Production Scheduling Research On Integrated Optimization Of Equipment Operation And Maintenance And Spare Parts Inventory

Intelligent production scheduling is the core of the production management of network cooperative manufacturing,and its decision-making scheme directly affects the service level,efficiency and cost of cooperative manufacturing.However,the existing production scheduling research is mainly oriented at the manufacturing stage of the product life cycle,which belongs to the optimization in the single manufacturing domain.This kind of production scheduling research in traditional manufacturing domain cuts off the relationship between the manufacturing stage and the operation and maintenance stage,and it is difficult to take into account the overall optimization of multi-dimensional resources in the whole process of the product life cycle.It has been unable to meet the strategic development requirements of modern new production modes such as intelligent manufacturing and network cooperative manufacturing in the new era.Manufacturing under the new mode emphasizes the idea of system engineering,which requires comprehensive consideration of the business activities at multiple stages of product life cycle,such as manufacturing,operation and maintenance,and puts forward new requirements and challenges to the existing research and methods of production scheduling theory.Therefore,under the guidance of the national network collaborative manufacturing strategy,this paper focuses on the research method of production scheduling for equipment operation and maintenance utility.From the perspective of system engineering,the production resources,spare parts inventory,equipment operation and maintenance are taken as a whole to carry out the overall optimization scheduling research.Through the link of “production scheduling optimization”,the integrated optimization of production resource utilization,spare parts inventory setting and equipment operation and maintenance services is realized.The main research contents of this dissertation are summarized as follows:1.In view of the disadvantages of the traditional serial and phased decisionmaking supply mode of “from production to inventory,to operation and maintenance”,which easily leads to a series of problems such as backlog waste of spare parts inventory and poor liquidity of enterprise funds,a parallel service mode of production resources and inventory for operation and maintenance spare parts inventory setting optimization is proposed.To obtain the optimal inventory setting under this model,the concept of virtual warehouse is first introduced,by which the inventory optimization problem under the parallel service mode is transformed into a production scheduling problem with the goal of minimizing the inventory capital occupation and minimizing the service cost in the later service cycle.According to the spare parts usage in each virtual warehouse in the optimal scheduling scheme,the spare parts inventory level of each type that should be set in each actual warehouse at the end of the previous service cycle can be determined.Secondly,an optimization algorithm based on the framework of NSGA-II for solving the model is proposed.Based on the model features,an improved idle time insertion method and five problem feature-guided local search operators are designed.Finally,the efficiency of the proposed algorithm and the effectiveness of the parallel service mode in reducing the inventory level compared with serial supply mode are verified by a large number of experiments.2.On the basis of content 1,a production scheduling model of parallel service of production resources and spare parts inventory to meet the spare parts demand of equipment operation and maintenance is constructed for the scenario where production resources and spare parts inventory coexist in the current service cycle.This model considers both the service cost of the manufacture side(also known as service manufacturer)and the capacity loss of complex equipment at the operation and maintenance side(also known as demand side)due to the delayed supply of spare parts.Based on the coupling relationship of “operation parameter--deterioration rate--operation efficiency” of complex equipment,an optimal speed adjustment strategy is constructed to minimize the operational capacity loss of the equipment of demand side.Then,four well-known algorithms with different architectures are adopted to solve the scheduling problem model,and corresponding operators are constructed according to the characteristics of the problem,including the dual-structure chromosome encoding operator integrating supply mode and processing sequence,uniform crossover operator,local search and idle time insertion method.By constructing relevant test instances for simulation experiments,the effectiveness of the proposed key operators is verified,and the influence of different inventory levels on the targets of manufacture side and demand side is compared and analyzed.The results show that inventory holding cost and delay cost are the main factors affecting the bilateral targets.3.Considering the high proportion of capital occupancy of spare parts for complex equipment,the “zero inventory” order-based production strategy can effectively solve the problem of high inventory capital occupancy of enterprises.Based on the content2,a spare parts production scheduling model considering the optimization of equipment operation utility under the zero-inventory strategy is proposed.The optimization objectives of the model are to minimize the energy consumption of the manufacture side and maximize the total operational utility of complex equipment of demand side.Based on the coupling relationship between the operation parameters and the operation power and deterioration rate of complex equipment,an optimal speed adjustment strategy is proposed to maximize the operational utility of the equipment before the spare parts are delivered.To solve the model effectively,a memetic algorithm based on NSGA-II is developed.The algorithm includes the model feature-guided initialization methods and local search operators.Finally,the efficiency of the proposed operators and the memetic algorithm is verified by numerical experiments.4.In view of the problem that most of previous studies on production scheduling focus on centralized decision model,and ignore that the formulation of final scheduling scheme under the background of network cooperative manufacturing needs multi-party consultation,a spare parts production scheduling model with information interaction and dominated by the demand side is constructed on the basis of content 3.In the model,the manufacture side is responsible for the formulation of scheduling schemes and feeding back the order delivery information to the demand side.The demand side makes policy response and is responsible for the decision of the final implementation scheme according to the feedback of order delivery information.Firstly,the information interaction framework is built from the perspective of practical application.Secondly,an information interaction mechanism with learning strategy is constructed to guide manufacture side to search for high-quality scheduling schemes to perform information interaction with demand side.A new metaheuristic algorithm based on the mechanism is designed to solve the model.Through a large number of numerical experiments,the efficiency of the proposed algorithm is verified.By comparing with the traditional centralized decision-making model,it is verified that the proposed algorithm can still help the manufacture side to find the solution that is very close to the optimal solution obtained by the centralized decision-making model in the case of incomplete information and non-dominant situation.