Production control systems are essential for optimizing manufacturing processes by controlling workflow, reducing waste, and improving efficiency. However, these systems are often challenging to understand due to the complexity of their underlying principles and their impact on production performance. Traditional teaching methods and tools may fail to provide the interactive and dynamic learning experience needed to convey these concepts effectively. This paper addresses the challenge of teaching production control systems in an engaging and intuitive way by introducing VISP ("Virtual Interactive Simulation of Production"), a Virtual Reality application designed to visualize and interactively demonstrate production planning and scheduling methods. As a first example, we implemented Kanban, allowing users to collaboratively adjust key parameters—such as machine speed, container configuration, and product flow—and observe their effects on critical Key Performance Indicators (KPIs), including production throughput time, costs, inventory, delays, and delivery reliability. The purpose of VISP is to bridge the gap between theoretical understanding and practical application of production control systems. By enabling hands-on experimentation and real-time feedback, VISP provides a tool for understanding complex systems and supports users in identifying optimal configurations through interactive learning.

This paper seeks to introduce an energy-aware heuristic for smart manufacturing. The core of the heuristic is a dispatching rule which supports two different types (levels) of decisions, namely (1) whether night-shifts should be staffed or not and (2) whether a particular machine should be stopped or not. While existing approaches often focus on energy-aware machine control, they typically neglect the more expensive cost implications of night-shift personnel availability. This paper addresses that gap by combining both machine- and system-level decisions into a unified heuristic. The decision is based on the current (day-time-dependent) energy price and the current workload within the production system, using predefined thresholds for either criterion. To evaluate the proposed heuristic, it is embedded into a stochastic multi-item, multi-stage simulation model of a manufacturing system. Cost aspects include energy, production logistics, and labor costs for staffing the night shifts. Computational results confirm that the trade-off between those cost factors can be effectively controlled via the parameterization of the underlying dispatching rule, i.e., the concrete threshold values. Furthermore, the lowest overall costs can be achieved when night-shifts are enabled only in case of very low energy prices. On the lower (machine) level, it turns out that allowing machines to operate also under moderate energy prices or small workloads helps avoid pushing too much work content towards the night-shifts.

As the share of renewable energy sources increases, electricity prices become more volatile, creating both challenges and opportunities for energy-intensive manufacturing industries. This paper introduces a mathematical optimization model that supports annual production planning with a focus on minimizing total costs associated with energy, labor, inventory, and capacity investments. The core idea is to use storable, energy-intensive products as a form of indirect energy storage, producing them when electricity prices are low and capacity is available. By integrating dynamic labor and energy costs at the shift level, the model enables cost-efficient activation of production shifts and guides capacity investment decisions to enhance production flexibility. The model was evaluated in a practice-oriented use case using electricity price data from Ember Energy across multiple scenarios and through sensitivity analyses. Results demonstrate that selectively activating shifts with lower energy and labor costs can significantly reduce total expenses while maintaining production efficiency. This work contributes to more sustainable industrial practices by aligning operational decisions with cost-efficient and environmentally conscious energy use.

In manufacturing, a bottleneck workstation frequently emerges, complicating production planning and escalating costs. To address this, Drum-Buffer-Rope (DBR) is a widely recognized production planning and control method that focuses on centralizing the bottleneck workstation, thereby improving production system performance. Although DBR is primarily focused on cre-ating a bottleneck schedule, the selection of planning parameters is crucial, as they significantly influence the scheduling process. Conducting a compre-hensive full factorial enumeration to identify the ideal planning parameters requires substantial computational effort. Simulation Budget Management (SBM) offers an effective concept to reduce this effort by skipping less promising parameter combinations. This publication introduces a method for integrating SBM into multi-stage multi-item DBR planned and controlled production system with limited capacity, aimed at determining the optimal planning parameters. Furthermore, we conduct a simulation study to analyze the effects of different production system environments, i.e., varying levels of shop load and process uncertainty, on both the performance and parame-terization of DBR and the efficacy of SBM. Our results show significant re-duction in simulation budget for identifying optimal planning parameters compared to traditional full factorial enumeration.

