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Distributed biomass supply chain cost optimization to evaluate multiple feedstocks for a biorefinery

APPLIED ENERGY 2019年 254卷 113660-000页

作者： Roni, Mohammad S. Thompson, David N. Hartley, Damon S. Idaho Natl Lab Data & Software Sci Idaho Falls ID 83402 USA Idaho Natl Lab Bioenergy Technol Dept Idaho Falls ID USA

Conventional practices of siting all biomass preprocessing operations at the biorefinery is widely believed to be the most cost-effective solution for feedstock supply because of economies of scale. However, biomass preprocessing operations could be decentralized by moving the preprocessing operations to distributed biomass preprocessing centers, also known as "depots" located near biomass sources. This study presents a comparative case study with multiple biomass resources to analyze biorefinery feedstock supply logistics designs having distributed depots and a primary depot co-located with the biorefinery. A mixed-integer linear programming model was developed to simultaneously optimize feedstock sourcing decisions, and optimal preprocessing depot locations and size, utilizing biomass resources from agricultural residue, energy and municipal solid waste to meet carbohydrate specifications and feedstock demand for a biochemical conversion process. Results from a case study in the US showed that a biorefinery could increase its feedstock supply draw area and supply volume by 57.3%, 177.4% respectively without increasing the feedstock delivered cost by adopting distributed depot-in the feedstock supply chain design. A distributed-depot-based supply chain can be more economical by selecting optimal mix of biomass resource, optimal siting and depot scales during feedstock supply chain design. The findings from this study indicate that a biorefinery can utilize dynamic blending to meet the feedstock quality specifications as well as larger supply radius in the distributed depot-based supply chain design to access more available biomass to handle potential feedstock supply uncertainty.

关键词： Feedstock supply chain mixed-integer linear programming Supply radius Depot Biomass Dynamic blending

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A comparative analysis of mixed-integer linear formulations for the multi-family capacitated lot-sizing problem

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JOURNAL OF INDUSTRIAL AND PRODUCTION ENGINEERING 2018年第4期35卷 229-242页

作者： Arias, Andrea L. Gatica, Ricardo A. Pontificia Univ Catolica Valparaiso Sch Ind Engn Ind Engn Valparaiso Chile Pontificia Univ Catolica Valparaiso Sch Ind Engn Valparaiso Chile Texas Tech Univ Dept Ind Mfg & Syst Engn Lubbock TX 79409 USA

Despite having a very broad spectrum of applicability in practice, the multi-family capacitated lot-sizing problem (MFCLSP) has been scarcely studied. The MFCLSP is an extension of the capacitated lot-sizing problem with setup times (CLST) in which items are organized into families based on similar setup structures. In this paper, we propose three formulations for the MFCLSP (MF-TRAD, MF-ARBNET, and MF-EXREQ), and develop a comprehensive comparative analysis to evaluate their performance using a generic solver (CPLEX). Solving large-scale problems to optimality has been shown to consume a great amount of computational time, which is very impractical for real-life applications. Because of that, this study focuses on analyzing the performance of these formulations in a limited, and reasonable, amount of time. The results show the MF-EXREQ model outperforms the other two models in both the time to the first feasible solution and the quality of the solutions generated throughout the solving process.

关键词： mixed-integer linear programming production planning mathematical modeling multi-family lot-sizing solver performance

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mixed-integer programming Model for Profiling Disease Biomarkers from Gene Expression Studies 5th

Mixed-Integer Programming Model for Profiling Disease Biomar...

