Multi-objective biopharma capacity planning under uncertainty using a flexible genetic algorithm approach

This paper presents a flexible genetic algorithm optimisation approach for multi-objective biopharmaceutical planning problems under uncertainty. The optimisation approach combines a continuous-time heuristic model of a biopharmaceutical manufacturing process, a variable-length multi-objective genetic algorithm, and Graphics Processing Unit (GPU)-accelerated Monte Carlo simulation. The proposed approach accounts for constraints and features such as rolling product sequence-dependent changeovers, multiple intermediate demand due dates, product QC/QA release times, and pressure to meet uncertain product demand on time. An industrially-relevant case study is used to illustrate the functionality of the approach. The case study focused on optimisation of conflicting objectives, production throughput, and product inventory levels, for a multi-product biopharmaceutical facility over a 3-year period with uncertain product demand. The advantages of the multi-objective GA with the embedded Monte Carlo simulation were demonstrated by comparison with a deterministic GA tested with Monte Carlo simulation post-optimisation

A Multi-objective Genetic Algorithm for Peptide Optimization

The peptide-based drug design process requires the identification of a wide range of candidate molecules with specific biological, chemical and physical properties. The laboratory analysis in terms of in vitro methods for the discovery of several physiochemical properties of theoretical candidate molecules is time- and cost-intensive. Hence, in silico methods are required for this purpose. Metaheuristics like evolutionary algorithms are considered to be adequate in silico methods providing good approximate solutions to the underlying multiobjective optimization problems. The general issue in this area is the design of a multi-objective evolutionary algorithm to achieve a maximum number of high-quality candidate peptides that differ in their genetic material, in a minimum number of generations. A multi-objective evolutionary algorithm as an in silico method of discovering a large number of high-quality peptides within a low number of generations for a broad class of molecular optimization problems of different dimensions is challenging, and the development of such a promising multi-objective evolutionary algorithm based on theoretical considerations is the major contribution of this thesis. The design of this algorithm is based on a qualitative landscape analysis applied on a three- and four-dimensional biochemical optimization problem. The conclusions drawn from the empirical landscape analysis of the three- and four-dimensional optimization problem result in the formulation of hypotheses regarding the types of evolutionary algorithm components which lead to an optimized search performance for the purpose of peptide optimization. Starting from the established types of variation operators and selection strategies, different variation operators and selection strategies are proposed and empirically verified on the three- and four-dimensional molecular optimization problem with regard to an optimized interaction and the identification of potential interdependences as well as a fine-tuning of the parameters. Moreover, traditional issues in the field of evolutionary algorithms such as selection pressure and the influence of multi-parent recombination are investigated

Oscar : Optimal subset cardinality regression using the L0-pseudonorm with applications to prognostic modelling of prostate cancer

Author summaryFeature subset selection has become a crucial part of building biomedical models, due to the abundance of available predictors in many applications, yet there remains an uncertainty of their importance and generalization ability. Regularized regression methods have become popular approaches to tackle this challenge by balancing the model goodness-of-fit against the increasing complexity of the model in terms of coefficients that deviate from zero. Regularization norms are pivotal in formulating the model complexity, and currently L-1-norm (LASSO), L-2-norm (Ridge Regression) and their hybrid (Elastic Net) dominate the field. In this paper, we present a novel methodology that is based on the L-0-pseudonorm, also known as the best subset selection, which has largely gone overlooked due to its challenging discrete nature. Our methodology makes use of a continuous transformation of the discrete optimization problem, and provides effective solvers implemented in a user friendly R software package. We exemplify the use of oscar-package in the context of prostate cancer prognostic prediction using both real-world hospital registry and clinical cohort data. By benchmarking the methodology against existing regularization methods, we illustrate the advantages of the L-0-pseudonorm for better clinical applicability, selection of grouped features, and demonstrate its applicability in high-dimensional transcriptomics datasets.In many real-world applications, such as those based on electronic health records, prognostic prediction of patient survival is based on heterogeneous sets of clinical laboratory measurements. To address the trade-off between the predictive accuracy of a prognostic model and the costs related to its clinical implementation, we propose an optimized L-0-pseudonorm approach to learn sparse solutions in multivariable regression. The model sparsity is maintained by restricting the number of nonzero coefficients in the model with a cardinality constraint, which makes the optimization problem NP-hard. In addition, we generalize the cardinality constraint for grouped feature selection, which makes it possible to identify key sets of predictors that may be measured together in a kit in clinical practice. We demonstrate the operation of our cardinality constraint-based feature subset selection method, named OSCAR, in the context of prognostic prediction of prostate cancer patients, where it enables one to determine the key explanatory predictors at different levels of model sparsity. We further explore how the model sparsity affects the model accuracy and implementation cost. Lastly, we demonstrate generalization of the presented methodology to high-dimensional transcriptomics data.Peer reviewe

