Integrated model for production planning and scheduling in a supply chain using benchmarked genetic algorithm

Production planning and scheduling are important bases for production decisions. Concerning the traditional modeling of production planning and scheduling based on Resource-Task Network (RTN) representation, uncertain factors such as utilities are rarely considered as constraints. For the production planning and scheduling problem based on RTN representation in an uncertain environment, this paper formulates the multi-period bi-level integrated model of planning and scheduling, and introduces the uncertainties of demand and utility in planning and scheduling layers respectively. Rolling horizon optimization strategy is utilized to solve the bi-level integrated model iteratively. The simulation results show that the proposed model and algorithm are feasible and effective, can calculate the consumption of utility in every period, decrease the effects of uncertain factors on optimization results, more accurately describe the uncertain factors, and reflect the actual production process.

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Transforming Demand in Supply Chain: Production Planning and Scheduling

Supply Chain Management: Concepts, Techniques and Practices ◽

10.1142/9789812771674_0006 ◽

2007 ◽

pp. 149-171

Keyword(s):

Supply Chain ◽

Production Planning ◽

Planning And Scheduling ◽

Production Planning And Scheduling

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Integrated optimization of production planning and scheduling in uncertain re-entrance environment for fixed-position assembly workshops

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-211159 ◽

2022 ◽

pp. 1-18

Author(s):

Nan-Yun Jiang ◽

Hong-Sen Yan

Keyword(s):

Genetic Algorithm ◽

Production Planning ◽

Programming Model ◽

Expected Value ◽

Fixed Position ◽

Planning And Scheduling ◽

Integrated Optimization ◽

Production Planning And Scheduling ◽

Level Model ◽

Upper Level

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.

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