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5th International Work-Conference on Bioinformatics and Biomedical Engineering (IWBBIO)

作者： Santiago, Andre M. Rocha, Miguel Dourado, Antonio Arrais, Joel P. Univ Minho Dept Informat Braga Portugal Univ Coimbra CISUC Coimbra Portugal

ISBN: (纸本)9783319561547;9783319561530

Biomedical research has seen great advances in recent years, in great part due to the long-term aid of the ability to identify biological or genetic markers that uniquely match a given disease. Despite several successes stories, the reality is that most diseases still lack an effective way of treatment, and even diagnostic. While the emergence of -omic technologies, enabled the screening of a whole cell at the molecular level, the large quantities of data produced restricted the capability to extract valid outcomes. In this paper, we propose an optimization model, based of mixedinteger linear programming, capable of identifying a combination of biomarkers for distinguishing between healthy and diseased samples. The model achieves this taking several individuals' gene expression profiles, identifying the most relevant genes for differentiation and discovering the optimal combination of biomarkers that best explains the difference between both states. This model was validated on two different datasets through sampling analysis, achieving an out of sample accuracy up to 93%.

关键词： Biomarkers Disease diagnostic mixed-integer linear programming

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Predictive management of cogeneration-based energy supply networks using two-stage multi-objective optimization

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ENERGY 2018年 162卷 1269-1286页

作者： Wakui, Tetsuya Sawada, Kento Yokoyama, Ryohei Aki, Hirohisa Osaka Prefecture Univ Dept Mech Engn 1-1 Caktien Cho Sakai Osaka 5998531 Japan Univ Tsukuba Fac Engn Informat & Syst 1-1-1 Tennodai Tsukuba Ibaraki 3058573 Japan

A predictive management system for cogeneration unit-based energy supply networks using two-stage multi-objective optimization was developed to tackle a trade-off between energy savings and operating cost reduction. The developed system integrated support vector regression-based energy demand prediction, MILP (mixed-integer linear programming)-based schedule planning, and rule-based operation control. The contribution is to develop two-stage MILP-based multi-objective schedule planning, which is extension of an epsilon-constraint method, and operation control rule of multiple cogeneration units. In the first-stage schedule planning, primary energy consumption in the prediction horizon is minimized, and a reduction rate of primary energy consumption is calculated. In the second-stage schedule planning, an operating cost is minimized additionally subject to satisfaction of partial achievement of the reduction rate of primary energy consumption calculated in the first stage. An energy-saving achievement rate is regarded as a decision-making parameter to control a trade-off between energy savings and cost reduction, of which definition is quantitatively apprehensible for decision makers. Annual operating simulation of an energy supply network using four fuel-cell-based cogeneration units revealed that the developed predictive management system has high controllability to the trade-off between the energy saving rates (18.9%-21.6%) and the operating cost reduction rate (19.0%-15.6%), caused by a time-of-use power tariff structure. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Energy management Multi-objective optimization Microgrid Cogeneration Model predictive control mixed-integer linear programming

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A joint chance-constrained programming approach for the single-item capacitated lot-sizing problem with stochastic demand

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ANNALS OF OPERATIONS RESEARCH 2018年第1-2期264卷 123-155页

作者： Gicquel, Celine Cheng, Jianqiang Univ Paris Sud Lab Rech Informat Paris France Univ Arizona Dept Syst & Ind Engn Tucson AZ 85721 USA

We study the single-item single-resource capacitated lot-sizing problem with stochastic demand. We propose to formulate this stochastic optimization problem as a joint chance-constrained program in which the probability that an inventory shortage occurs during the planning horizon is limited to a maximum acceptable risk level. We investigate the development of a new approximate solution method which can be seen as an extension of the previously published sample approximation approach. The proposed method relies on a Monte Carlo sampling of the random variables representing the demand in all planning periods except the first one. Provided there is no dependence between the demand in the first period and the demand in the later periods, this partial sampling results in the formulation of a chance-constrained program featuring a series of joint chance constraints. Each of these constraints involves a single random variable and defines a feasible set for which a conservative convex approximation can be quite easily built. Contrary to the sample approximation approach, the partial sample approximation leads to the formulation of a deterministic mixed-integer linear problem having the same number of binary variables as the original stochastic problem. Our computational results show that the proposed method is more efficient at finding feasible solutions of the original stochastic problem than the sample approximation method and that these solutions are less costly than the ones provided by the Bonferroni conservative approximation. Moreover, the computation time is significantly shorter than the one needed for the sample approximation method.

