A coupling optimization method of production scheduling and logistics planning for product processing-assembly workshops with multi-level job priority constraints

Complex products consisting of multiple intermediate components are usually produced and assembled in the processing-assembly workshops. Due to the dependency relationships between components at different levels, most production operations are restricted by priority constraints, which significantly increase the complexity and difficulty of production scheduling and logistics scheduling, and also strengthens the coupling relationship between them. However, the existing researches usually separate the relationships between production scheduling and logistics scheduling, making it difficult to describe the constraints in the actual production process of the workshops accurately and hard to get an optimal solution. To obtain the optimal production and logistics scheduling schemes, a flexible job shop coupling scheduling problem with job priority constraints and logistics constraints (JPC-FJSCSP-LC) is proposed with optimization objective of minimizing the maximum completion time and reducing the standard deviation of transportation equipment's loads. And then a boosted multiobjective jellyfish optimization algorithm (BMOJS) is proposed, which introduces three strategies: Differential Evolution Mutation, probability-based random individual selection mechanism and non-inertial weight coefficients to enhance the exploitation and exploration capabilities. The encoding is realized by the triple vector consisting of job and operation sorting vector (JOSV), processing machine allocation vector (MAV), and transportation equipment allocation vector (TEAV). The decoding scheme includes a correction mechanism to guarantee the operation priority constraints and logistics constraints. The BMOJS algorithm is tested against a range of multi-objective standard test functions, which revealed that it has superior convergence and diversity in comparison to commonly utilized multi-objective optimization algorithms. Lastly, the proposed coupled optimization model is proven to better balance productivity and the uniformity degree of transportation equipment's loads through different scale cases. In brief, the model can fulfill the optimization needs of product processingassembly workshops with multi-level job priority constraints more effectively.