For example, logistics, process optimization and production planning tasks must often be optimized for a range of time periods. Usually, these problems incorporating time structure are very large and cannot be solved to global optimality by modern solvers within a reasonable period of time. Therefore, the so-called rolling-horizon approach is often adopted.
Feedback
Note that the STDSM approach is based on decomposition according to the physical structure of the underlying supply chain, i.e., optimization models are solved for the different nodes of the supply chain or groups of them. It is not obvious which one of the two decomposition approaches, i.e. the STDSM or the RBR procedure, is better. It is well-known that planning problems for large-scaled semiconductor supply chains can only be tackled by decomposition (Fordyce et al. 2011). The different planning and control modules are summarized in Table 1. To the best of our knowledge, there is no approach described in the literature that covers the interaction of master planning, allocation planning, and order promising and repromising for semiconductor supply chains. Optimization-based STDSM approaches are not considered so far in the literature for semiconductor supply chains.
- It is integrated with order promising in a rolling horizon setting, while feedback from the shop floor is considered.
- The authors would like to thank Alexander Seitz, Thomas Ponsignon, and Hans Ehm, Infineon Technologies AG, Germany for fruitful discussions on demand fulfillment in semiconductor supply chains.
- Customers are assigned to priority groups based on the size of their orders.
- In addition, we introduce conditions that guarantee the quality of the solutions.
- The internal view of sellable products is given by finished products (FPs) that are available at the DCs.
- We start by describing the demand fulfillment function in semiconductor supply chains in Sect.
5. The Process of the Hybrid Algorithm
The approach first segments customers with respect to their importance and profitability into different priority classes. ATP quantities are allocated to these classes based on short-term demand information. Seitz et al. (2020) extend the allocation planning approach of Meyr (2009) by exploiting the known demand forecast bias of customers.
- This means that customer orders have to be completed on time and finished inventory cannot be used to satisfy demand of other customers.
- We note such temporal decomposition can be orthogonal to other existing ones, and future work could combine them to improve scalability and flexibility.
- This approach aims to solve the problem periodically, including additional information from proximately following periods.
- Decomposition is used to obtain computationally tractable subproblems.
- An allocation planning approach for semiconductor manufacturing is proposed by Mousavi et al. (2019).
Such unique challenges, coupled with the inherent NP-hardness and large-scale nature of the problems, call rolling horizon approach for advanced temporal decomposition strategies Du and Pardalos, 1998, Hentenryck and Bent, 2006, Yang et al., 2013. The FE and BE STDSM MILP instances can be solved individually for each single FE and BE facility since supply is provided by master planning for each single facility. This is indicated by individual boxes for the different FE facilities (indicated by FE1, …, FEm) in Step 1 and Step 4.
This approach aims to solve the problem periodically, including additional information from proximately following periods. In this paper, we first investigate several drawbacks of this approach and develop an algorithm that compensates for these drawbacks both theoretically and practically. As a result, the rolling horizon decomposition methodology is adjusted to enable large scale optimization problems to be solved efficiently. In addition, we introduce conditions that guarantee the quality of the solutions.
A.4 Architecture, train and evaluation setup.
This makes an introduction to the topic, explains why it is useful, what are some drawbacks and proposes an algorithm to deal with them. Also, it provides two different use cases, lot sizing optimization and tail optimization. This provides an example of a crane scheduling problem, with very complete explanations. And also, you could check this very good answer to a question posted some time ago, in which the logic about rolling periods is explained.
This approach repromises orders taking into account the finite capacity of the shop floor. Decomposition is used to obtain computationally tractable subproblems. The STDSM approach is applied together with master planning and allocation planning in a rolling horizon setting. A simulation model of a simplified semiconductor supply chain is used for the rolling horizon experiments. The experiments demonstrate that the proposed STDSM scheme outperforms conventional business rule-based heuristics with respect to several delivery performance-related measures and with respect to stability.
Empirically, we show that L-RHO reduces RHO solve times by up to 54% and improves solution quality by up to 21% compared to various heuristic and learning-based baselines (with and without decomposition), across a range of FJSP settings and distributions. Moreover, we further discuss the unique potential of our L-RHO in online FJSP settings, where FJSPs operate with limited initial information and require ongoing but more efficient re-optimization as new orders arrive with dynamic environments. The literature for demand fulfillment in semiconductor supply chains is limited (see Sect. 2.2). To the best of our knowledge STDSM approaches in semiconductor supply chains are rule-based taking into account ATP quantities (Herding et al. 2017). This paper contributes to this literature by designing a STDSM approach that considers available capacity in the FE and BE facilities while changing the current promised delivery dates of already promised orders as little as possible. Because of the large size of semiconductor supply chains, the proposed STDSM approach is based on decomposition.
We refer to STDSM when an order-based matching takes place on a short-term level. A semiconductor supply chain consists of several FE and BE facilities. The probed wafers are stored in die banks (DBs) that serve as decoupling points between FE and BE. Distribution centers (DCs) are responsible for decoupling BE facilities and customers. Each FE and BE facility consists of machine groups which contain machines that provide the same functionality.
However, only some preliminary computational results for the interaction of master planning and rule-based online order promising and repromising are presented in this paper. Demand fulfillment and order management are important functions in semiconductor supply chains to interact with customers. In this paper, an iterative short-term demand supply matching (STDSM) algorithm based on mixed-integer linear programming (MILP) is proposed.
While such overlap improves boundary decision-making, it can introduce redundant computations. Thus, many control and robotics studies leverage previous decisions to reduce the computations of the current subproblem. However, they often overlook the redundancies, and none of them have integrated machine learning to address this issue, leaving a gap in accelerating RHO for COPs. Long-horizon combinatorial optimization problems (COPs), such as the Flexible Job-Shop Scheduling Problem (FJSP), often involve complex, interdependent decisions over extended time frames, posing significant challenges for existing solvers.