River Basin Water Quality Management Models: A State-of-the-Art Review

With the increasing human activities within river basins, the problem of water quality management is becoming increasingly important. Quality management can be achieved through control/prevention measures that have various economic and water quality implications. To facilitate the analysis of available management options, decision models are needed which represent the many facets of the problem. Such models must be capable of adequately depicting the hydrological, chemical and biological processes occurring in the river; while incorporating social, economic and political considerations within the decision framework. Management analyses can be performed using simulation, optimization, or both, depending on the management goal and the size and type of the problem. The critical issues in a management model are the nonlinearities, uncertainties, multiple pollutant nature of waste discharges, multiple objectives, and the spatial and temporal distribution of management actions. Literature on various management models were reviewed under the headings of linear, nonlinear and dynamic programming approaches; their stochastic counterparts, and combined or miscellaneous approaches. Dynamic programming was found to be an attractive methodology which can exploit the sequential decision problem pertaining to river basin water quality problems (downstream control actions do not influence water quality upstream). DP handles discrete decision variables which represent discrete management alternatives, and it is generic in the sense that both linear and non-linear water quality models expressing the relation between emissions and ambient quality levels can be incorporated. An example problem is presented which demonstrates the application of a DP-based management model to formulate least-cost strategies for the Nitra River basin in Slovakia. However, it is hardly possible for a single model to represent all the aspects of a complex decision problem. Different types of management models (e.g. deterministic vs stochastic models) have different capabilities and limitations. The only way to compensate for the deficiencies is to perform the analysis in a sensitivity style. The necessity for sensitivity analyses is further implied due to the fact that water quality problems are rather loosely formulated with respect to the quality and economic goals

Multi-Criteria Decision Matrix Method in the Risk Analysis of Biodiesel Production Processes

Renewable fuel technologies aim to mitigate the non-renewability of fossil fuels, challenges with increased energy demand, and the climate impact of fossil fuel emissions. However, before investing in renewable technologies, there need to be decision strategies that assess and identify the best alternatives according to stakeholder priorities. There is also a concern about whether the technologies that are the “most sustainable” effectively meet the acceptable risk requirements of stakeholders. In response to this question, a risk-adapted multi-criteria decision model was developed and compared to a sustainability study that evaluated five renewable diesel technologies, including Green Diesel I, II, and III; Fischer-Tropsch biodiesel, and the transesterification of biodiesel from vegetable oils. This thesis work provides essential stakeholder perspectives on the risk of these same five technologies and limits the use of probabilistic quantification approaches. Instead, this study uses reasonable assumptions to measure the indicator data objectively. These quantified indicators are considered a cost or benefit and allow adequate comparison of less mature technologies where historical data may be unavailable to more mature ones. This model uses the Analytical Hierarchy Process (AHP) decision strategy with stakeholder survey input to determine criteria and sub-criteria weightings, while the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) subsequently ranks the alternative technologies. The criteria evaluated from a risk perspective include process safety, environmental, economic, technological, and social risks. This risk assessment process has ranked technologies producing alternative fuel types. However, it can also compare and rank bioproduct and process intensification technologies to fossil-derived products and more traditional production techniques. Moreover, the central conclusion of this work is that an even more comprehensive tool is needed that combines risk and sustainability aspects. This conclusion is due to the sustainability study indicating Fischer-Tropsch diesel as the best option. At the same time, the present risk research revealed it as the option with the most significant comparative risk

Statistical inference in mechanistic models: time warping for improved gradient matching

Inference in mechanistic models of non-linear differential equations is a challenging problem in current computational statistics. Due to the high computational costs of numerically solving the differential equations in every step of an iterative parameter adaptation scheme, approximate methods based on gradient matching have become popular. However, these methods critically depend on the smoothing scheme for function interpolation. The present article adapts an idea from manifold learning and demonstrates that a time warping approach aiming to homogenize intrinsic length scales can lead to a significant improvement in parameter estimation accuracy. We demonstrate the effectiveness of this scheme on noisy data from two dynamical systems with periodic limit cycle, a biopathway, and an application from soft-tissue mechanics. Our study also provides a comparative evaluation on a wide range of signal-to-noise ratios

Carnot Cycle and Heat Engine Fundamentals and Applications II

This second Special Issue connects both the fundamental and application aspects of thermomechanical machines and processes. Among them, engines have the largest place (Diesel, Lenoir, Brayton, Stirling), even if their environmental aspects are questionable for the future. Mechanical and chemical processes as well as quantum processes that could be important in the near future are considered from a thermodynamical point of view as well as for applications and their relevance to quantum thermodynamics. New insights are reported regarding more classical approaches: Finite Time Thermodynamics F.T.T.; Finite Speed thermodynamics F.S.T.; Finite Dimensions Optimal Thermodynamics F.D.O.T. The evolution of the research resulting from this second Special Issue ranges from basic cycles to complex systems and the development of various new branches of thermodynamics

On the design of a European bioeconomy that optimally contributes to sustainable development

