Affiliations: [a] School of Automation and MOE Key Laboratory of Measurement and Control of Complex Systems of Engineering, Southeast University, Nanjing, Jiangsu, China
| [b] School of Economics and Management, Nanjing Technology University, Nanjing, Jiangsu, China
Correspondence:
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Corresponding author. Hong-Sen Yan, Professor, School of Automation and MOE Key Laboratory of Measurement and Control of Complex Systems of Engineering, Southeast University, Nanjing, Jiangsu 210096, China. E-mail: [email protected].
Abstract: For the fixed-position assembly workshop, the integrated optimization problem of production planning and scheduling in the uncertain re-entrance environment is studied. Based on the situation of aircraft assembly workshops, the characteristics of fixed-position assembly workshop with uncertain re-entrance are abstracted. As the re-entrance repetition obeys some type of probability distribution, the expected value is used to describe the repetition, and a bi-level stochastic expected value programming model of integrated production planning and scheduling is constructed. Recursive expressions for start time and completion time of assembly classes and teams are confirmed. And the relation between the decision variable in the lower-level model of scheduling and the overtime and earliness of assembly classes and teams in the upper-level model of production planning is identified. Addressing the characteristics of bi-level programming model, an alternate iteration method based on Improved Genetic Algorithm (AI-IGA) is proposed to solve the models. Elite Genetic Algorithm (EGA) is introduced for the upper-level model of production planning, and Genetic Simulated Annealing Algorithm based on Stochastic Simulation Technique (SS-GSAA) is developed for the lower-level model of scheduling. Results from our experiments demonstrate that the proposed method is feasible for production planning and optimization of the fixed-position assembly workshop with uncertain re-entrance. And algorithm comparison verifies the effectiveness of the proposed algorithm.
Keywords: Uncertain re-entrance, fixed-position assembly workshop, integrated optimization of production planning and scheduling, improved genetic algorithm
