Research On Energy-efficient Process Planning And Scheduling

Manufacturing companies have faced rapidly increasing energy demand, coupling with rapid exhaustion of energy resources, quickly rising energy price and enormous environmental impacts. It is extremely urgent to transform manufacturing model for a traditional manufacturing industry. Thus, energy-efficient manufacturing has been widely studied, which is a new type of environment-friendly manufacturing model for the sustainable development of economy. Process planning and scheduling are two of the most significant aspects in manufacturing system. In general, a traditional production model considers performance indicators such as processing time, cost, and quality as optimization objectives in manufacturing system and it pays more attention to material flow and information flow. However, it is very significant that energy efficiency is used as performance indicator to evaluate manufacturing processes of process planning and scheduling from the perspective of sustainable development. Therefore, according to two aspects of energy savings for technology and management, this paper focuses on reducing energy consumption of process planning system, production scheduling system and their integrated system, and the major research contents are described as follows.1. An energy-efficient flexible process planning mathematical model and its solving method are studied. First, energy consumption of manufacturing process for a flexible process planning problem is analyzed, and a mathematical model with energy efficiency in flexible process plans is proposed. Then, an improved genetic algorithm method based on multi-layer integrated coding mechanism is designed to explore the optimal solution, where a new crossover operation based on features precedence constrain module is designed and a new mutation operation based on simulated annealing algorithm using biological hormone regulation mechanism is described. The algorithm effectively avoids the emergence of illegal solutions and improves the performance. Finally, the effectiveness and superiority of the algorithm through the tests are verified. At the same time, the algorithm is used to solve the model for the above problem effectively.2. An energy-efficient model for a flexible flow shop scheduling considering static scheduling environment and its solving method are proposed. First, a static flexible flow shop scheduling problem and its related theories on energy consumption of manufacturing system are addressed. A mathematical model for the problem which is based on an energy-efficient mechanism is described. Then, a new genetic-simulated annealing algorithm is adopted to solve it. On the one hand, the hybrid algorithm uses an improved genetic algorithm to quickly search for a good group of solutions; on the other hand, the hybrid algorithm uses an improved simulated annealing algorithm to further optimize the above solutions. Next, the test cases are carried out to evaluate the performance of the hybrid algorithm. Furthermore, the proposed algorithm is compared with other algorithms mentioned in the literature to verify the advantages. Finally, the algorithm is employed to obtain the feasible solutions of the above problem, and it is feasible and effective.3. In view of dynamic disturbance behavior of the actual production scheduling, an energy-efficient mathematical model to address a dynamic scheduling problem for a flexible flow shop and its solving method are established. First, a dynamic flexible flow shop scheduling problem based on new jobs arrival and machine breakdown is described and a multi-objective model to address the problem with reducing energy consumption is proposed. Then, a novel algorithm based on an improved particle swarm optimization, which is inspired from hormone modulation mechanism, is adopted to explore the optimal solution for the multi-objective mathematical model. Finally, numerical experiments are carried out to evaluate the performance and efficiency of the proposed approach.4. Based on a non-linear process planning, an energy-aware mathematical model integrating process planning and scheduling is proposed. First, an energy-aware integrated process planning and scheduling problem is defined and its mathematical model is presented. Due to the fact that the problem is strongly NP-hard, a modified genetic algorithm is adopted to explore the optimal solution. Finally, case studies of energy-aware integrated process planning and scheduling are carried out to verify the feasibility and effectiveness of the proposed method.5. An application supporting system with energy-efficient integrated process planning and scheduling(ASSEIPPS) is developed in terms of the above theoretical research works. First, a structure frame for the ASSEIPPS is introduced. Then, the system interface of ASSEIPPS is designed in detail. Finally, an actual case study of one enterprise production is analyzed and optimized by using ASSEIPPS to verify the feasibility of the system.