In the context of manufacturing servitization, manufacturing enterprises offer not only products but also associated services according to customer demands, which is the so-called Product Service System (PSS). In PSS configuration, enterprises need to jointly determine the selected modules with corresponding prices to improve sales profit and customer satisfaction. Better customer satisfaction can be achieved by lower price and larger module utility. Apart from most previous research, we consider not only the single module utility but also the pairing utility, which measures the additional value jointly created by a product module and a service module to illustrate the synergy effect of “product + service”. Furthermore, we take the uncertainty of service utility and customer behavior into account. The former is due to the heterogeneity and intangibility nature of service, and the latter is described by price sensitivity that varies among different customers. To realize the above two contributions, we first establish a multi-objective stochastic programming model to maximize expected customer satisfaction as well as sales profit. For a better trade-off between solution quality and computation efficiency, we develop an improved algorithm based on epsilon-constraint and sample average approximation, including the feasibility examination and accelerating strategy. Finally, numerical experiments are conducted to demonstrate our approach and validate the contributions.

在制造服务化的背景下，制造企业不仅提供产品，还根据客户的需求提供相关的服务，这就是所谓的产品服务系统（PSS）。在 PSS 配置中，企业需要共同确定所选模块和相应的价格，以提高销售利润和客户满意度。更低的价格和更大的模块效用可以获得更好的客户满意度。除了以往的大多数研究，我们不仅考虑了单个模块效用，还考虑了配对效用，它衡量了产品模块和服务模块共同创造的附加价值，以说明“产品+服务”的协同效应。此外，我们考虑了服务效用和客户行为的不确定性。前者是由于服务的异质性和无形性，后者是由不同客户的价格敏感性来描述的。为了实现以上两个贡献，我们首先建立了一个多目标随机规划模型，以最大化预期客户满意度和销售利润。为了更好地权衡解质量和计算效率，我们开发了一种基于 epsilon 约束和样本平均近似的改进算法，包括可行性检查和加速策略。最后，进行数值实验以证明我们的方法并验证贡献。我们首先建立了一个多目标随机规划模型，以最大化预期的客户满意度和销售利润。为了更好地权衡解质量和计算效率，我们开发了一种基于 epsilon 约束和样本平均近似的改进算法，包括可行性检查和加速策略。最后，进行数值实验以证明我们的方法并验证贡献。我们首先建立了一个多目标随机规划模型，以最大化预期的客户满意度和销售利润。为了更好地权衡解质量和计算效率，我们开发了一种基于 epsilon 约束和样本平均近似的改进算法，包括可行性检查和加速策略。最后，进行数值实验以证明我们的方法并验证贡献。