The batch process plays an important role in various fields of industrial production. Among them, the production processes that have specific requirements for operational continuity in each processing stage such as metallurgical, steelmaking, and pharmaceutical, should consider the no-wait constraints to meet the production reality. The batch process with no-wait constraints is a typical NP-hard problem. A new decomposition method with an improved genetic algorithm was proposed to solve the integration problem for the no-wait batch process. The integrated formulation represents a typical mixed-logic dynamic optimization (MLDO) problem which invokes logical disjunctions and operational dynamics. To solve such an MLDO problem, we transform it into a mixed-integer nonlinear program (MINLP) using the Big M reformulation and the simultaneous collocation method. Considering the structure and characteristics of the no-wait problem, an improved algorithm based on the previous GA-GBD algorithm is proposed. By collaboratively optimizing the process scheduling and dynamics, the proposed method substantially improves the overall economic performance of the batch production. At last, specific production instances are tested to demonstrate the feasibility and superiority of the proposed integration model of the no-wait batch process and optimization algorithm.

批处理在工业生产的各个领域都发挥着重要作用。其中，冶金、炼钢、制药等各加工阶段对操作连续性有特定要求的生产工艺，应考虑无等待约束，以满足生产实际。具有无等待约束的批处理是典型的 NP-hard 问题。针对无等待批处理的集成问题，提出了一种改进遗传算法的新分解方法。集成公式代表了一个典型的混合逻辑动态优化 (MLDO) 问题，它调用逻辑析取和操作动态。为了解决这样的 MLDO 问题，我们使用 Big M 重构和同时搭配方法将其转换为混合整数非线性规划 (MINLP)。考虑到无等待问题的结构和特点，提出了一种基于前人GA-GBD算法的改进算法。通过协同优化过程调度和动态，所提出的方法显着提高了批量生产的整体经济性能。最后通过具体的生产实例验证了所提出的无等待批处理与优化算法集成模型的可行性和优越性。所提出的方法大大提高了批量生产的整体经济性能。最后通过具体的生产实例验证了所提出的无等待批处理与优化算法集成模型的可行性和优越性。所提出的方法大大提高了批量生产的整体经济性能。最后通过具体的生产实例验证了所提出的无等待批处理与优化算法集成模型的可行性和优越性。