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Allende, H., Bravo, D., & Canessa, E. (2010). Robust design in multivariate systems using genetic algorithms. Qual. Quant., 44(2), 315–332.
Abstract: This paper presents a methodology based oil genetic algorithms, which finds feasible and reasonably adequate Solutions to problems of robust design in multivariate systems. We use a genetic algorithm to determine the appropriate control factor levels for simultaneously optimizing all of the responses of the system, considering the noise factors which affect it. The algorithm is guided by a desirability function which works with only one fitness function although the system May have many responses. We validated the methodology using data obtained from a real system and also from a process simulator, considering univariate and multivariate systems. In all cases, the methodology delivered feasible solutions, which accomplished the goals of robust design: obtain responses very close to the target values of each of them, and with minimum variability. Regarding the adjustment of the mean of each response to the target value, the algorithm performed very well. However, only in some of the multivariate cases, the algorithm was able to significantly reduce the variability of the responses.
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Alvarez-Miranda, E., & Pereira, J. (2017). Designing and constructing networks under uncertainty in the construction stage: Definition and exact algorithmic approach. Comput. Oper. Res., 81, 178–191.
Abstract: The present work proposes a novel Network Optimization problem whose core is to combine both network design and network construction scheduling under uncertainty into a single two-stage robust optimization model. The first-stage decisions correspond to those of a classical network design problem, while the second-stage decisions correspond to those of a network construction scheduling problem (NCS) under uncertainty. The resulting problem, which we will refer to as the Two-Stage Robust Network Design and Construction Problem (2SRNDC), aims at providing a modeling framework in which the design decision not only depends on the design costs (e.g., distances) but also on the corresponding construction plan (e.g., time to provide service to costumers). We provide motivations, mixed integer programming formulations, and an exact algorithm for the 2SRNDC. Experimental results on a large set of instances show the effectiveness of the model in providing robust solutions, and the capability of the proposed algorithm to provide good solutions in reasonable running times. (C) 2017 Elsevier Ltd. All rights reserved.
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Armstrong, M., Valencia, J., Lagos, G., & Emery, X. (2022). Constructing Branching Trees of Geostatistical Simulations. Math. Geosci., 54, 711–743.
Abstract: This paper proposes the use of multi-stage stochastic programming with recourse for optimised strategic open-pit mine planning. The key innovations are, firstly, that a branching tree of geostatistical simulations is developed to take account of uncertainty in ore grades, and secondly, scenario reduction techniques are applied to keep the trees to a manageable size. Our example shows that different mine plans would be optimal for the downside case when the deposit turns out to be of lower grade than expected compared to when it is of higher grade than expected. Our approach further provides th
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Canessa, E., Droop, C., & Allende, H. (2012). An improved genetic algorithm for robust design in multivariate systems. Qual. Quant., 46(2), 665–678.
Abstract: In a previous article, we presented a genetic algorithm (GA), which finds solutions to problems of robust design in multivariate systems. Based on that GA, we developed a new GA that uses a new desirability function, based on the aggregation of the observed variance of the responses and the squared deviation between the mean of each response and its corresponding target value. Additionally, we also changed the crossover operator from a one-point to a uniform one. We used three different case studies to evaluate the performance of the new GA and also to compare it with the original one. The first case study involved using data from a univariate real system, and the other two employed data obtained from multivariate process simulators. In each of the case studies, the new GA delivered good solutions, which simultaneously adjusted the mean of each response to its corresponding target value. This performance was similar to the one of the original GA. Regarding variability reduction, the new GA worked much better than the original one. In all the case studies, the new GA delivered solutions that simultaneously decreased the standard deviation of each response to almost the minimum possible value. Thus, we conclude that the new GA performs better than the original one, especially regarding variance reduction, which was the main problem exhibited by the original GA.
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Canessa, E., Vera, S., & Allende, H. (2012). A new method for estimating missing values for a genetic algorithm used in robust design. Eng. Optimiz., 44(7), 787–800.