关键词： Stochastic lot-sizing Chance-constrained programming Joint probabilistic constraint Sample approximation approach mixed-integer linear programming

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Designing Metabolic Division of Labor in Microbial Communities

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MSYSTEMS 2019年第2期4卷 e00263-18页

作者： Thommes, Meghan Wang, Taiyao Zhao, Qi Paschalidis, Ioannis C. Segre, Daniel Boston Univ Dept Biomed Engn Boston MA 02215 USA Boston Univ Biol Design Ctr Boston MA 02215 USA Boston Univ Dept Elect & Comp Engn Boston MA 02215 USA Boston Univ Div Syst Engn Boston MA 02215 USA Boston Univ Dept Biol 5 Cummington St Boston MA 02215 USA Boston Univ Dept Phys 590 Commonwealth Ave Boston MA 02215 USA Boston Univ Bioinformat Program Boston MA 02215 USA

Microbes face a trade-off between being metabolically independent and relying on neighboring organisms for the supply of some essential metabolites. This balance of conflicting strategies affects microbial community structure and dynamics, with important implications for microbiome research and synthetic ecology. A "gedanken" (thought) experiment to investigate this trade-off would involve monitoring the rise of mutual dependence as the number of metabolic reactions allowed in an organism is increasingly constrained. The expectation is that below a certain number of reactions, no individual organism would be able to grow in isolation and cross-feeding partnerships and division of labor would emerge. We implemented this idealized experiment using in silico genome-scale models. In particular, we used mixed-integer linear programming to identify trade-off solutions in communities of Escherichia coli strains. The strategies that we found revealed a large space of opportunities in nuanced and nonintuitive metabolic division of labor, including, for example, splitting the tricarboxylic acid (TCA) cycle into two separate halves. The systematic computation of possible solutions in division of labor for 1-, 2-, and 3-strain consortia resulted in a rich and complex landscape. This landscape displayed a non-linear boundary, indicating that the loss of an intracellular reaction was not necessarily compensated for by a single imported metabolite. Different regions in this landscape were associated with specific solutions and patterns of exchanged metabolites. Our approach also predicts the existence of regions in this landscape where independent bacteria are viable but are outcompeted by cross-feeding pairs, providing a possible incentive for the rise of division of labor. IMPORTANCE Understanding how microbes assemble into communities is a fundamental open issue in biology, relevant to human health, metabolic engineering, and environmental sustainability. A possible mechanism f

关键词： division of labor flux balance analysis genome-scale stoichiometric models metabolic networks microbiome mixed-integer linear programming resource allocation synthetic ecology microbial communities

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Location-allocation model for external beam radiotherapy as an example of an evidence-based management tool implemented in healthcare sector in Poland

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RADIOTHERAPY AND ONCOLOGY 2018年第1期127卷 154-160页

作者： Czerwinski, Adam Michal Wieckowska, Barbara Warsaw Sch Econ Dept Social Insurance Warsaw Poland Minist Hlth Dept Anal & Strategy Dluga 5 PL-00263 Warsaw Poland London Sch Econ & Polit Sci Dept Hlth Policy London England

Background and purpose: External beam radiotherapy (EBRT) is one of three key treatment modalities of cancer patients. Its utilisation and outcomes depend on a plethora of variables, one of which is the distance a patient must travel to undergo the treatment. The relation between distance and utilisation is clearly visible in Poland. At the same time no strategic investment plan is observed. This work proposes a method of resolving these two issues. Materials and methods: We propose a mixed-integer linear programming model that aims to optimise the distribution of linear accelerators among selected locations in such a way that a patient's journey to the nearest EBRT is as short as possible. The optimisation is done with observance of international guidelines concerning EBRT capacity. With the use of proposed theoretical framework, we develop a national, strategic plan for linear accelerator investments. Results: According to model assumptions decentralisation of EBRT, together with new equipment purchases, is required to ensure optimal access to EBRT. Conclusions: The results were incorporated into Healthcare Needs Maps for Poland. The plan based on the results of this study, implemented by 2025, should deal with the most pressing concerns of Polish EBRT. (C) 2018 Elsevier B.V. All rights reserved. Radiotherapy and Oncology 127 (2018) 154-160

