In this paper, we develop a multi-objective two-stage stochastic programming model, which takes into account the selection of warehouse and retailer sites and the decision about production levels, inventory levels, and shipping quantities among the entities of the supply chain network. The first objective function is to maximise the chain’s total profit over multiple periods, and the second objective function is to minimise the total travel times for unsatisfied customers, whose demands must be met by retailers which have been established in other markets, to maximise the chain’s responsiveness. Demands, selling prices and productions times at manufacturing sites are all considered as uncertain parameters. The two objective functions are in conflict with each other, and we use ϵ-constraint method to generate a set of Pareto optimal solutions for the proposed multi-objective problem. We then generalise the case and assume the uncertain parameters are continuously distributed random variables and use a simulation approach called sample average approximation (SAA) scheme to compute near optimal solutions to the stochastic model with potentially infinite number of scenarios. A computational study involving hypothetical networks of different sizes and a real supply chain network are presented to highlight the efficiency of the proposed solution methodology.

中文翻译：

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在不确定性下设计有利可图的响应式供应链

在本文中，我们开发了一个多目标两阶段随机规划模型，该模型考虑了仓库和零售商站点的选择以及供应链网络实体之间关于生产水平、库存水平和运输数量的决策。第一个目标函数是在多个时期内最大化连锁店的总利润，第二个目标函数是最小化不满意客户的总旅行时间，他们的需求必须由在其他市场建立的零售商来满足，以最大化连锁店的利润。响应性。制造场所的需求、销售价格和生产时间都被视为不确定参数。两个目标函数相互冲突，我们使用 ϵ-constraint 方法为提出的多目标问题生成一组帕累托最优解。然后我们对案例进行概括并假设不确定参数是连续分布的随机变量，并使用称为样本平均近似 (SAA) 方案的模拟方法来计算具有潜在无限数量场景的随机模型的接近最优解。提出了涉及不同规模的假设网络和实际供应链网络的计算研究，以强调所提出的解决方案方法的效率。然后我们对案例进行概括并假设不确定参数是连续分布的随机变量，并使用称为样本平均近似 (SAA) 方案的模拟方法来计算具有潜在无限数量场景的随机模型的接近最优解。提出了涉及不同规模的假设网络和实际供应链网络的计算研究，以强调所提出的解决方案方法的效率。然后我们对案例进行概括并假设不确定参数是连续分布的随机变量，并使用称为样本平均近似 (SAA) 方案的模拟方法来计算具有潜在无限数量场景的随机模型的接近最优解。提出了涉及不同规模的假设网络和实际供应链网络的计算研究，以强调所提出的解决方案方法的效率。