arXiv subjects

Klaus Altendorfer

Publications and source records attributed to Klaus Altendorfer.

8 recordsLinked to original sources

arXiv2025

Optimizing Energy Consumption in Stochastic Production Systems: Using a Simulation-Based Approach for Stopping Policy

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 - an energy-intensive lead-acid battery manufacturer - faces significant process uncertainty in its heat-treatment operations, making static planning inefficient. To evaluate a potential sensor-based solution, the SBA leverages simulated sensor data (using a Markovian model) to iteratively refine Bayesian energy estimates and dynamically adjust batch-specific processing times. A full-factorial numerical 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) Optimized Planned Processing Times (OPT); (2) the company's Current Baseline Practice; 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.

eess.SY↗

arXiv2025

Release Date Optimization in MRP Using Clearing Functions

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.

eess.SY↗

arXiv2024

Evaluating Production Planning and Control Systems in Different Environments: A Comparative Simulation Study

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.

econ.GN↗

arXiv2024

Energy Price and Workload Related Dispatching Rule: Balancing Energy and Production Logistics Costs

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.

econ.GN↗

arXiv2024

How Periodic Forecast Updates Influence MRP Planning Parameters: A Simulation Study

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.

econ.GN↗

arXiv2024

Enhancing Rolling Horizon Production Planning Through Stochastic Optimization Evaluated by Means of Simulation

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%. However, introducing explicit safety stocks fundamentally shifts system dynamics: physical buffers effectively absorb shop-floor noise, diminishing the stochastic model's anticipative advantage and enabling deterministic optimization to dominate. Ultimately, this study offers critical managerial insights for aligning planning algorithms, inventory buffering, and schedule flexibility.

econ.EM↗

arXiv2024

Integrating Simulation Budget Management into Drum-Buffer-Rope: A Study on Parametrization and Reducing Computational Effort

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.

eess.SY↗

arXiv2018

Effects of forecast errors on optimal utilisation in aggregate production planning with stochastic customer demand

The hierarchical structure of production planning has the advantage of assigning different decision variables to their respective time horizons and therefore ensures their manageability. However, the restrictive structure of this top-down approach implying that upper level decisions are the constraints for lower level decisions also has its shortcomings. One problem that occurs is that deterministic mixed integer decision problems are often used for long-term planning, but the real production system faces a set of stochastic influences. Therefore, a planned utilisation factor has to be included into this deterministic aggregate planning problem. In practice, this decision is often based on past data and not consciously taken. In this paper, the effect of long-term forecast error on the optimal planned utilisation factor is evaluated for a production system facing stochastic demand and the benefit of exploiting this decision's potential is discussed. Overall costs including capacity, backorder and inventory costs, are determined with simulation for different multi-stage and multi-item production system structures. The results show that the planned utilisation factor used in the aggregate planning problem has a high influence on optimal costs. Additionally, the negative effect of forecast errors is evaluated and discussed in detail for different production system environments.

math.OC↗