Two novel MILP models with different flexibilities for solving integrated process planning and scheduling problems
针对集成工艺规划与调度问题，提出了两种混合整数线性规划模型：全灵活性模型适用于小规模问题，半灵活性模型通过分层简化求解空间，适用于大规模问题，并在35个基准问题上验证了优于现有算法。
Process planning and shop scheduling are considered two independent subsystems in traditional flexible manufacturing systems. For correlation and complementarity, integrated process planning and scheduling (IPPS), which has become the main focus of production research, is investigated. The commonly used approaches, intelligent algorithms and their variants, can efficiently find high-quality solutions but cannot guarantee their optimality and stability. To address these shortcomings, based on the OR-nodes of a process network, this paper establishes a mixed-integer linear programming (MILP) model with full-flexibility to solve small-scale IPPS problems. Additionally, for larger-scale problems, another model with semi-flexibility is generated by decomposing the flexibilities into two layers to simplify the original solution space of IPPS. Based on the semi-finished process routes generated by level-1 models, level-2 can search and obtain satisfactory results. The two proposed MILP models are coded in the optimisation programming language and solved by the linear solver CPLEX on 35 benchmark problems with different degrees of flexibility. Extensive experimental results successfully show the superiority of the two proposed models to the other state-of-the-art algorithms and MILP models.