Abstract: This article presents an improved genetic algorithm (GA), which finds solutions to problems of robust design in multivariate systems with many control and noise factors. Since some values of responses of the system might not have been obtained from the robust design experiment, but may be needed in the search process, the GA uses response surface methodology (RSM) to estimate those values. In all test cases, the GA delivered solutions that adequately adjusted the mean of the responses to their corresponding target values and with low variability. The GA found more solutions than the previous versions of the GA, which makes it easier to find a solution that may meet the trade-off among variance reduction, mean adjustment and economic considerations. Moreover, RSM is a good method for estimating the mean and variance of the outputs of highly non-linear systems, which makes the new GA appropriate for optimizing such systems.
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Cano-Martinez, M. J., Carrasco, M., Sandoval, J., & Gonzalez-Martin, C. (2022). Quantitative Analysis of Visual Representation of Sign Elements in COVID-19 Context. Empir. Stud. Arts, 41(1), 31–51.
Abstract: Visual representation as a means of communication uses elements to build a narrative. We propose using computer analysis to perform a quantitative analysis of the elements used in the visual creations that have been produced in reference to the epidemic, using 927 images compiled from The Covid Art Museum's Instagram account. This process has been carried out with techniques based on deep learning to detect objects contained in each study image. The research reveals the elements that are repeated in images to create narratives and the relations of association that are established in the sample. The predominant discourses in the sample do not show concern for the effects of illness. On the contrary, the impact and effects of confinement, through the prominent presence of elements such as human figures, windows, and buildings, are the most expressed experiences in the creations analyzed.
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De la Iglesia, R., Valenzuela-Heredia, D., Pavissich, J. P., Freyhoffer, S., Andrade, S., Correa, J. A., et al. (2010). Novel polymerase chain reaction primers for the specific detection of bacterial copper P-type ATPases gene sequences in environmental isolates and metagenomic DNA. Lett. Appl. Microbiol., 50(6), 552–562.
Abstract: Aims: In the last decades, the worldwide increase in copper wastes release by industrial activities like mining has driven environmental metal contents to toxic levels. For this reason, the study of the biological copper-resistance mechanisms in natural environments is important. Therefore, an appropriate molecular tool for the detection and tracking of copper-resistance genes was developed. Methods and Results: In this work, we designed a PCR primer pair to specifically detect copper P-type ATPases gene sequences. These PCR primers were tested in bacterial isolates and metagenomic DNA from intertidal marine environments impacted by copper pollution. As well, T-RFLP fingerprinting of these gene sequences was used to compare the genetic composition of such genes in microbial communities, in normal and copper-polluted coastal environments. New copper P-type ATPases gene sequences were found, and a high degree of change in the genetic composition because of copper exposure was also determined. Conclusions: This PCR based method is useful to track bacterial copper-resistance gene sequences in the environment. Significance and Impact of the Study: This study is the first to report the design and use of a PCR primer pair as a molecular marker to track bacterial copper-resistance determinants, providing an excellent tool for long-term analysis of environmental communities exposed to metal pollution.
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Fukushima, K., Kabir, M., Kanda, K., Obara, N., Fukuyama, M., & Otsuki, A. (2022). Equivalent Circuit Models: An Effective Tool to Simulate Electric/Dielectric Properties of Ores-An Example Using Granite. Materials, 15(13), 4549.
Abstract: The equivalent circuit model is widely used in high-voltage (HV) engineering to simulate the behavior of HV applications for insulation/dielectric materials. In this study, equivalent circuit models were prepared in order to represent the electric and dielectric properties of minerals and voids in a granite rock sample. The HV electric-pulse application shows a good possibility of achieving a high energy efficiency with the size reduction and selective liberation of minerals from rocks. The electric and dielectric properties were first measured, and the mineral compositions were also determined by using a micro-X-ray fluorescence spectrometer. Ten patterns of equivalent circuit models were then prepared after considering the mineral distribution in granite. Hard rocks, as well as minerals, are dielectric materials that can be represented as resistors and capacitors in parallel connections. The values of the electric circuit parameters were determined from the known electric and dielectric parameters of the minerals in granite. The average calculated data of the electric properties of granite agreed with the measured data. The conductivity values were 53.5 pS/m (measurement) and 36.2 pS/m (simulation) in this work. Although there were some differences between the measured and calculated data of dielectric loss (tan delta), their trend as a function of frequency agreed. Even though our study specifically dealt with granite, the developed equivalent circuit model can be applied to any other rock.