In response to the escalating need for sustainable manufacturing, this study introduces a Simulation-Based Approach (SBA) to model a stopping policy for energy-intensive stochastic production systems, developed and tested in a real-world industrial context. The case company-a lead-acid battery manufacturer-faces significant process uncertainty in its heat-treatment operations, making static planning inefficient and limiting energy efficiency. To evaluate a potential sensor application for real-time control, the SBA leverages simulated sensor data (using a Markovian model) to iteratively refine Bayesian energy estimates and dynamically adjust batch-specific processing times. A discrete-event simulation, mirroring the company's 2024 heat-treatment process, evaluates the SBA's energy reduction potential, configuration robustness, and sensitivity to process uncertainty and sensor distortion. Results are benchmarked against three planning scenarios: (1) the company's CurrentBaseline Practice; (2) Optimized Planned Processing Times (OPT); and (3) an Ideal Scenario with perfectly known energy requirements. SBA significantly outperforms OPT across all tested environments and in some cases even performs statistically equivalent to an Ideal Scenario. Compared to the Current Baseline Practice, energy input is reduced by 14-25 %, depending on uncertainty and sensor accuracy. A Pareto analysis further highlights SBA's ability to balance energy and inspection-labour costs, offering actionable insights for industrial decision-makers.

This paper integrates a clearing function (CF)-based release planning approach into Material Requirements Planning (MRP) to address its limitations in modeling capacity constraints and dynamic lead times. The proposed optimization model replaces MRP's backward scheduling step while preserving its overall structure. Performance is evaluated through simulation experiments on two flow shop systems that explore a range of demand uncertainties and utilization levels. Computational results show that the proposed approach is capable of yielding significant improvements over the conventional backward scheduling approach, due to its ability to compute planned lead times for individual production orders as opposed to BOM items.

In many supply chains, the current efforts at digitalization have led to improved information exchanges between manufacturers and their customers. Specifically, demand forecasts are often provided by the customers and regularly updated as the related customer information improves. In this paper, we investigate the influence of forecast updates on the production planning method of Material Requirements Planning (MRP). A simulation study was carried out to assess how updates in information affect the setting of planning parameters in a rolling horizon MRP planned production system. An intuitive result is that information updates lead to disturbances in the production orders for the MRP standard, and, therefore, an extension for MRP to mitigate these effects is developed. A large numerical simulation experiment shows that the MRP safety stock exploitation heuristic, that has been developed, leads to significantly improved results as far as inventory and backorder costs are concerned. An interesting result is that the fixed-order-quantity lotsizing policy performs - in most instances - better than the fixed-order-period lotsizing policy, when periodic forecast updates occur. In addition, the simulation study shows that underestimating demand is marginally more costly than overestimating it, based on the comparative analysis of all instances. Furthermore, the results indicate that the MRP safety stock exploitation heuristic can mitigate the negative effects of biased forecasts.

Rising energy price volatility and the shift toward renewables are driving the need for energy-aware production planning. This paper investigates the integration of energy storage systems into dynamic dispatching to balance production-logistics and energy costs. Building on prior work that introduced a workload- and price-based dispatching rule, we extend the model to include energy storage loading and unloading decisions. The rule prioritizes storage refill at low prices and lets machines resort to stored energy when grid prices rise and the workload is high. A simulation-based evaluation examines scenarios with different storage capacities whereby decision rule parameters are optimized. Computational results demonstrate that a reduction of operational costs is possible without deteriorating production logistics performance. By decoupling energy sourcing from real-time prices, manufacturers achieve both resilience and cost savings. This research contributes to sustainable manufacturing by offering a practical strategy for integrating energy storage into production planning under volatile energy conditions.

To achieve the 1.5-degree climate goal of the Paris Agreement, energy consumption in production must be reduced, particularly for energy-intensive products like lead-acid batteries. In this study, we develop a simulation model for a lead-acid battery real-world case company and integrate the effect of sensor data during the heat treatment process, i.e., maturation and drying of lead plates. The sensor used in this context provides a range for the stochastic minimum required energy at the maturation and drying. Leveraging this data, we develop a heuristic approach to optimize the planned process times for both steps. Moreover, the effect of sensor accuracy, which determines the number of provided ranges, is observed. Simulation results reveal a significant energy reduction compared to optimized planned process times without sensor information. In addition, our findings also highlight the importance of sensor accuracy in achieving lower energy consumption during the heat treatment process.

Production planning must account for uncertainty in a production system, arising from fluctuating demand forecasts and execution-level friction. This article integrates scenario-based stochastic programming into a rolling horizon framework for capacitated lot sizing, evaluated via discrete-event simulation. We compare this stochastic approach against deterministic optimization and standard Material Requirements Planning (MRP) across varying customer update behaviors, shop loads, and diverse multi-stage topologies (divergent, convergent, and mixed). To accurately capture shop-floor dynamics, the framework introduces a non-anticipativity parameter controlling schedule flexibility, alongside probabilistic setup-time feedback and soft overtime constraints. Results indicate that optimization consistently outperforms MRP. In unbuffered, highly congested settings, stochastic optimization natively smooths workloads and reduces costs by up to 68

In response to the escalating need for sustainable manufacturing practices amid fluctuating energy prices, this study introduces a novel dispatching rule that integrates energy price and workload considerations with Material Requirement Planning (MRP) to optimize production logistics and energy costs. The dispatching rule effectively adjusts machine operational states, i.e. turn the machine on or off, based on current energy prices and workload. By developing a stochastic multi-item multi-stage job shop simulation model, this research evaluates the performance of the dispatching rule through a comprehensive full-factorial simulation. Findings indicate a significant enhancement in shop floor decision-making through reduced overall costs. Moreover, the analysis of the Pareto front reveals trade-offs between minimizing energy and production logistics costs, aiding decision-makers in selecting optimal configurations.

