A rolling-horizon approach for multi-period optimization

Finally, the fabrication position (FPOS) aggregate is used to represent the FE level in the supply picture offered by master planning. In this paper, we differentiate between orders that are fulfilled by FP, DREP, and FPOS product aggregates. The structure of the considered semiconductor supply chains including the different product aggregates is shown in Fig.

In this section you will find two strategies for implementing a rollinghorizon. One is a simple strategy that will only work with certainrestrictions. It requires just a single aggregation step and a singledisaggregation step.

Model and metaheuristics for a scheduling problem integrating procurement, sale and distribution decisions

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. We refer to machine groups as work centers in the rest of this paper. We start by describing different product aggregates, i.e. a grouping of products based on certain criteria, to characterize the supply.

3.4 Impact of the cost setting for the STDSM scheme

The upper value is obtained by the RBR whereas the lower value is computed by the STDSM. Best values for each pair of performance measure values are marked bold. 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 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 rolling horizon approach and with respect to stability.

The decision rule approach to optimization under uncertainty: methodology and applications

The algorithm to implement the rolling horizon can be outlined asfollows. The BE facilities are much smaller with respect to the number of work centers and number of process steps in the routes (Mönch et al. 2013). Therefore, solving a simultaneous BE STDSM MILP instance for all BE facilities is possible. This is indicated by the surrounding frame for the BE facilities (Step 3) in Fig.

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. Using data from a large semiconductor manufacturer, it is shown by designed experiments that average stock levels are reduced and the overall service level is increased.

3.3 Impact of information accuracy for firm order quantities

• Mathematical optimization problems including a time dimension abound.
• The STDSM approach proposed in the present paper is different since we compute the supply for BE facilities based on FE production planning.
• The results of the rolling horizon experiments are shown in Table 5.
• Demand fulfillment and order management are important functions in semiconductor supply chains to interact with customers.
• Note that the proposed planning approach is somehow similar to the FE- and BE-based production planning decomposition procedure used in the decision support system IMPReSS (Leachman et al. 1996).

We refer to STDSM when an order-based matching takes place on a short-term level. In this paper, a STDSM approach for semiconductor supply chains was proposed. The approach is based on a decomposition that takes into account the structure of the semiconductor supply chain. The NP-hardness of the related planning problem was proven. The integration of the STDSM approach into a hierarchical planning approach that includes master planning, allocation planning, and production planning was discussed. 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.

• The details of the MMFE demand generation scheme are provided in Appendix D of the electronic supplement for the sake of completeness.
• Commercial advanced planning and scheduling (APS) systems are not appropriate for demand fulfillment in semiconductor supply chains (Chien et al. 2016).
• It is well-known that planning problems for large-scaled semiconductor supply chains can only be tackled by decomposition (Fordyce et al. 2011).
• Customers are assigned to priority groups based on the size of their orders.
• ATP reallocation approaches are responsible for releasing unused committed ATP quotas.
• Additional experiments are conducted for a foundry-type setting (Mönch et al. 2018a).

1 Demand fulfillment in semiconductor supply chains

The need for rolling horizon approaches for assessing demand fulfillment is conceptually discussed by Chen et al. (2008). A STDSM approach is proposed by Geier (2014) for a computer manufacturer. It is integrated with order promising in a rolling horizon setting, while feedback from the shop floor is considered. The STDSM approach proposed in the present paper is different since we compute the supply for BE facilities based on FE production planning. Moreover, we use an iterative approach that extends the delivery time windows of the orders. Seitz and Grunow (2017) propose an order promising approach that exploits product and process flexibility typical for semiconductor supply chains.

This small-sized semiconductor supply chain model is abbreviated by SSC-S. The infrastructure including the simulation model is shown in Fig. The first two planning epochs of the master planning and production planning function are indicated in the figure by vertical lines that are blue colored.