基于IGAIP协同优化的合班排课算法研究

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中图分类号：TP18；G647.3 文献标识码：A

Research on Joint-class Course Scheduling Algorithm Based on Collaborative Optimization of Improved Genetic Algorithm and Integer Programming

LIU Jinfeng，WANG Dian （College of Engineering，Beijing Forestry University，Beijing lOoo83，China）

Abstract： Large-scale joint-class course scheduling in universities is characterized by complex resource conflicts and synchronous constraints. Traditional genetic algorithms are prone to local optima. Additionally，exact solvers often suffer from low computational efficiency. To address these issues， a collaborative optimization strategy based on Improved Genetic Algorithm and Integer Programming （IGAIP） is proposed. In this strategy， dynamic chromosome encoding and an entropy-based adaptive mechanism were employed for global search. Feasible solutions were generated. Furthermore，the CPLEX solver was introduced for precise verification and conflict repair based on an integer programming model. A bidirectional feedback mechanism was established between the two layers. Experimental results showed that a 100% satisfaction rate for hard constraints was achieved on a large-scale dataset. Compared with the standalone Improved Genetic Algorithm，the solution efficiency was improved by 59.51% . The optimization difficulty of largescale joint-class scheduling was effectively resolved. Consequently，a novel algorithmic approach is provided for scheduling problems with similar complex constraints. A feasible technical pathway is offered for automated and intelligent university timetabling management.

Keywords： university joint-class course scheduling; improved genetic algorithm; integer programming；collaborative strategy

课程表编排是高校教学管理的核心环节，对保障教学秩序与提升资源利用效率至关重要。（剩余10784字）