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Gonzalez-Martin, C., Carrasco, M., & Oviedo, G. (2022). Analysis of the Use of Color and Its Emotional Relationship in Visual Creations Based on Experiences during the Context of the COVID-19 Pandemic. Sustainability, 14(20), 12989.
Abstract: Color is a complex communicative element. At the level of artistic creation, this component influences both formal aspects and symbolic weight, directly affecting the construction of the message, and its associated emotion. During the COVID-19 pandemic, people generated countless images transmitting the subjective experiences of this event, and the social network Instagram was used to share this visual material. Using the repository of images created in the Instagram account CAM (The COVID Art Museum), we propose a methodology to understand the use of color and its emotional relationship in this context. The proposed methodology consists of creating a model that learns to recognize emotions via a convolutional neural network using the ArtEmis database. This model will subsequently be applied to recognize emotions in the CAM dataset, also extracting color attributes and their harmonies. Once both processes are completed, we combine the results, generating an expanded discussion on the usage of color and emotion. The results indicate that warm colors and analog compositions prevail in the sample. The relationship between emotions and composition shows a trend in positive emotions, reinforced by the results of the emotional relationship analysis of color attributes (hue, saturation, and lighting).
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Loira, N., Mendoza, S., Cortes, M. P., Rojas, N., Travisany, D., Di Genova, A., et al. (2017). Reconstruction of the microalga Nannochloropsis salina genome-scale metabolic model with applications to lipid production. BMC Syst. Biol., 11, 17 pp.
Abstract: Background: Nannochloropsis salina (= Eustigmatophyceae) is a marine microalga which has become a biotechnological target because of its high capacity to produce polyunsaturated fatty acids and triacylglycerols. It has been used as a source of biofuel, pigments and food supplements, like Omega 3. Only some Nannochloropsis species have been sequenced, but none of them benefit from a genome-scale metabolic model (GSMM), able to predict its metabolic capabilities. Results: We present iNS934, the first GSMM for N. salina, including 2345 reactions, 934 genes and an exhaustive description of lipid and nitrogen metabolism. iNS934 has a 90% of accuracy when making simple growth/no-growth predictions and has a 15% error rate in predicting growth rates in different experimental conditions. Moreover, iNS934 allowed us to propose 82 different knockout strategies for strain optimization of triacylglycerols. Conclusions: iNS934 provides a powerful tool for metabolic improvement, allowing predictions and simulations of N. salina metabolism under different media and genetic conditions. It also provides a systemic view of N. salina metabolism, potentially guiding research and providing context to -omics data.
Keywords: Genome-scale Metabolic model; Nannochloropsis salina; TAG; Microalg ae
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Mascareno, A., Goles, E., & Ruz, G. A. (2016). Crisis in complex social systems: A social theory view illustrated with the chilean case. Complexity, 21(S2), 13–23.
Abstract: The article argues that crises are a distinctive feature of complex social systems. A quest for connectivity of communication leads to increase systems' own robustness by constantly producing further connections. When some of these connections have been successful in recent operations, the system tends to reproduce the emergent pattern, thereby engaging in a non-reflexive, repetitive escalation of more of the same communication. This compulsive growth of systemic communication in crisis processes, or logic of excess, resembles the dynamic of self-organized criticality. Accordingly, we first construct the conceptual foundations of our approach. Second, we present three core assumptions related to the generative mechanism of social crises, their temporal transitions (incubation, contagion, restructuring), and the suitable modeling techniques to represent them. Third, we illustrate the conceptual approach with a percolation model of the crisis in Chilean education system. (c) 2016 Wiley Periodicals, Inc. Complexity 21: 13-23, 2016
Keywords: crisis; social complexity; social systems; communication; contagion
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Petrou, K., Procopiou, A. T., Gutierrez-Lagos, L., Liu, M. C. Z., Ochoa, L. F., Langstaff, T., et al. (2021). Ensuring Distribution Network Integrity Using Dynamic Operating Limits for Prosumers. IEEE Trans. Smart Grid, 12(5), 3877–3888.
