In recent years, there have been frequent cases of impact on the stable development of supply chain economy caused by uncertain events such as COVID-19 and extreme weather events. The creation, management, and impact coping techniques of the supply chain economy now face wholly novel requirements as a result of the escalating level of global uncertainty. Although a significant literature applies uncertainty analysis and optimization modeling (UAO) to study supply chain management (SCM) under uncertainty, there is a lack of systematic literature review and research classification. Therefore, in this paper, 121 articles published in 44 international academic journals between 2015 and 2022 are extracted from the Web of Science database and reviewed using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Bibliometric analysis and CiteSpace software are used to identify current developments in the field and to summarize research characteristics and hot topics. The selected published articles are classified and analyzed by author name, year of publication, application area, country, research purposes, modeling methods, research gaps and contributions, research results, and journals to comprehensively review and evaluate the SCM in the application of UAO. We find that UAO is widely used in SCM under uncertainty, especially in the field of decision-making, where it is common practice to abstractly model the decision problem to obtain scientific decision results. This study hopes to provide an important and valuable reference for future research on SCM under uncertainty. Future research could combine uncertainty theory with supply chain management segments (e.g., emergency management, resilience management, and security management), behavioral factors, big data technologies, artificial intelligence, etc.

With increasing awareness of perishable products, green grocers are forced to be concerned about the deterioration of freshness and adopt a time-based multiple pricing (TMP) policy. However, the reluctance of supply chain members to share their information exacerbates the predicament in the perishable product industry. This paper focuses on the TMP policy with bilateral asymmetric information that the supplier conceals freshness information and the retailer conceals demand information. Differing from existing literature, we adopt the TMP policy to release the retailer from the inferiority of information asymmetry. Based on the Stackelberg game model, three main conclusions are summarized as follows. First, information asymmetry does not always damage profits of the agent in the supply chain. Second, when the retailer agrees to adopt the TMP policy, the TMP policy decreases the price but the retailer can benefit from the low price in perishable product industry. Third, interestingly, the TMP policy can mitigate the impact of information asymmetry that reduces profits of the retailer. However, the mitigation of the TMP policy is limited and it cannot undo the detrimental effects instigated by information asymmetry. Thus, managers in the perishable product industry should adopt the TMP policy as a countermeasure to prevent the reverse caused by information asymmetry.