关键词： External beam radiotherapy mixed-integer linear programming Cancer policy Evidence-based management in healthcare

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Focused Plant Optimization Strategy for Polyethylene Multi-grades and Multi-sites Production

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2018年第6期43卷 3173-3185页

作者： Alqahtani, Mushabeb Z. Shaikh, Arifusalam Ndiaye, Malick M. Prince Mohammad Bin Fahd Univ Coll Business Adm COBA Al Khobar 31952 Saudi Arabia King Fahd Univ Petr & Minerals Dept Syst Engn Dhahran 31261 Saudi Arabia

A petrochemical industry produces multiple grades of products along with a significant amount of 'off-spec' grades which are bi-products resulting from a transition phase between production of any two grades. While the optimization of grades production in different reactors is important, it is equally important to minimize the production of off-spec grades to minimize loss and reduce safety risks imposed on the reactor in every transition. The production of off-spec grades depends on many factors, particularly the number of grades produced in the reactor and the nature and duration of the transitions. In this study, an adoption of a 'focused plant' approach is used that meets the demand of different grades by assigning limited or focused grades to selected reactors based on their capabilities and constraints. A mixed-integer linear programming model is presented to determine the optimum grades selection for focused reactors of a plant and the quantity to be produced in each reactor to maximize overall profit. The model is applied on a case study with six reactors and twelve grades over a period of 1 year.

关键词： Focused plant Reactor optimization Mathematical modeling Petrochemical production mixed-integer linear programming

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A mixed-integer programming-based heuristic for project scheduling with work-content constraints

A mixed-integer programming-based heuristic for project sche...

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15th International Conference on Project Management and Scheduling (PMS)

作者： Zimmermann, Adrian Univ Bern Dept Business Adm Bern Switzerland

We consider the project scheduling problem in which each project activity has a prescribed work content that must be completed by a so-called work-content resource and the activities' resource usage may change over time. The resource usage must lie within prescribed bounds and cannot be changed for a minimum number of consecutive periods. The amount of resource units used determines the requirements for further resources. The activities must be scheduled such that the project makespan is minimised. For this problem, we devise a mixed-integer programming-based heuristic that schedules the activities iteratively. To improve the resource usage for multiple activities simultaneously, subsets of activities are rescheduled each time the activities' resource usage appears to be inefficient. Our computational results for a standard test set from the literature show that our heuristic outperforms the state-of-the-art method for medium-and large-sized instances, and that for many small-sized instances, optimal solutions are obtained.

关键词： MIP-based heuristic mixed-integer linear programming project scheduling flexible resource usage work content

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Prolific Compositions

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Discrete Mathematics and Theoretical Computer Science 2019年第2期21卷 1页

作者： Albert, Michael Tannock, Murray Department of Computer Science University of Otago Dunedin New Zealand

Under what circumstances might every extension of a combinatorial structure contain more copies of another one than the original did? This property, which we call prolificity, holds universally in some cases (e.g., finite linear orders) and only trivially in others (e.g., permutations). integer compositions, or equivalently layered permutations, provide a middle ground. In that setting, there are prolific compositions for a given pattern if and only if that pattern begins and ends with 1. For each pattern, there is an easily constructed automaton that recognises prolific compositions for that pattern. Some instances where there is a unique minimal prolific composition for a pattern are classified. © 2019 by the author(s)

关键词： mixed-integer linear programming

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