The inevitability for a change in humankind's resource and fossil energy consumption is demonstrated by global crises such as the climate change, disturbances of natural cycles, and the loss of biodiversity. The sun provides sufficient energy to generate electricity and by photosynthesis, solar radiation is converted into energy chemically bound in biomolecules, which provide building blocks for the production of various materials, chemicals, or fuels. The bioeconomy puts biomass at the center of an economy that attempts to cover resource and energy demand by renewable materials to address the global challenges. However, the finiteness of the terrestrial surface limits renewables, requiring a prioritization of use. The Sustainable Development Goals (SDGs) provide a common ground for global peace, prosperity, improved health and education, reduced inequality, and spur economic growth while tackling climate change and biodiversity loss, making it the most comprehensive framework for defining objectives in the design of the bioeconomy. Against this background, this dissertation is particularly dedicated to the design of bioeconomic value chains based on agroforestry residues in the European Union, considering economic, environmental, and social objectives to optimally exploit the potential to contribute to a sustainable development. All objectives are matched to SDGs to unveil congruencies, conflicts and trade-offs between different goals, and to provide aggregated insights and courses of action in the agroforestry residue-based bioeconomy to politics, the scientific community, and corporate decision-makers. The availability of agroforestry residue volumes and their current uses is the first major concern of a bioeconomy aligned with the SDGs to be assessed in this work. Key findings are that the most promising agricultural residue in the EU is wheat straw, followed by maize stover, barley straw, and rapeseed straw, which together account for about 80% of EU’s cereals and oil crops residues. In forestry, waste bark from the two coniferous species, spruce and pine, are most promising with the highest supplies in Scandinavia and central EU. The time-series-based forecast model predicts a total increase of the bioeconomic potential of the prioritized agricultural feedstocks from 113 Mt in 2017 to 127 Mt in 2030. The forecast indicates the largest increase of all investigated crops for corn stover at up to 20% until 2030, while rapeseed straw production is forecasted to decrease in many regions. To take environmental and social aspects into account on a regional level, along with international competitiveness, this dissertation develops a multi-criteria strategic network design model for the planning of bioeconomic value chains. The environmental and social objectives are derived by means of Life Cycle Assessment and Social Life Cycle Assessment, respectively. The developed set of 35 economic, environmental, and social objective functions allows for the consideration of 16 of the 17 SDGs. The model is applied for the planning of a second-generation bioethanol production network based on agricultural residues in the EU. Single-criteria optimization shows that sustainably available agroforestry residues could substitute up to 22% of the petrol demand in the EU in 2018 under optimal production networks for certain objectives (i.a., global warming). For environmental objectives, the decision to substitute petrol or edible crops-based ethanol has the highest impact. The greenhouse gas benefits could amount to up to 59 Mt CO2 eq., conforming to about 1.35% of the EU’s 2018 total emissions. However, global warming optimization leads to opportunity costs for other objectives. While for ecosystem quality, for example, the achieved value reaches 50% of its optimum, other categories like land use and water consumption could even be net deteriorated by optimizing global warming. For objectives such as land use, only 19% of the total agroforestry residues is used to substitute 100% of the edible crops-based ethanol, which would free up 11.7 billion m2 crop land. Social objectives lead to large and labor-intensive production networks distributed all over the EU. Depending on the social objective, the value creation slightly shifts regionally. To optimize local employment, the network relocates to regions with high unemployment rates, such as Spain, Italy, and parts of France. Economically strong metropolitan regions are at a disadvantage in favor of weaker regions of Central and Eastern EU when optimizing economic development. At best, up to 140,000 new jobs could be created in the EU while 12,000 jobs could be lost due to substitution of reference products. In terms of network extend, most socially and environmentally optimal production networks are similar, although the substitution decision has little impact for social objectives. This means that interesting trade-offs between social and environmental objectives can be found with only minor sacrifices. Economically optimal networks are much smaller and more centralized than environmental ones, and lead to costs of about 0.75 €/l second-generation ethanol. Environmental optimization results in cost between 0.88 €/l to 2.00 €/l, which implies that large-scale bioethanol production is not economically feasible with today’s oil prices and taxes. While the single-criteria optimization reveals conflicts within and between the environment, social, and economic dimensions, Pareto optimization is conducted to unveil trade-offs between conflicting goals. Significant environmental and social benefits can often be realized with only small economic detriments, and vice versa, economic profitability can substantially be improved at low environmental opportunity cost. Furthermore, the applied Pareto optimization shows that the endpoints human health and ecosystem quality are suitable aggregators of environmental impact categories, wherefore they could serve as representative of the environmental dimension in decision-making. Nonetheless, a transparent consideration of a broad range of impacts and knowledge about the categories’ contributions remains indispensable to reveal possible negative consequences of a decision. In a final step, the objective functions are matched to SDGs, and opportunity cost between the objective functions are calculated to unveil congruencies and conflicts between different goals. The assessment of relationships between the different SDGs supports the perception that different aspects of sustainability are not equally directed. Sustainability, expressed by the SDGs, is rather case-specific and varies between a multitude of interdependent social, environmental, and economic criteria. Decision-makers, whether at the corporate level pursuing one or more business objectives or at the policy level, using the SDGs as a framework, should be aware of the reciprocities between the different criteria. The dissertation shows that the European bioeconomy has a great potential to contribute to sustainable development. Multi-criteria optimization models enable sound trade-off decisions that are aligned to the SDGs