The fermionic Tonks-Girardeau gas: composite boson formation and a novel formulation of the ground state wave function

Màster Oficial de Ciència i Tecnologia Quàntiques / Quantum Science and Technology, Facultat de Física, Universitat de Barcelona. Curs: 2023-2024. Tutors: Bruno Juliá-Díaz, Grigori E. AstrakharchikAttractive p-wave one-dimensional fermions are studied in the fermionic Tonks-Girardeau regime, in which the diagonal properties are shared with those of an ideal Bose gas. We study the off-diagonal properties and present analytical expressions for the eigenvalues of the one-body density matrix. We show that the occupation of natural orbitals occurs in pairs, indicating the formation of composite bosons, each consisting of two attractive fermions. The formation of composite bosons sheds light on the pairing mechanism of the system orbitals, yielding a total density equal to that of an ideal Bose gas. Furthermore, we introduce an alternative expression for the ground state wave function of the fermionic Tonks-Girardeau gas. Our wave function is constructed based on the occupation numbers and natural orbitals of the one-body density matrix. We demonstrate that the newly found wave function describes the ground state of the fermionic Tonks-Girardeau gas under any external potential. By expressing the proposed wave function in the framework of second quantization, we show that the ground state of the fermionic Tonks-Girardeau gas is a number-conserving Bardeen-Cooper-Schrieffer (BCS) state. We provide explicit expressions for the corresponding coefficients that describe the fermionic Tonks-Girardeau gas as a number-conserving BCS state. Additionally, the suitable form of the proposed wave function in second quantization allows us to derive the necessary expectation values to experimentally detect pairing in the fermionic Tonks-Girardeau gas. With this, we prove and show how to detect that the fermionic Tonks-Girardeau gas not only exhibits non-trivial quantum correlations, but is also a paired state

Protocol: Triple Diamond method for problem solving and design thinking. Rubric validation

[EN] There is a set of tools that we can use to improve the results of each of the phases that continuous improvement projects must go through (8D, PDCA, DMAIC, Double diamond, etc.). These methods use divergent techniques, which help generate multiple alternatives, and convergent techniques that help analyze and filter the generated options. However, the tools used in all those frameworks are often very similar. Our goal, in this research, is to develop a comprehensive model that allows it to be used both for problem-solving and for taking advantage of opportunities. This protocol defines the main terms related to our research, makes a framework proposal, proposes a rubric that identifies observable milestones at each stage of the model and proposes the action plan to validate this rubric and the model in a given context. The action plan will be implemented in a future research.Marin-Garcia, JA.; Garcia-Sabater, JJ.; Garcia-Sabater, JP.; Maheut, J. (2020). Protocol: Triple Diamond method for problem solving and design thinking. Rubric validation. WPOM-Working Papers on Operations Management. 11(2):49-68. https://doi.org/10.4995/wpom.v11i2.14776OJS496811

