing time,cost,and quality is crucial in intelligent manufacturing.However,finding the optimal value of production parameters is a challengingnon-deterministic polynomial(NP)-hard problem.In the actual production process,the production process has the characteristics of multi-stage parallel.Therefore,aiming at the difficult problem of multi-stage nonlinear production process optimization,this paper proposes a workflow optimization algorithm based on virtualization and nonlinear production quality under time constraints(T-OVQT).The algorithm proposed in this paper first abstracts the actual production process into a virtual workflow model,which is divided into three layers:The bottom production process collection layer,the middle layer of service node partial order composition layer,and the high level of virtual node collection layer.Then,the virtual technology is used to reconstruct the node set and divide the task interval.The optimal solution is obtained through inverse iterative normalization and forward scheduling,and the global optimal solution is obtained by algorithm integration.Experimental results demonstrate that this algorithm better meets actual production requirements than the traditional minimum critical path(MCP)algorithm.展开更多
基金supported by Heilongjiang Provincial Natural Science Foundation of China(LH2021F030)。
文摘Balancing time,cost,and quality is crucial in intelligent manufacturing.However,finding the optimal value of production parameters is a challengingnon-deterministic polynomial(NP)-hard problem.In the actual production process,the production process has the characteristics of multi-stage parallel.Therefore,aiming at the difficult problem of multi-stage nonlinear production process optimization,this paper proposes a workflow optimization algorithm based on virtualization and nonlinear production quality under time constraints(T-OVQT).The algorithm proposed in this paper first abstracts the actual production process into a virtual workflow model,which is divided into three layers:The bottom production process collection layer,the middle layer of service node partial order composition layer,and the high level of virtual node collection layer.Then,the virtual technology is used to reconstruct the node set and divide the task interval.The optimal solution is obtained through inverse iterative normalization and forward scheduling,and the global optimal solution is obtained by algorithm integration.Experimental results demonstrate that this algorithm better meets actual production requirements than the traditional minimum critical path(MCP)algorithm.
