With the increasingly customized product requirements of customers, the manufactured products have the characteristics of multi-variety and small-batch production. A high-quality production scheduling scheme can reduce energy consumption, improve production capacity and processing quality of the enterprise. The high-dimensional many-objective green flexible job shop scheduling problem (Ma-OFJSSP) urgently needs to be solved. However, the existing optimization method are difficult to effectively optimize the Ma-OFJSSP. This study proposes a many-objective flexible job shop scheduling model. An optimization method SV-MA is designed to effectively optimize the Ma-OFJSSP model. The SV-MA memetic algorithm combines an improved strength Pareto evolution method (SPEA2) and the variable neighborhood search method. To effectively distinguish the better solutions and increase the selection pressure of the non-dominated solutions, the fitness calculation method based on the shift-based density estimation strategy is adopted. The SV-MA algorithm designs the variable neighborhood strategy which combines with scheduling knowledge. Finally, in the workshop scheduling benchmarks and the machining workshop engineering case, the feasibility and effectiveness of the proposed model and SV-MA algorithm are verified by comparison with other methods. The production scheduling scheme obtained by the proposed model and SV-MA optimization algorithm can improve production efficiency and reduce energy consumption in the production process.

With the development of intelligent manufacturing and the customized product demand of customers, manufacturing enterprises are urgently required to carry out high-efficiency, high-quality, flexibility and low-cost manufacturing to enhance the competitiveness of enterprises. Intelligent job shop scheduling problem is the core decision of intelligent manufacturing production management. Many-objective job shop scheduling algorithms can effectively solve this problem. However, existing optimization algorithms cannot effectively solve many-objective flexible job shop scheduling problem. This paper establishes the many-objective job shop intelligent scheduling model with complex constraints, and proposes an improved intelligent decision optimization algorithm named NSGA-III-APEV based on NSGA-III. This algorithm uses the penalty-based boundary intersection distance that takes into account both convergence and diversity simultaneously to define the distance between the population individual and the reference vector in the association operation. This paper exploits the penalty-based boundary intersection distance-based elimination mechanism to preserve individuals and reduce the computational cost in the individual preservation strategy. Meanwhile, the adaptive mutation strategy based on consanguinity is employed in genetic operators. The presented method effectively improves the convergence and diversity of the population. Finally, NSGA-III-APEV with other algorithms was compared through benchmarks. Experimental results demonstrated the effectiveness and superiority of the improved method. The feasibility of the improved method in solving the many-objective flexible job shop scheduling problem are verified by engineering examples.

Due to the increasingly customized product requirements of customers, the production characteristics of the aluminum alloy creep forming components production line have changed to multiple varieties and small batches. As a result, it has the problems of complex production organization, low production efficiency. As far as we know, this is the first time to study the production scheduling problem of the aluminum alloy creep forming component production line. The whole-process production scheduling model is established. Multi-population coevolutionary optimization algorithm (MPCOA) is proposed. The MPCOA method uses three subpopulations for collaborative optimization. The sharing and integration of search information is realized through the interaction between subpopulations. For the optimization of the first subpopulation, an improved VAEA (vector angle-based evolutionary algorithm) which integrates the large neighborhood search is proposed. It can help to mine global information, and avoid local optimization. For the optimization of the second subpopulation, to get uniformly distributed solutions and improve the convergence, an improved decomposition-based method is proposed, which uses an improved Tchebycheff function and adaptive update weight vector mechanism. Computational experiments are performed by using industrial datasets and engineering production data. The competitiveness and superiority of the whole-process production scheduling model and the MPCOA method are demonstrated.

For the many-objective collaborative optimization problem of high performance alloy casting manufacturing process, how to achieve high efficiency, low energy consumption and intelligent scheduling process is an urgent problem to be solved. Firstly, a multi-objective pro-duction scheduling model of the whole process was established according to the production characteristics, process flow and production constraints. Meanwhile, a new high-dimensional multi-objective strong dominant optimization algorithm was proposed to solve the production scheduling model of high-performance alloy melting casting production line. The proposed algorithm is designed by combining strong domination algorithm and large neighborhood search algorithm. The proposed algorithm method implements deep exploration of solution space through cyclic destruction-repair operation. Its dynamic neighborhood characteristics can effectively escape the local optimal. Based on the industrial data set of alloy casting production line, the performance of the model and the proposed algorithm are tested. Experimental results substantiate the effectiveness and comparative advantages of the many-objective scheduling model and the proposed algorithm method.