Abstract: The number of residential consumers with solar PV and batteries, aka prosumers, has been increasing in recent years. Incentives now exist for prosumers to operate their batteries in more profitable ways than self-consumption mode. However, this can increase prosumer exports or imports, resulting in power flows that can lead to voltage and thermal limit violations in distribution networks. This work proposes a framework for Distribution Network Operators (DNOs) to ensure the integrity of MV-LV networks by using dynamic operating limits for prosumers. Periodically, individual prosumers send their intended operation (net exports/imports) as determined by their local control to the DNO who then assesses network integrity using smart meter data and a power flow engine. If a potential violation is detected, their maximum operating limits are determined based on a three-phase optimal power flow that incorporates network constraints and fairness aspects. A real Australian MV feeder with realistically modelled LV networks and 4,500+ households is studied, where prosumers' local controls operate based on energy prices. Time-series results demonstrate that the proposed framework can help DNOs ensure network integrity and fairness across prosumers. Furthermore, it unlocks larger profitability for prosumers compared with the use the 5kW fixed export limit adopted in Australia.
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Plominsky, A. M., Henriquez-Castillo, C., Delherbe, N., Podell, S., Ramirez-Flandes, S., Ugalde, J. A., et al. (2018). Distinctive Archaeal Composition of an Artisanal Crystallizer Pond and Functional Insights Into Salt-Saturated Hypersaline Environment Adaptation. Front. Microbiol., 9, 13 pp.
Abstract: Hypersaline environments represent some of the most challenging settings for life on Earth. Extremely halophilic microorganisms have been selected to colonize and thrive in these extreme environments by virtue of a broad spectrum of adaptations to counter high salinity and osmotic stress. Although there is substantial data on microbial taxonomic diversity in these challenging ecosystems and their primary osmoadaptation mechanisms, less is known about how hypersaline environments shape the genomes of microbial inhabitants at the functional level. In this study, we analyzed the microbial communities in five ponds along the discontinuous salinity gradient from brackish to salt-saturated environments and sequenced the metagenome of the salt (halite) precipitation pond in the artisanal Cahuil Solar Saltern system. We combined field measurements with spectrophotometric pigment analysis and flow cytometry to characterize the microbial ecology of the pond ecosystems, including primary producers and applied metagenomic sequencing for analysis of archaeal and bacterial taxonomic diversity of the salt crystallizer harvest pond. Comparative metagenomic analysis of the Cahuil salt crystallizer pond against microbial communities from other salt-saturated aquatic environments revealed a dominance of the archaeal genus Halorubrum and showed an unexpectedly low abundance of Haloquadratum in the Cahuil system. Functional comparison of 26 hypersaline microbial metagenomes revealed a high proportion of sequences associated with nucleotide excision repair, helicases, replication and restriction-methylation systems in all of them. Moreover, we found distinctive functional signatures between the microbial communities from salt-saturated (>30% [w/v] total salinity) compared to sub-saturated hypersaline environments mainly due to a higher representation of sequences related to replication, recombination and DNA repair in the former. The current study expands our understanding of the diversity and distribution of halophilic microbial populations inhabiting salt-saturated habitats and the functional attributes that sustain them.
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Puig-Castellvi, F., Midoux, C., Guenne, A., Conteau, D., Franchi, O., Bureau, C., et al. (2022). A longitudinal study of the effect of temperature modification in full-scale anaerobic digesters – dataset combining 16S rDNA gene sequencing, metagenomics, and metabolomics data. Data Br., 41, 107960.
