This proof-of-concept study provides a novel method for robust-stable scheduling in dynamic flow shops based on deep reinforcement learning (DRL) implemented with OpenAI frameworks. In realistic manufacturing environments, dynamic events endanger baseline schedules, which can require a cost intensive re-scheduling. Extensive research has been done on methods for generating proactive baseline schedules to absorb uncertainties in advance and in balancing the competing metrics of robustness and stability. Recent studies presented exact methods and heuristics based on Monte Carlo experiments (MCE), both of which are very computationally intensive. Furthermore, approaches based on surrogate measures were proposed, which do not explicitly consider uncertainties and robustness metrics. Surprisingly, DRL has not yet been scientifically investigated for generating robust-stable schedules in the proactive stage of production planning. The contribution of this article is a proposal on how DRL can be applied to manipulate operation slack times by stretching or compressing plan durations. The method is demonstrated using different flow shop instances with uncertain processing times, stochastic machine failures and uncertain repair times. Through a computational study, we found that DRL agents achieve about 98% result quality but only take about 2% of the time compared to traditional metaheuristics. This is a promising advantage for the use in real-time environments and supports the idea of improving proactive scheduling methods with machine learning based techniques.

Due to severe biodiversity and climate crises, there is now a need for sustainable supply chains. Food supply chains contribute to biodiversity loss, especially through land use and agriculture. In addition, energy-intensive storage for refrigeration and intercontinental transportation lead to high emission along chains due to seasonality. Selecting and prioritizing decarbonization actions is a key task for decision makers along food supply chains this decade. Often, modernizing supply chains by integrating information and digital technologies is seen as beneficial for environmental goals. The aim of this work is to develop a new systematization of sustainability measures based on archetypal sustainability strategies (sufficiency, consistency, and efficiency) that support the prioritization and thus the selection of decarbonization measures. Existing measures will be researched through a structured literature review. At the same time, it is recorded how they are categorized or systematized. Forty-eight different systematizations are analyzed. The majority relate to specific sectors and are not generally transferable. Sustainability is often addressed using the triple bottom line. In particular, efficiency and consistency measures are often included in the categories found but are rarely used for systematization. A new systematization of sustainability measures is proposed and applied to a set of digitalization and logistics 4.0 measures using the example of a fresh apple supply chain. The advantages of the proposed systematization are discussed, and further research directions are given. The presented method has not been examined in the literature so far; this concerns both the width and depth of the consideration of supply chains.

The following interdisciplinary article presents a memetic algorithm with applying deep reinforcement learning (DRL) for solving practically oriented dual resource constrained flexible job shop scheduling problems (DRC-FJSSP). From research projects in industry, we recognize the need to consider flexible machines, flexible human workers, worker capabilities, setup and processing operations, material arrival times, complex job paths with parallel tasks for bill of material (BOM) manufacturing, sequence-dependent setup times and (partially) automated tasks in human-machine-collaboration. In recent years, there has been extensive research on metaheuristics and DRL techniques but focused on simple scheduling environments. However, there are few approaches combining metaheuristics and DRL to generate schedules more reliably and efficiently. In this paper, we first formulate a DRC-FJSSP to map complex industry requirements beyond traditional job shop models. Then we propose a scheduling framework integrating a discrete event simulation (DES) for schedule evaluation, considering parallel computing and multicriteria optimization. Here, a memetic algorithm is enriched with DRL to improve sequencing and assignment decisions. Through numerical experiments with real-world production data, we confirm that the framework generates feasible schedules efficiently and reliably for a balanced optimization of makespan (MS) and total tardiness (TT). Utilizing DRL instead of random metaheuristic operations leads to better results in fewer algorithm iterations and outperforms traditional approaches in such complex environments.

Effective management of the hydrogen supply chain (HSC), starting with supplier selection, is crucial for advancing the hydrogen industry and economy. Supplier selection, a complex Multi-Criteria Decision Analysis (MCDA) problem in an inherently uncertain environment, requires careful consideration. This study proposes a novel hybrid MCDA framework that integrates the Bayesian Best–Worst Method (BWM), Fuzzy Analytic Hierarchy Process (AHP), and Entropy Weight Method (EWM) for robust criteria weighting, which is aggregated using a game theory-based model to resolve inconsistencies and capture the complementary strengths of each technique. Next, the globally weighted criteria, emphasizing sustainability performance and techno-risk considerations, are used in the TODIM method grounded in prospect theory to rank hydrogen suppliers. Numerical experiments demonstrate the approach’s ability to enhance decision robustness compared to standalone MCDA methods. The comparative evaluation and sensitivity analysis confirm the stability and reliability of the proposed method, offering valuable insights for strategic supplier selection in the evolving hydrogen landscape in the HSC.

Single- and small-batch production requires flexible production control to maximise machine reliability and minimise delivery delays. Existing planning approaches often do not take into account the dynamic production conditions of these environments, where machine breakdowns, variable order volumes and short-term changes lead to inefficiencies. This paper presents an enhanced job-shop scheduling model that integrates preventive maintenance strategies directly into production control. Using a mixed-integer programming approach, machine allocation and maintenance measures are optimised simultaneously in order to reduce unplanned downtimes and make efficient use of free time slots. The model is implemented in Python with Pyomo (Python 3.13.0 and Pyomo Version: 6.8.0) and validated using a scenario. The results show that an adaptive maintenance strategy contributes significantly to reducing machine downtimes without compromising production output. Visualisations support users in their decision-making by clearly presenting machine availability, maintenance slots and production orders. The approach is specifically designed for production and maintenance planners who need efficient and adaptable scheduling in volatile production environments. Compared to traditional maintenance models, this approach improves schedule adherence and optimises resource utilisation by dynamically linking production control and maintenance planning.

The present era is characterised by many events that have influences on supply chains and supply networks. This concerns, e.g., war, epidemics, natural disasters, accidents, strikes, political instability, and political sanctions. These are generally grouped under the term “disruption”. In order to avoid the risk of supply chain disruption, major disruption of supply networks, or loss of customers associated with disruptions, it is necessary to take preventive and proactive measures in supply chain management in terms of planning. This paper is intended to briefly summarise the current state of knowledge with the most important facts and derive a new definition from it. In addition, an analogy to maintenance is established for the first time. In doing so, a comparison of the concepts and a listing of the important proactive measures derived from them for increasing resilience are made. In the course of this, the field of action considered is extended from the exchange of suppliers through the entire supply chain network to the exchange of customers.