26 papers · ranked by Valyu relevance
Chao Ning, Fengqi You
This paper reviews recent advances in the field of optimization under uncertainty via a modern data lens, highlights key research challenges and promise of data-driven optimization that organically integrates machine learning and mathematical programming for decision-making under uncertainty, and identifies potential…
Martin Biel, Mikael Johansson
We present StochasticPrograms.jl, a user-friendly and powerful open-source framework for stochastic programming written in the Julia language. The framework includes both modeling tools and structure-exploiting optimization algorithms. Stochastic programming models can be efficiently formulated using expressive syntax…
Hyunwoo Lee, Seokhyun Chung, Taesu Cheong, Sang Hwa Song
Kidney exchange programs, which allow a potential living donor whose kidney is incompatible with his or her intended recipient to donate a kidney to another patient in return for a kidney that is compatible for their intended recipient, usually aims to maximize the number of possible kidney exchanges or the total…
Qi Li, Guiping Hu, Baogui Xin
Farmland management and irrigation scheduling are vital to a productive agricultural economy. A multistage stochastic programming model is proposed to maximize farmers’ annual profit under uncertainty. The uncertainties considered include crop prices, irrigation water availability, and precipitation. During the first…
Roberto Rossi
—Stochastic Programming is a framework for modelling and solving problems of decision making under uncertainty. Stochastic Dynamic Programming is a branch of Stochastic Programming that takes a "functional equation" approach to the discovery of optimal policies. By leveraging constructs lambda expressions, functional…
Ronald Hochreiter
There are many approaches towards solving decision problems under uncertainty. Throughout this paper, we will focus on solving Stochastic Programming problems - both the classical single-stage as well as the multi-stage case. This article intends to provide ideas on how to use Evolutionary Optimization techniques to…
Xavier Cabezas, Sergio García, Carlos Martin-Barreiro, Erwin Delgado + 2 more
'Víctor Leiva' 'James Covington'] Healthcare service centers must be sited in strategic locations that meet the immediate needs of patients. The current situation due to the COVID-19 pandemic makes this problem particularly relevant. Assume that each center corresponds to an assigned place for vaccination and that each…
José Emmanuel Gómez-Rocha, Eva Selene Hernández-Gress, Héctor Rivera-Gómez, Dragan Pamucar
'Héctor Rivera-Gómez' 'Dragan Pamucar'] In this article two multi-stage stochastic linear programming models are developed, one applying the stochastic programming solver integrated by Lingo 17.0 optimization software that utilizes an approximation using an identical conditional sampling and Latin-hyper-square…
Francesca Maggioni, Florian A. Potra, Marida Bertocchi
In this paper we analyze the effect of two modelling approaches for supply planning problems under uncertainty: two-stage stochastic programming (SP) and robust optimization (RO). The comparison between the two approaches is performed through a scenario-based framework methodology, which can be applied to any…
Robert L. Burdett, Paul Corry, Belinda Spratt, David Cook + 2 more
'Prasad Yarlagadda' 'Mazyar Ghadiri Nejad'] This article introduces a bespoke risk averse stochastic programming approach for performing a strategic level assessment of hospital capacity (QAHC). We include stochastic treatment durations and length of stay in the analysis for the first time. To the best of our knowledge…
Jonathan Ferrer-Mestres, Thomas G. Dietterich, Olivier Buffet, Iadine Chadès
In conservation of biodiversity, natural resource management and behavioural ecology, stochastic dynamic programming, and its mathematical framework, Markov decision processes (MDPs), are used to inform sequential decision-making under uncertainty. Models and solutions of Markov decision problems should be…
Beste Basciftci, Shabbir Ahmed, Nagi Gebraeel
Multi-stage stochastic programming is a well-established framework for sequential decision making under uncertainty by seeking policies that are fully adapted to the uncertainty. Often, e.g. due to contractual constraints, such flexible and adaptive policies are not desirable, and the decision maker may need to commit…
Vincent Guigues
