## 2 Literature Review
### Uncertainty Management Approach  
The uncertainty of customer orders poses a huge challenge to manufacturing companies. Studying uncertainty management in the production field creates possibilities for coping with related issues and provides a path for improving enterprise resilience and creating higher profits [3]. Orders are the link between sales and production and can influence other logistics objectives, such as the utilization of production. For Make-To-Order (MTO) companies, delivery time and delivery reliability are key evaluation criteria for customers [4]. Therefore, a model-based procedure that uses a diagram with early available information, such as the offer requests, in the planning process to determine delivery time is favorable for addressing the challenge of confirming binding delivery times with customers in the early stages of the ordering process [4]. To maintain the stable and robust production of Multistate Manufacturing Systems (MMS) under various kinds of disruptions, a comprehensive production-and-maintenance scheduling method oriented towards overall interruptions resilience was proposed, and a performance loss-based resilience measurement considering operational uncertainty was developed, to enhance adaptability to changes in production demand [5]. In an environment of uncertain market demand and spot prices, retailers can order products before demand updates by signing wholesale price contracts (known as contract orders), or they can order products from manufacturers in the spot market after demand updates (known as spot orders). A dynamic decision model was constructed to derive the optimal equilibrium solution for production and order quantities under the contract order strategy and spot order strategy, to achieve the maximum profit in uncertain environments [6]. Supply chain disruption is another type of uncertainty faced by manufacturing enterprises. To enhance supply chain resilience and effectively manage disturbances, implementing flexible/hybrid manufacturing systems is a feasible strategy that uses dedicated machinery to meet regular demand and Flexible Manufacturing Systems (FMSs) to respond to demand surges. A scenario-based method was used to model demand uncertainty, enabling instant and adaptive decision making using the cost-effectiveness of standard production and the responsiveness of FMSs [7]. [PERSON] et al. [8] proposed a two-stage stochastic programming model to minimize the cost of business relations establishment with upstreaming supply enterprises, as well as production costs, inventory costs, transportation costs, and penalty costs for unfulfilled demands. They designed an improved Benders decomposition algorithm by incorporating a heuristic searching mechanism to enhance its problem-solving efficiency. A hybrid manufacturing system that uses raw materials and returns components for further production is easily affected by inventory, market demand, and return uncertainties. The Kalman-based estimation control method provides a path to dealing with uncertainties by estimating inventory levels and demands and forecasting return components, allowing cost estimation over a long period; it also allows for determining production and disposal policies that adapt to market changes and possible uncertainties [9]. [PERSON] [10] studied the production load problem of a global clothing manufacturing company facing uncertainties in demand and import quotas; [PERSON] [10] constructed a two-stage stochastic programming model and used two-stage production planning to rapidly respond to constantly changing market information while minimizing total production costs. [PERSON] and [PERSON] [11] studied the impact mechanism of the uncertain demands of enterprises on the profit decision making of the Warehouse Product Service System (WPSS) based on intelligent warehouses and \"centralized procurement + shared storage + JIT distribution\" services.