25 papers · ranked by Valyu relevance
Orlenys López-Pintado, Jannis Rosenbaum, Marlon Dumas
In business processes, activity batching refers to packing multiple activity instances for joint execution. Batching allows managers to trade off cost and processing effort against waiting time. Larger and less frequent batches may lower costs by reducing processing effort and amortizing fixed costs, but they create…
Maximilian Siska, Emma Pajak, Katrin Rosenthal, Antonio del Rio Chanona + 2 more
Bayesian optimization has become widely popular across various experimental sciences due to its favorable attributes: it can handle noisy data, perform well with relatively small data sets, and provide adaptive suggestions for sequential experimentation. While still in its infancy, Bayesian optimization has recently…
Seungwoo Kum, Seungtaek Oh, Jeongcheol Yeom, Jaewon Moon + 2 more
'Claudio Gennaro' 'Claudio Vairo'] As deep learning technology paves its way, real-world applications that make use of it become popular these days. Edge computing architecture is one of the service architectures to realize the deep learning based service, which makes use of the resources near the data source or…
Mimi Zhang, Andrew Parnell, Dermot Brabazon, Alessio Benavoli
Bayesian optimization (BO) is an approach to globally optimizing black-box objective functions that are expensive to evaluate. BO-powered experimental design has found wide application in materials science, chemistry, experimental physics, drug development, etc. This work aims to bring attention to the benefits of…
Markus Grimm, Sébastien Paul, Pierre Chainais
The optimization of yields in multi-reactor systems, which are advanced tools in heterogeneous catalysis research, presents a significant challenge due to hierarchical technical constraints. To this respect, this work introduces a novel approach called process-constrained batch Bayesian optimization via Thompson…
Javad Azimi, Ali Jalali, Xiaoli Z. Fern
Bayesian Optimization (BO) aims at optimizing an unknown function that is costly to evaluate. We focus on applications where concurrent function evaluations are possible. In such cases, BO could choose to either sequentially evaluate the function (sequential mode) or evaluate the function at a batch of multiple inputs…
Huiyue Xu, Juping Shao, Yanan Sun, Joy Nondy
In the context of e-commerce, the order batching optimization problem in e-commerce warehousing centers has been addressed by establishing a model aimed at minimizing the order picking time, order delay costs, and picking costs, as well as achieving workload balance. An improved NSGA-II algorithm has been designed…
Dawei Zhan, Zeng Zhao-xi, Wei Sui, Ping Wu
—Extending Bayesian optimization to batch evaluation can enable the designer to make the most use of parallel computing technology. Most of current batch approaches use artificial functions to simulate the sequential Bayesian optimization algorithm's behavior to select a batch of points for parallel evaluation.…
Paul Stapor, Leonard Schmiester, Christoph Wierling, Bodo M.H. Lange + 2 more
Quantitative dynamical models are widely used to study cellular signal processing. A critical step in modeling is the estimation of unknown model parameters from experimental data. As model sizes and datasets are steadily growing, established parameter optimization approaches for mechanistic models become…
Adelle Holder, Henry DeBruin, Jesse M. Sestito, Yuanchao Liu
Bayesian optimization is a surrogate-based global optimization method that is increasingly being used for engineering design. However, most methods are designed to be sequential, and in optimization problems where the functional evaluation can be easily parallelized, batch methods are more effective at reducing…
Victor Abu-Marrul, Rafael Martinelli, Sílvio Hamacher, Irina Gribkovskaia
'Irina Gribkovskaia'] In this paper, we address a variant of a batch scheduling problem with identical parallel machines and non-anticipatory family setup times to minimize the total weighted completion time. We developed an ILS and a GRASP matheuristics to solve the problem using a constructive heuristic and two…
Máté Mohácsi, Márk Patrik Török, Sára Sáray, Luca Tar + 1 more
Finding optimal parameters for detailed neuronal models is a ubiquitous challenge in neuroscientific research. Recently, manual model tuning has been replaced by automated parameter search using a variety of different tools and methods. However, using most of these software tools and choosing the most appropriate…
Anirudh Srikanth, Carlotta Trigila, Emilie Roncali
The demand for specialized hardware to train AI models has increased in tandem with the increase in the model complexity over the recent years. Graphics processing unit (GPU) is one such hardware that is capable of parallelizing operations performed on a large chunk of data. Companies like Nvidia, AMD, and Google have…
