To address the distributed precast scheduling problem,considering the characteristics of mixed interruptible and non-interruptible operations,mixed serial and parallel operations,as well as constraints on order acceptance for factories in precast component production,and the varying impact of geographical locations in the transportation process,a sequence-based mixed-integer nonlinear programming model was established to minimize production-transportation costs and inventory-delay penalties.Given the complexity of the problem,a hybrid intelli-gent optimization algorithm based on adaptive large neighborhood search was proposed.In this algorithm,an ordinal-based vector encoding and decoding method was first designed,followed by a combination of dynamic neigh-borhood extraction heuristic and taboo search algorithms to enhance the quality of initial solutions.Diverse neighbor-hood structures and a multi-strategy fusion approach were introduced to further enhance the solution quality and so-lution efficiency to prevent premature convergence to local optima.Finally,the effectiveness of the proposed algo-rithm was validated through extensive experimentation.
关键词
分布式预制构件生产调度/准时制/混合整数非线性规划/自适应大邻域搜索
Key words
distributed precast production scheduling/just-in-time/mixed integer nonlinear programming/adaptive large neighborhood search
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