Process Planning Optimization With Energy Consumption Reduction From a Novel Perspective: Mathematical Modeling and a Dynamic Programming-Like Heuristic Algorithm

• Main Authors: Liangliang Jin, Chaoyong Zhang
• Format: Article
• Language: English
• Published: IEEE 2019-01-01
• Series: IEEE Access
• Subjects: Energy consumption reduction; dynamic programming; green process planning; heuristic algorithms; operations selection & sequencing; production time minimization
• Online Access: https://ieeexplore.ieee.org/document/8601322/

Process planning can be deemed as an important component in manufacturing systems. It bridges the gap between designing and manufacturing by specifying the manufacturing requirements and details to convert a part from raw materials to the finished form. For the purpose of low carbon emission, this paper pays attention to both technical performance measures and environmental impact criteria. In this problem, a part may have more than one process plans and only one process plan can finally be adopted. Due to the non-deterministic polynomial-hardness, it is rather difficult to conduct operation selection, machine determination, operation sequencing, and energy consumption reduction simultaneously with various constraints from technical requirements or the shop floor status. A novel position-based mixed integer linear programming model is developed to reduce total production time and the total energy consumption. The energy consumption coefficient matrix is proposed to evaluate the energy consumption in process planning. Because of the complexity in solving the model, this research proposes dynamic programming-like heuristic algorithm to tackle this problem. The weighted sum method is applied in multi-objective optimization and three typical instances with operation flexibility, sequencing flexibility, and processing flexibility are used to test the proposed algorithm. According to the results, both the total production time and the energy consumption criteria are optimized; in the best case, the energy consumption after optimization takes only 21.2% of the one before optimization. On average, about 40.9% of the total energy consumption can be reduced after optimization. Computational results also show that the proposed algorithm is generally better than the genetic algorithm. This research gives a novel perspective to reduce energy consumption in the process planning stage.