Evolving Neural Networks to Solve a Two-Stage Hybrid Flow Shop Scheduling Problem with Family Setup Times

We present a novel strategy to solve a two-stage hybrid flow shop scheduling problem with family setup times. The problem is derived from an industrial case. Our strategy involves the application of NeuroEvolution of Augmenting Topologies - a genetic algorithm, which generates arbitrary neural networks being able to estimate job sequences. The algorithm is coupled with a discrete-event simulation model, which evaluates different network configurations and provides training signals. We compare the performance and computational efficiency of the proposed concept with other solution approaches. Our investigations indicate that NeuroEvolution of Augmenting Topologies can possibly compete with state-of-the-art approaches in terms of solution quality and outperform them in terms of computational efficiency

Developing a Decision Support System for Integrated Decision-Making in Purchasing and Scheduling under Lead Time Uncertainty

Decision-making in supply chain management is complex because of the relations between planning tasks from different stages and planning levels. Uncertainties such as unpredictable supplier lead times and supply chain disruptions further complicate decision-making. Considering the case study of a company in printed circuit board assembly, a three-level concept is proposed that includes a decision support system. The global single-source supply network is characterized by highly variable lead times. Hence, the company maintains high inventory levels to prevent running out of stock. The decision support system considers the purchasing and scheduling decision problems in an integrated way. The prototypical implementation of the purchasing algorithm uses a genetic algorithm that recommends reorder days and order quantities using a simulation model. In addition, it evaluates the risks of the recommended solution by calculating the probability of stockouts for each order cycle

A Framework for Applying Reinforcement Learning to Deadlock Handling in Intralogistics

Intralogistics systems, while complex, are crucial for a range of industries. One of their challenges is deadlock situations that can disrupt operations and decrease efficiency. This paper presents a four-stage framework for applying reinforcement learning algorithms to manage deadlocks in such systems. The stages include Problem Formulation, Model Selection, Algorithm Selection, and System Deployment. We carefully identify the problem, select an appropriate model to represent the system, choose a suitable reinforcement learning algorithm, and finally deploy the solution. Our approach provides a structured method to tackle deadlocks, improving system resilience and responsiveness. This comprehensive guide can serve researchers and practitioners alike, offering a new avenue for enhancing intralogistics performance. Future research can explore the framework’s effectiveness and applicability across different systems

NMR-Based Configurational Assignments of Natural Products: Gibbs Sampling and Bayesian Inference Using Floating Chirality Distance Geometry Calculations

Floating chirality restrained distance geometry (fc-rDG) calculations are used to directly evolve structures from NMR data such as NOE-derived intramolecular distances or anisotropic residual dipolar couplings (RDCs). In contrast to evaluating pre-calculated structures against NMR restraints, multiple configurations (diastereomers) and conformations are generated automatically within the experimental limits. In this report, we show that the "unphysical" rDG pseudo energies defined from NMR violations bear statistical significance, which allows assigning probabilities to configurational assignments made that are fully compatible with the method of Bayesian inference. These "diastereomeric differentiabilities" then even become almost independent of the actual values of the force constants used to model the restraints originating from NOE or RDC data

The Advanced Floating Chirality Distance Geometry Approach ― How Anisotropic NMR Parameters Can Support the Determination of the Relative Configuration of Natural Products

The configurational analysis of complex natural products by NMR spectroscopy is still a challenging task. The assignment of the relative configuration is usually carried out by analysis of interproton distances from NOESY or ROESY spectra (qualitative or quantitative) and scalar (J) couplings. About 15 years ago, residual dipolar couplings (RDCs) were introduced as a tool for the configurational determination of small organic molecules. In contrast to NOEs/ROEs which are local parameters (distances up to 400 pm can be detected for small organic molecules), RDCs are global parameters which allow to obtain structural information also from long-range relationships. RDCs have the disadvantage that the sample needs a setup in an alignment medium in order to obtain the required anisotropic environment. Here, we will discuss the configurational analysis of five complex natural products: axinellamine A (1), tetrabromostyloguanidine (2), 3,7-epi-massadine chloride (3), tubocurarine (4), and vincristine (5). Compounds 1–3 are marine natural products whereas 4 and 5 are from terrestrial sources. The chosen examples will carefully work out the limitations of NOEs/ROEs in the configurational analysis of natural products and will also provide an outlook on the information obtained from RDCs

Discrete-event modelling of autonomous transport vehicles using open-source software

This contribution addresses the need for improved methods in modelling and simulating transportation vehicles in discrete event simulation (DES), since current commercial software solutions suffer from a limited adaptability, high costs, and slow performance. We introduce an object class that enables the addition of freely moving transport resources to open-source DES libraries, including collision-free motion modelling. Applicable to all object-oriented programming languages, this class design extends the functionality of existing open-source software. A component for an intralogistics transport vehicle was developed for the Python library Salabim, and its functionality was successfully verified in a two-vehicle simulation environment