# Applying Simheuristics to Minimize Overall Costs of an MRP Planned Production System
## 1 Introduction  
For manufacturing companies, an enterprise resource planning system (ERP) is the central system to plan and control production-related resources. Compared to the technological development of ERP systems, the used planning algorithms have not changed that much during the last decade. Most commercial ERP systems still use the hierarchical production planning approach of material requirements planning (MRP) to generate production orders [1]. The applicability in different industries and the straightforward and scalable logic--independent from the product complexity--fostered MRP's importance in industry and also science. The three planning parameters to control MRP are lot size, planned lead time, and safety stock. Many other parameters must be set up and defined to get a production system on which an MRP can be applied, such as a bill of material (BOM), processing time, setup time, planning period, machine availability, and many more. The master production schedule (MPS), which is based on the customer demands, defines the quantities and time periods of the ordered materials and specifies the gross requirements for the MRP algorithm. Even though the MRP logic is not complicated, keeping an MRP system up to date is a challenging task. Suppose raw material is available in the ERP system, but cannot be handed to production. This type of misinformation canrequire updating the complete MRP system with re-planning, including scheduled receipts and planned order releases [1]. However, these are not sophisticated tasks compared to selecting optimal MRP planning parameters. To overcome a worsening service level, it is, for example, possible to increase planned lead time with the consequence of increasing stocking level [2]. Considering the effects of changing the value of one MRP planning parameter is manageable, but doing this in a systematic way for lot size, planned lead time, and safety stock need to be supported by methods from the field of production system simulation and heuristics. Using production system simulation to evaluate the performance of a production system relying on an MRP provides interesting insights into the behavior of the production system performance. In particular, the systematic exploration of suitable MRP parameter combinations will help find optimal values with respect to given performance indicators, such as inventory and tardiness costs. Production systems are characterized by a high uncertainty level, which is associated with their planning. This may be due to internal factors, such as stochastic processing times, machine failures, limited availability of resources--either workers or raw materials--etc. It can also be due to external factors associated with customers' demand, such as required lead times, changes in orders requested, or cancellations [3]. Given this complexity, the planning of the MRP parameters in a production system requires the development of methodologies capable of dealing with systems under uncertainty. The implementation of optimization techniques based on hybrid methods between metaheuristic and simulation techniques has proven to be a methodology with the potential to establish the stochastic MRP parameters at near optimal levels [4]. Therefore, this paper proposes a simulation heuristic (simheuristic) to establish the values of the MRP parameters in a multi-stage, multi-item production system. A discrete event simulation model simulating a stochastic MRP system is combined with a metaheuristic using a daemon-like procedural criterion and simulation budget management (SBM). The stochastic MRP mimics a production plant exposed to the uncertainties associated with stochastic order amounts, the customer-required lead times, and machine setup times. Hereby, the focus of this article is to investigate how to apply different simheuristic approaches to systematically find the best MRP parameter settings in order to minimize overall costs. From an algorithmic perspective, the challenge is to integrate the simheuristics into the used simulation framework.