Selecting the appropriate production planning and control systems (PPCS) presents a significant challenge for many companies, as their performance, i.e., overall costs, depends on the production system environment. Key environmental characteristics include the system's structure, i.e., flow shop, hybrid shop, or job shop, and the planned shop load. Besides selecting a suitable PPCS, its parameterization significantly influences the performance. This publication investigates the performance and the optimal parametrization of Material Requirement Planning (MRP), Reorder Point System (RPS), and Constant Work In Progress (ConWIP) at different stochastic multi-item multi-stage production system environments by conducting a comprehensive full factorial simulation study. The results indicate that MRP and ConWIP generally outperform RPS in all observed environments. Moreover, when comparing MRP with ConWIP, the performance clearly varies depending on the specific production system environment.

Constant-Work-In-Process (ConWIP) is a promising production planning and control method for make-to-order production systems, exhibiting notable potential in attaining reduced tardiness alongside effective management of work in process and finished goods inventories, as demonstrated in various studies. Furthermore, several papers show that the negative effects of high demand uncertainty, which occur when applying a make-to-order approach, can be mitigated by providing flexible capacity to coordinate demand and throughput. Therefore, in this paper the workload-based ConWIP method is combined with a flexible capacity setting method, to enable a better fit between demand and throughput. To fully capitalize on the benefits of flexible capacity and enable the production system to adapt to changes in throughput potential, an adjustment of the WIP-Cap is integrated to avoid machine starvation or unused overcapacity. To evaluate the system performance, a multi-stage multi-item make-to-order flow shop production system with stochastic demand, processing and customer required lead times is simulated. The results of a broad numerical study show a high improvement potential of the extended ConWIP version in comparison to workload-based ConWIP.

Production Planning and its parameterization is critical to fulfil customer demands and to successfully react on changes in high volatile markets. Therefore, demand updates should be considered to improve production planning. In this paper the performance of two production planning methods MRP (Material Requirements Planning) and RPS (Reorder Point System) are compared in a multi-item single stage system where customer orders are updated in a rolling horizon manner. Applying a simulation study, we investigate the performance of MRP and RPS for biased and unbiased forecast information and discuss the difference in the optimal planning parameters. The study shows that for a production system with underbooking and low demand uncertainty, RPS method is superior, in all other scenarios MRP outperforms RPS. For overbooking scenarios, the results show MRP leads to overall cost improvements ranging from 8 to 30 %.

In this paper a general demand model for the supplier is developed based on practically observed customer forecasting behaviours. A rolling horizon information update approach is assumed where customers provide their forecasts for a predefined information horizon. In the basic situ-ation, a MMFE (martingale model of forecast evolution) demand model is assumed where in-formation updates are unbiased. This setting is extended to a situation where updates are biased, i.e., a forecast update does not necessarily imply an information improvement. Furthermore, the model is extended by a demand shifting behaviour, i.e., demands for certain periods may be shifted to other periods. For this practically motivated demand model, the forecast accuracy related to periods before delivery is calculated and a measure for production order accuracy is developed assuming a simplified material requirements planning structure. Finally, a correction method for reducing the negative effects of biased demand forecasts is introduced and its per-formance is evaluated based on simulation and real company data.

The performance of modern production systems often depends upon automated production planning strategies such as material requirements planning. Parametrizing, evaluating and comparing these strategies by testing them in the real world is often difficult and prohibitively resource intensive. State-of-the-art computer simulation can be used to adequately model the production processes and predict the relevant performance metrics without investing valuable production capacities. Heuristic optimization procedures can build on these simulations to fine-tune production planning strategies. A major obstacle for this simulation-based optimization approach, however, lies in its computational requirements since accurate production simulations require their fair share of computation time. In this work, we will demonstrate the use of heuristic optimization to learn optimal production strategies for a bi-objective high-dimensional real world scenario and explore how to reduce the computational cost of the heuristic search by use of surrogates and dimensionality reduction. Results indicate that the employed approach achieved solutions that could outperform the production planning parameters currently used in the real world.