Abstract: Data in this article provides detailed information on the microbial dynamics and degradation performances in two fullscale anaerobic digesters operated in parallel for 476 days. One of them was kept at 35 degrees C for the whole experiment, while the other was submitted to sub-mesophilic (25 degrees C) conditions between days 123 and 373. Sludge samples were collected from both digesters at days 0, 80, 177, 218, 281, 353, and 462. The provided data include the operational conditions of the digesters and the characterization of the sludge samples at the physicochemical level, indicative of the digesters' degradation performance. It also includes the characterization of the sludge samples at the multiomics level (16S rRNA gene sequencing, metagenomics, and metabolomics profiling), to decipher the changes in the microbial structure and molecular activity. The 16S rDNA gene sequencing, metagenomics, and metabolomics data were generated using an IonTorrent PGM sequencer, an Illumina NextSeq 500 sequencer, and LTQ-Orbitrap XL mass spectrometer respectively. The 16S rDNA gene raw data and the metagenomics data have been deposited in the BioProject PRJEB49115, in the ENA database (https://www.ebi.ac.uk/ena/browser/view/PRJEB49115). The metabolomics data has been deposited at the Metabolomics Workbench, with study id ST002004 (DOI: 10.21228/M8JM6B). The data can be used as a source for comparisons with other studies working with data from full-scale anaerobic digesters, especially for those investigating the effect of the temperature modification. The data is associated with the research article “Metataxonomics, metagenomics, and metabolomics analysis of the influence of temperature modification in full-scale anaerobic digesters” (Puig-Castellvi et al [1]). (c) 2022 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)
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Puig-Castellvi, F., Midoux, C., Guenne, A., Conteau, D., Franchi, O., Bureau, C., et al. (2022). Metataxonomics, metagenomics and metabolomics analysis of the influence of temperature modification in full-scale anaerobic digesters. Bioresour. Technol., 346, 126612.
Abstract: Full-scale anaerobic digesters' performance is regulated by modifying their operational conditions, but little is known about how these modifications affect their microbiome. In this work, we monitored two originally mesophilic (35 degrees C) full-scale anaerobic digesters during 476 days. One digester was submitted to sub-mesophilic (25 degrees C) conditions between days 123 and 373. We characterized the effect of temperature modification using a multi-omics (metataxonomics, metagenomics, and metabolomics) approach. The metataxonomics and metagenomics results revealed that the lower temperature allowed a substantial increase of the sub-dominant bacterial population, destabilizing the microbial community equilibrium and reducing the biogas production. After restoring the initial mesophilic temperature, the bacterial community manifested resilience in terms of microbial structure and functional activity. The metabolomic signature of the sub-mesophilic acclimation was characterized by a rise of amino acids and short peptides, suggesting a protein degradation activity not directed towards biogas production.
Keywords: Metagenomics; Sub-mesophilic; Metabolomics; Full-scale; Chemometrics
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Ramirez-Flandes, S., Gonzalez, B., & Ulloa, O. (2019). Redox traits characterize the organization of global microbial communities. Proc. Natl. Acad. Sci. U. S. A., 116(9), 3630–3635.
Abstract: The structure of biological communities is conventionally described as profiles of taxonomic units, whose ecological functions are assumed to be known or, at least, predictable. In environmental microbiology, however, the functions of a majority of microorganisms are unknown and expected to be highly dynamic and collectively redundant, obscuring the link between taxonomic structure and ecosystem functioning. Although genetic trait-based approaches at the community level might overcome this problem, no obvious choice of gene categories can be identified as appropriate descriptive units in a general ecological context. We used 247 microbial metagenomes from 18 biomes to determine which set of genes better characterizes the differences among biomes on the global scale. We show that profiles of oxidoreductase genes support the highest biome differentiation compared with profiles of other categories of enzymes, general protein-coding genes, transporter genes, and taxonomic gene markers. Based on oxidoreductases' description of microbial communities, the role of energetics in differentiation and particular ecosystem function of different biomes become readily apparent. We also show that taxonomic diversity is decoupled from functional diversity, e. g., grasslands and rhizospheres were the most diverse biomes in oxidoreductases but not in taxonomy. Considering that microbes underpin biogeochemical processes and nutrient recycling through oxidoreductases, this functional diversity should be relevant for a better understanding of the stability and conservation of biomes. Consequently, this approach might help to quantify the impact of environmental stressors on microbial ecosystems in the context of the global-scale biome crisis that our planet currently faces.
Keywords: microbial ecology; functional traits; oxidoreductases; biomes; metagenomics
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Ramirez-Pico, C., Ljubic, I., & Moreno, E. (2023). Benders Adaptive-Cuts Method for Two-Stage Stochastic Programs. Transp. Sci., Early Access.
