A theory of rolling horizon decision making Annals of Operations Research

This is especially true for customers that provide truthful forecasts. An allocation planning model similar to the model of Meyr (2009) is proposed by Babarogić et al. (2012). Customers are assigned to priority groups based on the size of their orders. Computational examples from the fast-moving consumer goods industry are used.

2 Master planning and allocation planning

• Next, we will describe the different ingredients of the proposed planning approach.
• However, different objectives for allocation planning are considered in the present paper in a multi-facility setting which is different from Mousavi et al. (2019).
• The approach is based on a decomposition that takes into account the structure of the semiconductor supply chain.
• Because of the large size of semiconductor supply chains, the proposed STDSM approach is based on decomposition.
• Computational examples from the fast-moving consumer goods industry are used.

This includes a network-wide allocation planning approach and the STDSM scheme. 4, we describe the simulation infrastructure that is used to apply master planning, allocation planning, and rolling horizon approach the STDSM scheme in a rolling horizon setting. Moreover, the supply chain simulation model and the demand generation scheme are described. The results of simulation experiments are presented and analyzed in Sect. Conclusions and future research directions are discussed in Sect.

3 Simulation results

ATP reallocation approaches are responsible for releasing unused committed ATP quotas. All already promised but unfinished orders are considered within a STDSM approach (Fleischmann and Meyr 2004) taking into account the available supply and capacity. STDSM approaches are desirable in semiconductor supply chains due to the long cycle times and the process and demand uncertainty (Mönch et al. 2018b). A single simulation run leads to 728 planning epochs of the STDSM approach. The corresponding average computing time for a single simulation run of the SSC-S scenario is 3581 min whereas the corresponding time for the RBR heuristic is only 1388 min. Both the STDSM and the RBR approach require allocation planning, i.e. solving instances of the model (A1)–(A6).

Multi-Period Optimization of Hydrogen-Based Steelmaking System: A Rolling-Horizon Approach

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. 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.

• The integration of the STDSM approach into a hierarchical planning approach that includes master planning, allocation planning, and production planning was discussed.
• Complex process flows in which machines are visited many times by jobs, also called lots in semiconductor manufacturing, are a result of the layer-based manufacturing of ICs.
• An iterative method is proposed to improve previously made matching decisions.
• This approach repromises orders taking into account the finite capacity of the shop floor.
• The order penetration point is at the interface between planning and control.
• An allocation planning approach for the lighting industry is proposed by Meyr (2009).

3.2 Impact of demand settings

We further demonstrate the applicability of the method to a variety of challenging optimization problems. We substantiate the findings with computational studies on the lot-sizing problem in production planning, as well as for large-scale real-world instances of the tail-assignment problem in aircraft management. It proves possible to solve large-scale realistic tail-assignment instances efficiently, leading to solutions that are at most a few percent away from a globally optimum solution. First of all, we believe that it is possible to execute the STDSM approach in a distributed manner using a cloud-based infrastructure to obtain reasonable computing times. Cloud manufacturing is a promising direction for semiconductor supply chains (Wu et al. 2014; Chen 2014; Yang et al. 2020; Herding and Mönch 2022). 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).

A STDSM approach strives to keep the promised delivery dates and to perform manufacturing at the lowest possible cost. Order repromising is required due to high uncertainty and the resulting changes in supply and available capacity. The STDSM function is similar to batch promising, however, all already promised orders compete for the supply and the capacity, while only the orders arriving within the batch interval are considered in batch order promising.

However, details are not provided for all these systems that provide demand fulfillment functionality. Demand fulfillment and order management are important in supply chains (Fleischmann and Meyr 2004; Kilger and Meyr 2015). Commercial advanced planning and scheduling (APS) systems are not appropriate for demand fulfillment in semiconductor supply chains (Chien et al. 2016). It is also shown by Mönch et al. (2018b) that demand fulfillment for semiconductor supply chains is an underresearched area. This is at least partially caused by the fact that demand fulfillment strongly interacts with other planning functions which makes it difficult to study it in a stand-alone manner. Demand and order information evolving over time is crucial for the rolling horizon scheme.