Two novel MILP models with different flexibilities for solving integrated process planning and scheduling problems

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.

Automated summary

Process planning and shop scheduling are two independent subsystems in traditional flexible manufacturing systems. Integrated process planning and scheduling (IPPS) is the main focus of production research. This study proposes two MILP models to solve IPPS problems, with full-flexibility and semi-flexibility for small-scale and larger-scale problems respectively. Experimental results demonstrate the superiority of the two models in solving IPPS problems.