Abstract: Benders decomposition is one of the most applied methods to solve two-stage stochastic problems (TSSP) with a large number of scenarios. The main idea behind the Benders decomposition is to solve a large problem by replacing the values of the second stage subproblems with individual variables and progressively forcing those variables to reach the optimal value of the subproblems, dynamically inserting additional valid constraints, known as Benders cuts. Most traditional implementations add a cut for each scenario (multicut) or a single cut that includes all scenarios. In this paper, we present a novel Benders adaptive-cuts method, where the Benders cuts are aggregated according to a partition of the scenarios, which is dynamically refined using the linear program-dual information of the subproblems. This scenario aggregation/disaggregation is based on the Generalized Adaptive Partitioning Method (GAPM), which has been successfully applied to TSSPs. We formalize this hybridization of Benders decomposition and the GAPM by providing sufficient conditions under which an optimal solution of the deterministic equivalent can be obtained in a finite number of iterations. Our new method can be interpreted as a compromise between the Benders single-cuts and multicuts methods, drawing on the advantages of both sides, by rendering the initial iterations faster (as for the single-cuts Benders) and ensuring the overall faster convergence (as for the multicuts Benders). Computational experiments on three TSSPs [the Stochastic Electricity Planning, Stochastic Multi Commodity Flow, and conditional value-at-risk (CVaR) Facility Location] validate these statements, showing that the new method outperforms the other implementations of Benders methods, as well as other standard methods for solving TSSPs, in particular when the number of scenarios is very large. Moreover, our study demonstrates that the method is not only effective for the risk-neutral decision makers, but also that it can be used in combination with the risk-averse CVaR objective.
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Saavedra, L. M., Saldias, G. S., Broitman, B. R., & Vargas, C. A. (2021). Carbonate chemistry dynamics in shellfish farming areas along the Chilean coast: natural ranges and biological implications. ICES J. Mar. Sci., 78(1), 323–339.
Abstract: The increasing shellfish aquaculture requires knowledge about nearshore environmental variability to manage sustainably and create climate change adaptation strategies. We used data from mooring time series and in situ sampling to characterize oceanographic and carbonate system variability in three bivalve aquaculture areas located along a latitudinal gradient off the Humboldt Current System. Our results showed pH(T) <8 in most coastal sites and occasionally below 7.5 during austral spring-summer in the lower (-30 degrees S) and central (-37 degrees S) latitudes, related to upwelling. Farmed mussels were exposed to undersaturated (Omega(arag) < 1) and hypoxic (<2 ml l(-1)) waters during warm seasons at -37 degrees S, while in the higher latitude (43 degrees S) undersaturated waters were only detected during colder seasons, associated with freshwater runoff. We suggest that both Argopecten purpuratus farmed at -30 degrees S and Mytilus chilensis farmed at -43 degrees S may enhance their growth during summer due to higher temperatures, lower pCO(2), and oversaturated waters. In contrast, Mytilus galloprovincialis farmed at 37 degrees S grows better during spring-summer, following higher temperatures and high pCO(2). This knowledge is relevant for aquaculture, but it must be improved using high-resolution time series and in situ experimentation with farmed species to aid their adaptation to climate change and ocean acidification.
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Yushimito, W. F., Ban, X. G., & Holguin-Veras, J. (2014). A Two-Stage Optimization Model for Staggered Work Hours. J. Intell. Transport. Syst., 18(4), 410–425.
Abstract: Traditional or standard work schedules refer to the requirement that workers must be at work the same days and during the same hours each day. This requirement constrains work-related trip arrivals, and generates morning and afternoon peak hours due to the concentration of work days and/or work hours. Alternative work schedules seek to reschedule work activities away from this traditional requirement. The aim is to flatten the peak hours by spreading the demand (i.e., assigning it to the shoulders of the peak hour), lowering the peak demand. This not only would reduce societal costs but also can help to minimize the physical requirements. In this article, a two-stage optimization model is presented to quantify the effects of staggered work hours under incentive policies. In the first stage, a variation of the generalized quadratic assignment problem is used to represent the firm's assignment of workers to different work starting times. This is the input of a nonlinear complementarity problem that captures the behavior of the users of the transportation network who are seeking to overcome the constraints imposed by working schedules (arrival times). Two examples are provided to show how the model can be used to (a) quantify the effects and response of the firm to external incentives and (b) evaluate what type of arrangements in starting times are to be made in order to achieve a social optimum.
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