The scheduling of offshore wind farm (OWF) installations is a complex, weather-driven, and resource-constrained problem. Mixed-integer linear programming (MILP) models excel at cost and makespan optimization, but remain difficult to verify and validate beyond feasibility checks. Similarly, Petri nets (PNs) provide behavioral transparency and simulation fidelity, yet lack prescriptive optimality. This article presents frameworks that utilize complementary application strategies to address two key needs, thereby combining the strengths of both approaches. First, an iterative verification–validation framework is employed that derives local PN models from MILP solutions and compares them with knowledge-based PN representations using conformance and reachability checks. Implemented in a simulation environment for the installation of OWF, the framework enables consistent model verification, cross-model validation, and context-aware scheduling. Second, a cascading decision-support framework that selects or blends MILP, heuristic, and PN-based scheduling methods through standardized descriptors and context signals. Numerical experiments demonstrate that optimization minimizes offshore time and cost, but requires high computational efforts. In contrast, Heuristics deliver short-term plans rapidly, while PN-based schedules offer intermediate costs and longer planning horizons, demonstrating that hybrid, context-sensitive orchestration outperforms any single method.

This paper introduces requirements for a flexible modeling framework for creating digital work instructions (DWI) for industrial assembly processes. Designed to standardize DWI development, the framework organizes materials, tools, and hints into structured workflows, integrating diverse input data. Developed through a three-phase methodology data identification, framework creation, and validation its design is informed by a requirement analysis. A LEGO® use case demonstrates its application, highlighting its potential to improve efficiency, reduce errors, and support the integration of advanced technologies in manufacturing.

Effective resource and task scheduling is crucial for operational efficiency in offshore installation projects. Although Mixed Integer Linear Programming (MILP) models are commonly utilized for prescriptive scheduling, they often lack comprehensive validation. This study introduces an iterative validation framework that integrates Petri nets (PNs) and process mining to validate MILP models, specifically applied to Offshore Wind Farm (OWF) installations. The proposed methodology enables cross-validation through PNs derived from MILP solutions, facilitating robust verification of the scheduling output. The practical applicability and computational complexity of this iterative approach are demonstrated through an extensive numerical case study, highlighting significant improvements in validation practices for industrial scheduling challenges. Copyright (c) 2025 The Authors. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/)

Animal feed supply networks heavily rely on just-in-time deliveries between raw material producers, retailers, manufacturers, and customers. Accordingly, transportation contributes largely to this industry's CO2e footprint. This article extends an existing simulation model with capabilities to track the CO2e footprint of individual products across the supply network. It further integrates the capability to simulate the use of electric transport vehicles. This article presents a simulation study to investigate using electric trucks instead of diesel trucks in terms of CO2e and kilometers traveled. The results show that the animal feed distribution is particularly suitable for electric vehicles due to the comparably localized area covered by these supply networks and can achieve reductions of up to 70% CO2e for a well-utilized fleet.

Wind energy constitutes a main contributor to clean and renewable energy. While the offshore sector has received much attention from research and industry, onshore wind farms still make up the largest share of installation projects. Thereby, onshore installations retain similar wind speed restrictions as their offshore counterparts but additionally introduce limits and wait time restrictions between installation operations. This article proposes extending a planning method initially designed for offshore wind farms to cover these additional requirements and proposes a simulation model capable of evaluating the resulting plans. The results show that the extended approach prevents violations of these requirements, mitigates the influence of weather forecast uncertainties, and provides efficient plans for installation operations.

Recent developments in food consumption open up new challenges in production control in manufacturing systems with low levels of automation. The demand for fresh and healthy products, in combination with the demand for processed products, requires short throughput times and attribution of exposure to process-specific, time-sensitive negative quality influences on certain products. Therefore, this article presents a framework incorporating quality aspects into production planning and control by developing mixed-integer linear programming for scheduling and integrating it into simulation-based control for sequencing. The article applies this framework to a flexible job shop problem derived from a real-world use case. The resulting schedules are used in a simulation model using different order release schemes and priority rules to test how the scheduling approach could deal with dynamic manufacturing environments. The evaluation shows the satisfying integration of quality issues in a flexible job shop problem by minimizing the deterioration while not compromising on makespan and utilization.

