Energy-efficient integration of process planning and scheduling in discrete parts manufacturing with a heuristic-based two-stage approach

Energy-efficient manufacturing is playing an important role in addressing worldwide problems like air pollution, climate change, and energy crisis. Despite of the close relationship between process planning and scheduling, existing research efforts often consider energy efficiency-related issues by treating them separately and sequentially. This may cause various problems such as unbalanced resource allocation and infeasible production solutions, thus limiting their potential application. To advance the current state of the art in this area, this paper investigates energy-efficient integration of process planning and scheduling. Specifically, a mixed-integer linear programming model is firstly developed to formulate this problem mathematically. Subsequently, to solve more realistic large-sized problems, a heuristic-based two-stage approach is proposed. Stage 1 and Stage 2 aim at minimizing the total tardiness and reducing the total energy consumption, respectively. A realistic case study shows that the proposed two-stage approach works more efficiently and effectively than a modified genetic algorithm from existing literature. Through instructive modification of the solution space, the proposed approach can effectively reduce the total tardiness and total energy consumption; meanwhile the machining cost of individual process plans can also be kept at low level throughout the searching process. Results show that the proposed approach can allocate the machining resources in a more reasonable manner. As such, this paper may be a valuable supplement to existing efforts aiming at developing energy-efficient manufacturing techniques.