Applying Simheuristics for Safety Stock and Planned Lead Time Optimization in a Rolling Horizon MRP System Under Uncertainty

Seiringer, Wolfgang; Altendorfer, Klaus; Castaneda, Juliana; Panadero, Javier; Juan, Ángel A.

doi:10.1109/wsc52266.2021.9715294

Cited by 4 publications

(4 citation statements)

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“…To analyze the effects of setting values for lot size, the STR, ERR and BSBR versions are evaluated using a simulation experiment with 200 iteration, 20 runs and simulation experiment run time of 1800 days. The results of the associated simulation experiments are illustrated in Figure 2 and represent the applied basic heuristics setting from [ 26 ] with all three MRP parameters optimized. In the scenario comparison, Table 4 , the results correspond to experiment

{SBM = NO, SA = YES, Iterations = 100}.…”

Section: Results Of the Simulation Studymentioning

confidence: 99%

“…In the context of MRP parameter setting, simheuristic algorithms [ 25 ] are a meaningful approach to evaluate a broad range of parameter combinations with a defined objective (e.g., minimizing overall cost). A first simple simheuristic with two extensions to minimize overall costs was presented in [ 26 ]. However, their approach is limited in the sense that it excludes lot sizing.…”

Section: Simulation Heuristicmentioning

confidence: 99%

“…For all three variants, the possible values for the MRP parameters (planned lead time and safety stock) lie within the LB and the UB. While in [ 26 ] the lot size is not included, in this paper, we consider its parameter sampling for optimization in the same way as for the parameters planned lead time and safety stock.…”

Section: Simulation Heuristicmentioning

confidence: 99%

“…As shown in [ 26 ], even basic versions of these concepts can be useful to identify parameter settings that minimize the overall cost. In this paper, we extend the initial concepts to develop a holistic approach for MRP parameter optimization.…”

Section: Simulation Heuristicmentioning

confidence: 99%

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Applying Simheuristics to Minimize Overall Costs of an MRP Planned Production System

et al. 2022

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Looking at current enterprise resource planning systems shows that material requirements planning (MRP) is one of the main production planning approaches implemented there. The MRP planning parameters lot size, safety stock, and planned lead time, have to be identified for each MRP planned material. With increasing production system complexity, more planning parameters have to be defined. Simulation-based optimization is known as a valuable tool for optimizing these MRP planning parameters for the underlying production system. In this article, a fast and easy-to-apply simheuristic was developed with the objective to minimize overall costs. The simheuristic sets the planning parameters lot size, safety stock, and planned lead time for the simulated stochastic production systems. The developed simheuristic applies aspects of simulation annealing (SA) for an efficient metaheuristic-based solution parameter sampling. Additionally, an intelligent simulation budget management (SBM) concept is introduced, which skips replications of not promising iterations. A comprehensive simulation study for a multi-item and multi-staged production system structure is conducted to evaluate its performance. Different simheuristic combinations and parameters are tested, with the result that the combination of SA and SBM led to the lowest overall costs. The contributions of this article are an easy implementable simheuristic for MRP parameter optimization and a promising concept to intelligently manage simulation budget.

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{SBM = NO, SA = YES, Iterations = 100}.…”

Section: Results Of the Simulation Studymentioning

confidence: 99%

Section: Simulation Heuristicmentioning

confidence: 99%

Section: Simulation Heuristicmentioning

confidence: 99%

Section: Simulation Heuristicmentioning

confidence: 99%

See 2 more Smart Citations