Production Scheduling Of Process Industry Based On Self-adaptive Collaborative Optimization Algorithm

Production scheduling,as the core of manufacturing and management of process enterprises,is of great importance for improving the enterprise's comprehensive competitiveness and economic effectiveness.Production scheduling of process industry is a typical issue of NP-hard optimization,which has complexity,multi-biding and multi objectives,thus an effective and feasible optimization algorism is needed to solve the problem.And Collaborative optimization is a newly emerged multi-subject optimization design algorism that can be used to break down the complex process scheduling,lower the complexity of the system and result reduce the difficulty of solving the problem,which is of high application value in the field of production scheduling in the process industry.This article's main research content is as follows:(1)Collaborative optimization algorithm is sensitive to the choice of initial point and is prone to converge on a local optimum.A self-adaptive collaborative optimization algorithm is proposed with a view to overcoming such inconveniences.Firstly,a collaborative non-uniformity is introduced at the system level to improve the dynamic slack factor such that the optimization design point converges rapidly on the extreme point.Secondly,at the disciplinary level,the uniformity objective function and the sub-disciplinary optimal objective function are added up,by use of dynamic weightings,and the sum is treated as the sub-disciplinary objective function,hence giving allowance for uniformity as well as sub-discipline independence.Finally,a two-stage optimization process is utilized and,in the final phase of iteration,both the dynamic slack factor and the sub-disciplinary optimal objective function are removed to prevent excessive oscillation in the convergence process.A simulation was performed on classical cases,and the optimization results attested to the insensitivity of this proposed algorithm to the choice of initial point,its significant optimization efficiency improvement,and its good robustness.(2)As for the production scheduling problem,the MILP model of process industry based on discrete time is established and applied in the seven-day production scheduling practice in the saccharification-brewing workshop in a brewery company.With the SCO algorism,the model is broken down into two sub disciplines,seven one-day production scheduling of saccharification workshops and a seven-day production scheduling of brewing workshop,and use the genetic algorism to resolve the discipline level and system level SCO algorism respectively.Through the simulation and analysis of this case,it verified the reasonability of the model and the feasibility and effectiveness of the SCO algorism when used to resolve the production scheduling of process industry.