Abstract: The present disclosure belongs to the field of flexible job shop scheduling, and relates to a dual-effect scheduling method for heterogeneous robots in a flexible job shop. In a case of strong coupling of processing and transferring, this method comprehensively considers such constraints on selection of flexible manufacturing cells (FMCs), transferring time by automatic guided vehicles (AGVs) and processing resource waste, and improves encoding schemes and genetic operators with order completion time and minimization of resource consumption as evaluation criteria. Additionally, this method can fully apply environmental characteristics of job shop to scheduling design, and automatically design more precise scheduling schemes, overcoming the deficiencies of slow response and prone to local optimal solutions existing in conventional scheduling schemes, and ensuring efficient and green operation.

Abstract: Disclosed is a large-scale dynamic double-effect scheduling method for a flexible job shop based on genetic programming (GP). In a GP implementation process of the present disclosure, in addition to features related to workpieces, processing robots and automated guided vehicles (AGVs), such as processing time of working procedures and buffer area waiting time, features of a ratio of processing time to transfer time and a ratio of processing energy consumption to transfer energy consumption are also added in a terminal set to cause job shop environment features to be more comprehensively applied to a scheduling rule design, thereby automatically designing a more consistent and accurate scheduling rule. A double-effect scheduling rule of the present disclosure overcomes the shortcomings of slow convergence speed, difficulty in rapid corresponding changes and unfit scheduling rules, and can quickly produce a more efficient and energy-saving production scheduling scheme.