An integrated approach based on fuzzy inference system for scheduling and process planning through multiple objectives

Abstract

Integrated process planning and scheduling (IPPS) problems are one of the most important flexible planning functions for a job shop manufac-turing. In a manufacturing order to produce n jobs (parts) on m machines in a flexible manufacturing environment, an IPPS system intends to generate the process plans for all n parts and the overall job-shop schedule concurrently, with the objective of optimizing a manufacturing objective such as make-span. The optimization of the process planning and scheduling will be applied through an integrated approach based on Fuzzy Inference System (FIS), to provide for flexibilities of the given components and consider the qualitative parameters. The FIS, Constraint Programming (CP) and Simulated Annealing (SA) algo-rithms are applied in this design. The objectives of the proposed model consist of maximization of processes utility, minimization of make-span and total pro-duction costs including costs of flexible tools, machines, process and TADs. The proposed approach indicates that The CP and SA algorithms are able to resolve the IPPS problem with multiple objective functions. The experiments and related results indicate that the CP method outperforms the SA algorithm. © 2020, American Institute of Mathematical Sciences.