Optimization Modeling
本课程教授运筹优化模型的构建与求解方法，重点关注商业运营中的优化决策问题。内容涵盖线性规划、整数规划、目标规划、网络优化、动态规划、启发式算法等。课程使用Python的PuLP、SciPy等库进行建模求解，结合生产计划、库存管理、运输配送、人员排班、投资组合等经典商业优化场景，培养学生将实际业务问题抽象为数学优化模型并求解分析的能力。
This course teaches operations research optimization modeling and solution methods, focusing on optimization decision problems in business operations. Topics include linear programming, integer programming, goal programming, network optimization, dynamic programming, and heuristic algorithms. Using Python libraries such as PuLP and SciPy for modeling and solving, with classic business optimization scenarios like production planning, inventory management, transportation, workforce scheduling, and portfolio optimization, students develop skills to abstract real business problems into mathematical optimization models.
针对某连锁零售企业的门店人员排班问题，收集各时段客流量、员工工作效率、用工成本等数据，分析门店运营的人员需求规律。建立整数规划优化模型，以最小化总用工成本为目标，考虑人员编制限制、工时约束、班次规则、员工偏好等约束条件。使用Python求解器求解最优排班方案，对比优化前后的成本和效率差异，提出实施建议。提交完整的建模文档、求解代码和优化分析报告。
Address workforce scheduling for a retail chain. Collect data on hourly foot traffic, employee productivity, and labor costs. Analyze staffing demand patterns. Build an integer programming optimization model minimizing total labor cost, subject to headcount limits, hour constraints, shift rules, and employee preferences. Solve using Python optimizers. Compare cost and efficiency before and after optimization. Propose implementation recommendations. Submit complete modeling documentation, solver code, and optimization analysis report.