Animal feed production constitutes a significant market in today's agricultural sector, with an annual turnover of 55 billion euros within the European Union in 2020. Nevertheless, feed logistics stills suffer from low digitization and manually coordinated supply chains. These factors lead to high transportation and product costs for customers and retailers by inducing short-term orders that often disregard current price developments. This article presents a simulation model for feed supply networks consisting of a number of customers, retailers, and manufacturers. It proposes a fuzzy-based decision strategy for customers to decide when to order specific products. Moreover, it describes a possible decision strategy for retailers to optimize their transport routes by selecting viable manufacturers. The evaluation shows that the proposed decision strategy can reduce costs for feed and, depending on the supply network structure, reduce delivery distances for feed retailers.

. The installation of offshore wind farms con-stitutes a highly weather-dependent process. Despite this dynamic, practice and research generally assume fixed resupply cycles to deliver components from their production sites to the installation’s base port, resulting in high storage requirements. This article proposes a cascading discrete-event simulation framework combined with offline mathematical optimizations to decide demand-driven on suitable resupply cycle from a pool of routes. This approach combines the advantages of both methods by allowing high flexibility to cope with weather dynamics while reducing the search space to a few optimal alternatives. The evaluation uses two real-world use cases. It demonstrates that selecting cycles based on estimated weather developments reduces the required base port storage capacity. Moreover, in some cases it additionally maintains lower capacity levels after an initial ramp-up phase.

Over the last years, reinforcement learning has been extensively applied to schedule complex and dynamic systems. There are multitudes of simulation environments and algorithms, which hinder standardization and impede testing the suitability of reinforcement learning for specific scheduling applications and their easy implementation. This article proposes a framework to model production systems easily and transform them into standard industry simulation software to solve this issue. This framework contains major elements of classic production systems and references them adequately to allow effortless modelling. Furthermore, the domain models’ adjacent systems and their respective functionalities are described to facilitate reinforcement learning-based scheduling. This study demonstrates the framework's applicability using an existing dynamic scheduling problem. The experiences during modelling and training of the reinforcement learning subsequently are discussed.

The online grocery trade has received an additional boost from the Covid pandemic. The delivery of such purchases places particular demands on last mile logistics since consumers demand more and more individualized delivery options, e.g., regarding the delivery arrival or the type of transport. At the same time, many consumers are becoming more environmentally conscious, so there is a need to examine further how this particular consumer behavior affects the sustainability of deliveries. This paper develops and presents a simulation model, which considers grocery delivery under different framework conditions. The examined scenarios show that a change in consumer behavior directly impacts last mile logistics systems, mainly by increasing the total number of orders and a slight reduction in emissions through improved vehicle utilization. Nevertheless, the results show that without sufficiently high utilization of delivery vehicles, shopping trips by private car may cause fewer emissions.

This paper focuses on the scheduling problem in the offshore wind farm installation process, which is strongly influenced by the offshore weather condition. Due to the nature of the offshore weather condition, i.e., partially predictable and uncontrollable, it is urgent to find a way to schedule the offshore installation process effectively and economically. For this purpose, this work presents a model based on Timed Petri Nets (TPN) approach for the offshore installation process and applies simulated annealing algorithm to find the optimal schedule.

In the literature, different authors attribute between 15% to 30% of a wind farm's costs to logistics during the installation, e.g., for vessels or personnel. Currently, there exist only a few approaches for crew scheduling in the offshore area. However, current approaches only satisfy subsets of the offshore construction area's specific terms and conditions. This article first presents a literature review to identify different constraints imposed on crew scheduling for offshore installations. Afterward, it presents a new Mixed-Integer Linear Model that satisfies these crew scheduling constraints and couples it with a scheduling approach using a Model Predictive Control scheme to include weather dynamics. The evaluation of this model shows reliable scheduling of persons/teams given weather-dependent operations. Compared to a conventionally assumed full staffing of vessels and the port, the model decreases the required crews by approximately 50%. Moreover, the proposed model shows good runtime behavior, obtaining optimal solutions for realistic scenarios in under an hour.

