| Abstrakt: |
With the increasing level of manufacturing and the rapid development of the market economy, enterprises need to improve the level of service to meet the individual needs of customers. Among them, urgent order is a common problem faced by order-oriented enterprises, but it brings additional profits to enterprises and also causes damage to the stability of production. To address this challenge, this paper takes the hybrid flow shop as the research object, considers the equipment switching strategy, AGV transportation device and the carrying capacity of the shop buffer, establishes a mathematical model with the optimization objectives of minimizing the total energy consumption, minimizing the maximum completion time and minimizing the production cost, and proposes the Improved Red-billed Blue Magpie Optimizer (IRBMO) based on the complete re-scheduling strategy for solving the problem. In this algorithm, according to the characteristics of the hybrid flow shop, a strategy approach based on workpiece-equipment two-layer encoding and decoding is adopted, and a Logistic Chaos Mapping backward learning strategy is proposed to generate the initial population; an adaptive cross-variance operator is introduced to balance the algorithm's search capability. For the emergency order insertion scenario, a comparison test with real workshop data is carried out through the extension of the algorithm, which verifies the feasibility of the IRBMO algorithm in complex combinatorial optimization problems and the superiority of the solving effect. It provides new ideas for the green and efficient production scheduling technology of manufacturing enterprises, and has practical application value for promoting the sustainable development of the production system. [ABSTRACT FROM AUTHOR] |
