Zheng Yi, Lei Ming, Peng Yijie
Guanghua School of Management, Peking University, Beijing 100871, China.
Fundam Res. 2023 Sep 9;5(2):473-485. doi: 10.1016/j.fmre.2023.06.013. eCollection 2025 Mar.
This study considers a problem of coordinating production, transportation and sales in a multi-echelon supply chain network. A simulation model is built to generate the random customer demands at different locations, which are affected by a marketing strategy. Customer demands need to be satisfied by the supply chain through production, transportation and distribution. The optimization problem for coordination of production, transportation and distribution is first formulated as a linear programming with demands as input parameters in the constraint. Our objective is to maximize the expectation of the optimal profit of the supply chain given random demands by selecting an optimal marketing strategy. A simulation optimization technique is proposed to control the generation of random demands and solve the linear programming for efficiently learning the optimal marketing strategy. Numerical results show that our method can significantly improve the expected profit of the supply chain and reduce the computational burden of solving linear programming for achieving a given level of probability of correct selection of the optimal marketing strategy. Furthermore, we extend the optimization problem to a mixed integer programming and also demonstrate the computational efficiency of our proposed method.
本研究考虑了多级供应链网络中生产、运输和销售的协调问题。构建了一个仿真模型来生成不同地点的随机客户需求，这些需求受营销策略的影响。供应链需要通过生产、运输和配送来满足客户需求。生产、运输和配送协调的优化问题首先被表述为一个线性规划，其中需求作为约束中的输入参数。我们的目标是通过选择最优营销策略，在随机需求的情况下最大化供应链最优利润的期望。提出了一种仿真优化技术来控制随机需求的生成，并求解线性规划以有效地学习最优营销策略。数值结果表明，我们的方法可以显著提高供应链的预期利润，并减轻求解线性规划以达到给定最优营销策略正确选择概率水平的计算负担。此外，我们将优化问题扩展为混合整数规划，并展示了我们所提方法的计算效率。