Offshore wind energy constitutes a promising technology to achieve the world's need for sustainable energy. However, offshore wind farm installations require sophisticated planning methods due to increasing resource demands and the processes' high dependence on viable weather conditions. Current literature provides several models that either provide strategic or tactical decision support using historical data or operative support using current measurements and forecasts. Unfortunately, models of the first type cannot support the operative level. In contrast, the second type provides decision support using local, short-term optimizations that do not consider these decisions' effect on the overall installation project. This article proposes a cascading online-simulation concept that optimizes local decisions using current data. However, it estimates the effects of each decision using nested simulation and aggregates of historical data. The results show that this approach achieves a good trade-off between the project's duration and cost-inducing delays at comparably low computational costs.

Supplier development constitutes one of the current tools to enhance supply chain performance. While most literature in this context focuses on the relationship between manufacturers and suppliers, supplier development also provides an opportunity for distinct manufacturers to collaborate in enhancing a joint supplier. This article proposes a model for the optimization of such joint supplier development programs, which incorporates the effects of trust in the manufacturer-to-manufacturer relationship. This article uses a model-predictive formulation to obtain optimal supplier development investment decisions to consider the strong dynamics of the markets. Thereby, the model is designed to be highly customizable to the needs and requirements of different companies. We analyzed the price development related to Mercedes’ A-Class cars and the cost development in the automotive sector over the last ten years in Germany. According to the obtained result, the proposed model shows a sensible behavior in including trust and its effects in supplier development, even when just applying a set of generalized rules. Moreover, the numeric experiments showed that aiming for a balanced mix of optimizing revenue and trust results in the highest revenue obtained by each partner.

Over the last decades, supplier development has become an increasingly important concept to remain competitive in today’s markets. Therefore, manufacturers invest resources in their suppliers to increase their abilities and, ultimately, to reduce their product prices. Thereby, most approaches found in the literature focus on long-term supplier development programs. Nevertheless, today’s volatile and dynamic markets require flexible approaches to deal with this complexity. We apply Model Predictive Control to optimize the number of supplier development projects in order to achieve flexibility while maintaining a certain level of security for all parties. Thereby, the article focusses on a multimanufacturer scenario, where two manufacturers aim to develop the same supplier. These manufacturers can establish different levels of horizontal collaboration. While previous results already show the benefits of applying this approach to a static scenario, this article extends this formulation by introducing market dynamics in the numerical simulations as well as into the optimization approach. Thus, the article proposes to derive regression models using real-world data. The article evaluates the effects of real-world market dynamics on two use cases: an automotive use case and a use case from the mobile phone sector. The results show that assuming market dynamics during the optimization leads to increased or at least close-to-equal revenues across the involved partners. The average increase ranges from approximately 1% to 5% depending on the type and magnitude of the dynamics. Thereby, the results differ depending on the selected collaboration scheme. While a full-cooperative collaboration scheme benefits the least from regarding dynamics in the optimization, it results in the highest overall revenue across all partners.

Despite the success in developments of wind energy technology, there remain challenges, for example, in offshore wind energy installation. Due to changeable and unstable offshore weather conditions, it is hard to effectively schedule the installation logistics. In this work, we propose a simulation-based scheduling strategy to help the operators and the project managers, to make the main decisions during the installation to increase efficiency, e.g. how many offshore wind turbines should be loaded onto the installation vessel. The offshore logistic concept is modeled using timed Petri nets (TPN) approach. The timed transitions in the TPN model are assigned with operation times estimated by means of discrete-time Markov chain (DTMC) approach, which uses historical weather data. Besides, operability is introduced in this work as an indicator to evaluate schedules of a certain time period.

Besides managing accruing data in production systems, Digital Twins provide additional services like the simulation or control of production systems. The Digital Twin and its real-world physical counterpart need to be interconnected using sensors and actuators to enable such services. This article proposes a simulation-based method to design and evaluate such an interface. Applying concepts from the area of software-in-the-loop, it proposes the use of a so-called Physical Twin: a simulation model which mimics the abilities of the physical system but allows simulation-based experiments to optimise the interface. After presenting the general approach, the article provides an application example of the proposed procedure. A Digital and a Physical Twin are implemented for an application scenario and connected using a simple TCP/IP interface. By varying the number of sensors as well as considering component breakdowns in the Physical Twin, this setup allows evaluating different configurations in terms of production performance. The evaluation confirms that configurations with more sensors result in higher production performance of the system. However, a saturation of the performance gain is also observable when exceeding a certain number of sensors. Therefore, determining the optimal configuration is vital to optimise the interface from a financial point of view.