We introduce the class of multistage stochastic optimization problems with a random number of stages. For such problems, we show how to write dynamic programming equations and how to solve these equations using the Stochastic Dual Dynamic Programming algorithm. Finally, we consider a portfolio selection problem over an…
Oğuz Mesüm, Ali Rana Atilgan, Burak Kocuk
Antibiotics Time Machine is an important problem to understand antibiotic resistance and how it can be reversed. Mathematically, it can be modelled as follows: Consider a set of genotypes, each of which contain a set of mutated and unmutated genes. Suppose that a set of growth rate measurements of each genotype under a…
Mike Hewitt, Giovanni Pantuso
We study the problem of determining how much finished goods inventory to source from different capacitated facilities in order to maximize profits resulting from sales of such inventory. We consider a problem wherein there is uncertainty in demand for finished goods inventory and production yields at facilities.…
João Flávio de Freitas Almeida, Samuel Vieira Conceição, Luiz Ricardo Pinto, Ricardo Saraiva de Camargo + 2 more
'Luiz Ricardo Pinto' 'Ricardo Saraiva de Camargo' 'Gilberto de Miranda Júnior' 'Yong Deng'] Multiechelon supply chains are complex logistics systems that require flexibility and coordination at a tactical level to cope with environmental uncertainties in an efficient and effective manner. To cope with these challenges…
Berhanu Belay, Adane Abebaw, Omar A. Alzubi
This manuscript presents a technique for solving a multiple-objective probabilistic fractional programming problem with discrete random variables. A multiple-objective probabilistic mathematical model is constructed with fractional objectives. In the model, some parameters of coefficients and right hand side parameters…
Krishna Rijal, Pankaj Mehta
The Gillespie algorithm is commonly used to simulate and analyze complex chemical reaction networks. Here, we leverage recent breakthroughs in deep learning to develop a fully differentiable variant of the Gillespie algorithm. The differentiable Gillespie algorithm (DGA) approximates discontinuous operations in the…
Rowan Iskandar
Following its introduction over thirty years ago, the Markov cohort model state-transition has been used extensively to model population trajectories over time (Markov trace) in decision modeling and cost-effectiveness studies. We recently showed that a cohort model represents the average of a continuous-time…
Authors not listed
Continuous manufacturing processes offer significant advantages over batch processes, including easier scalability, reduced costs, lower raw material and solvent consumption, and improved energy efficiency. A robust techno-economic assessment is therefore essential to evaluate and facilitate the adoption of such…
Uri Hertz, Bahador Bahrami, Mehdi Keramati
Every day we make choices under uncertainty; choosing what route to work or which queue in a supermarket to take, for example. It is unclear how outcome variance, e.g. waiting time in a queue, affects decisions when outcome is stochastic and continuous. For example, how does one choose between an option with unreliable…
Authors not listed
Optimizing the synthesis conditions of advanced materials is challenging, especially when outcomes are subject to inherent experimental uncertainties. Bayesian optimization is a popular tool for accelerating materials discovery, but its standard risk-neutral framework overlooks the variability of outcomes under…
Richard D. Paul, Johann F. Jadebeck, Anton Stratmann, Wolfgang Wiechert + 1 more
Effective collaboration between developers of Bayesian inference methods and users is key to advance our quantitative understanding of biosystems. We here present hopsy, a versatile open source platform designed to provide convenient access to powerful Markov chain Monte Carlo sampling algorithms tailored to models…
Authors not listed
Machine olfaction—the artificial replication of the sense of smell—faces significant challenges due to the absence of large, standardized training datasets. Unlike vision, language, and audio models, which benefit from extensive corpora such as ImageNet, GLUE, and AudioSet, olfaction lacks scaled equivalents and…
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…
Sterling Baird, Jason R. Hall, Taylor D. Sparks
Would you rather search for a line inside a cube or a point inside a square? Physics-based simulations and wet-lab experiments often have symmetries (degeneracies) that allow reducing problem dimensionality or search space, but constraining these degeneracies is often unsupported or difficult to implement in many…