Edward Ma, James Morrissey, Shutong Duan, Ziqi Lu + 10 more
Process optimization for Chinese hamster ovary (CHO) cell culture remains a challenge in biopharmaceutical development because multiple interacting parameters jointly influence productivity and product quality attributes. Traditional design-of-experiments (DoE) methods, while systematic, become impractically expensive…
Rubaiyat Mohammad Khondaker, Stephen Gow, Samantha Kanza, Jeremy G Frey + 1 more
'Jeremy G Frey' 'Mahesan Niranjan'] The related problems of chemical reaction optimization and reaction scope search concern the discovery of reaction pathways and conditions that provide the best percentage yield of a target product. The space of possible reaction pathways or conditions is too large to search in full…
Pablo Quijano Velasco, Kedar Hippalgaonkar, Balamurugan Ramalingam
The discovery of optimal conditions of chemical reactions is a labor-intensive, time-consuming task that requires exploring a high-dimensional parametric space. Historically the optimization of chemical reactions has been performed by manual experimentation guided by human intuition and Design of Experiments where one…
Patrick Koch, Oleg Golovidov, Steven D. Gardner, Brett Wujek + 2 more
'Joshua Griffin' 'Yan Xu'] Machine learning applications often require hyperparameter tuning. The hyperparameters usually drive both the efficiency of the model training process and the resulting model quality. For hyperparameter tuning, machine learning algorithms are complex black-boxes. This creates a class of…
Aidan Slattery, Zhenghui Wen, Pauline Tenblad, Diego Pintossi + 3 more
The optimization, intensification, and scaling up of chemical processes are essential and time-consuming aspects of contemporary chemical manufacturing, necessitating expertise and precision due to their intricate and sensitive nature. However, these process development problems are often carried out independently and…
Stephan Grein, David R. Penas, Daniel Weindl, Polina Lakrisenko + 2 more
Dynamic models are central to the computational life sciences but typically contain unknown parameters that must be inferred from experimental data. High-throughput measurements have made this task increasingly challenging, yielding high-dimensional search spaces and non-convex objectives with many local optima. This…
Lisa Laux, Marie F.A. Cutiongco, Nikolaj Gadegaard, Bjørn Sand Jensen
Automatic profiling of cell morphology is a powerful tool for inferring cell function. However, this technique retains a high barrier to entry. In particular, configuring image processing parameters for optimal cell profiling is susceptible to cognitive biases and dependent on user experience. Here, we use interactive…
Alejandro F. Villaverde, Fabian Fröhlich, Daniel Weindl, Jan Hasenauer + 1 more
Mechanistic kinetic models usually contain unknown parameters, which need to be estimated by optimizing the fit of the model to experimental data. This task can be computationally challenging due to the presence of local optima and ill-conditioning. While a variety of optimization methods have been suggested to…
Yifan Wu, Aron Walsh, Alex Ganose
What is the minimum number of experiments, or calculations, required to find an optimal solution? Relevant chemical problems range from identifying a compound with target functionality within a given phase space to controlling materials synthesis and device fabrication conditions. A common feature in this application…
Riley Hickman, Priyansh Parakh, Austin Cheng, Qianxiang Ai + 3 more
Experiment planning algorithms are a required component of autonomous platforms for scientific discovery. Selecting a suitable optimization algorithm for a novel application is an important yet difficult choice a researcher has to make based on past empirical performance on similar tasks. To facilitate the evaluation…
Sterling Baird, Jason R. Hall, Taylor D. Sparks
Would you rather search for a line inside a cube or a point inside a square? This type of solution degeneracy often exists in physics-based simulations and wet-lab experiments, but constraining these degeneracies is often unsupported or difficult to implement in many optimization packages, requiring additional time and…
Riley Hickman, Matteo Aldeghi, Alán Aspuru-Guzik
Model-based optimization strategies, such as Bayesian optimization (BO), have been deployed across the natural sciences in design and discovery campaigns due to their sample efficiency and flexibility. The combination of such strategies with automated laboratory equipment and/or high-performance computing in a…