Coproducción: Una revisión de la literatura

[ES] El objetivo del presente artículo es analizar la literatura existente en el entorno de la coproducción. De acuerdo con Deuermeyer y Pierskalla (1978), es posible afirmar que existe coproducción cuando un proceso productivo da como resultado más de un producto de manera simultánea. La coproducción aparece en ambientes de alta y baja tecnología de producción. La coproducción, suele ocurrir en entornos de producción en los que algunos procesos no se conocen/comprenden perfectamente y/o no están totalmente bajo control (coproducción incontrolada). Sin embargo, tal y como, se ha podido constatar en la realidad industrial, en ocasiones el proceso de coproducción, si se conoce/comprende perfectamente (coproducción controlada). La coproducción puede ser un fenómeno intrínseco al propio proceso productivo (coproducción no deliberada). Aunque en ocasiones puede ser escogida por el gestor del proceso (coproducción deliberada). Así, resulta interesante clasificar la literatura respecto a estas variables, pues hasta la fecha no se había realizado, proporcionando al lector una visión clara de la literatura existente en torno a la coproducción.Este trabajo ha sido realizado gracias a la financiación del Ministerio de Ciencia e Innovación a través del proyecto CORSARI MAGIC: Coordinación de operaciones en redes de suministro/demanda ajustadas, resilientes a la incertidumbre: modelos y algoritmos para la gestión de la incertidumbre y la complejidad, DPI: 2010-18243.Vidal Carreras, PI. (2011). Coproducción: Una revisión de la literatura. Working Papers on Operations Management. 2(1):11-17. doi:10.4995/wpom.v2i1.810SWORD111721BITRAN, G. B., & LEONG, T.-Y. (1995). Co-production of substitutable products. Production Planning & Control, 6(1), 13-25. doi:10.1080/09537289508930249Bitran, G. R., & Dasu, S. (1992). Ordering Policies in an environment of Stochastic Yields and Substitutable Demands. Operations Research, 40(5), 999-1017. doi:10.1287/opre.40.5.999Bitran, G. R., & Gilbert, S. M. (1994). Co-Production Processes with Random Yields in the Semiconductor Industry. Operations Research, 42(3), 476-491. doi:10.1287/opre.42.3.476Bitran, G. R., & Leong, T.-Y. (1992). Deterministic Approximations to Co-Production Problems with Service Constraints and Random Yields. Management Science, 38(5), 724-742. doi:10.1287/mnsc.38.5.724Bitran, G. R., & Yanasse, H. H. (1984). Deterministic Approximations to Stochastic Production Problems. Operations Research, 32(5), 999-1018. doi:10.1287/opre.32.5.999Bravo, D., Rodríguez, E., & Medina, M. (2009). Nisin and lacticin 481 coproduction by Lactococcus lactis strains isolated from raw ewes’ milk. Journal of Dairy Science, 92(10), 4805-4811. doi:10.3168/jds.2009-2237Deuermeyer, B. L. (1979). A Multi-Type Production System for Perishable Inventories. Operations Research, 27(5), 935-943. doi:10.1287/opre.27.5.935Deuermeyer, B. L., & Pierskalla, W. P. (1978). A By-Product Production System with an Alternative. Management Science, 24(13), 1373-1383. doi:10.1287/mnsc.24.13.1373DUENYAS, I., & TSAI, C.-Y. (2000). Control of a manufacturing system with random product yield and downward substitutability. IIE Transactions, 32(9), 785-795. doi:10.1080/07408170008967438Evans, R. V. (1969). Inventory control of by-products. Naval Research Logistics Quarterly, 16(1), 85-92. doi:10.1002/nav.3800160107Garcia-Sabater, J. P.; Vidal-Carreras, P. I. (2010). Programación de producción en los proveedores del automóvil. Revista Virtual Pro, Vol. 104, p. 23.García-Sabater, J. P., Vidal-Carreras, P.I., & García-Sabater, J. J. (2005). Estudio de la Problemática de Programación de la Producción en el sector del Automóvil. Aplicación a una red de fabricación, in VIII Congreso de Ingeniería de Organización.Gerchak, Y., & Grosfeld-Nir, A. (1999). International Journal of Flexible Manufacturing Systems, 11(4), 371-377. doi:10.1023/a:1008131213614GERCHAK, Y., TRIPATHY, A., & WANG, K. (1996). Co-production models with random functionality yields. IIE Transactions, 28(5), 391-403. doi:10.1080/07408179608966286Grosfeld-Nir, A., & Gerchak, Y. (2004). Multiple Lotsizing in Production to Order with Random Yields: Review of Recent Advances. Annals of Operations Research, 126(1-4), 43-69. doi:10.1023/b:anor.0000012275.01260.f5LISBONA, P., & ROMEO, L. (2008). Enhanced coal gasification heated by unmixed combustion integrated with an hybrid system of SOFC/GT. International Journal of Hydrogen Energy, 33(20), 5755-5764. doi:10.1016/j.ijhydene.2008.06.031Mcgillivray, R., & Silver, E. (1978). Some Concepts For Inventory Control Under Substitutable Demand*. INFOR: Information Systems and Operational Research, 16(1), 47-63. doi:10.1080/03155986.1978.11731687Nahmias, S., & Moinzadeh, K. (1997). Lot Sizing with Randomly Graded Yields. Operations Research, 45(6), 974-989. doi:10.1287/opre.45.6.974Nielsen, D. R., Yoon, S.-H., Yuan, C. J., & Prather, K. L. J. (2010). Metabolic engineering of acetoin and meso-2, 3-butanediol biosynthesis in E. coli. Biotechnology Journal, 5(3), 274-284. doi:10.1002/biot.200900279Öner, S., & Bilgiç, T. (2008). Economic lot scheduling with uncontrolled co-production. European Journal of Operational Research, 188(3), 793-810. doi:10.1016/j.ejor.2007.05.016Ou, J., & Wein, L. M. (1995). Dynamic Scheduling of a Production/Inventory System with By-Products and Random Yield. Management Science, 41(6), 1000-1017. doi:10.1287/mnsc.41.6.1000Caner Taşkın, Z., & Tamer Ünal, A. (2009). Tactical level planning in float glass manufacturing with co-production, random yields and substitutable products. European Journal of Operational Research, 199(1), 252-261. doi:10.1016/j.ejor.2008.11.024Tomlin, B., & Wang, Y. (2008). Pricing and Operational Recourse in Coproduction Systems. Management Science, 54(3), 522-537. doi:10.1287/mnsc.1070.0807Vidal-Carreras, P. I., & Garcia-Sabater, J. P. (2009). Comparison of heuristics for an economic lot scheduling problem with deliberated coproduction. Journal of Industrial Engineering and Management, 2(3). doi:10.3926/jiem.2009.v2n3.p437-463Yano, C. A., & Lee, H. L. (1995). Lot Sizing with Random Yields: A Review. Operations Research, 43(2), 311-334. doi:10.1287/opre.43.2.31

Solving the multisite staff planning and scheduling problem in a sheltered employment centre that employs workers with intellectual disabilities by MILP: a Spanish case study

[EN] In sheltered employment centres, staff planning and scheduling activities are critical for operations managers. A generic framework is not easy to set up not only because legal issues are diverse and differ among these service organization types, but one worker may not fit in anywhere at any time. This complexity is greater when workers with specific needs perform work activities in many labour enclaves and different sectors. In this paper, a mixed-integer linear model to solve workers¿ shift assignments to other workplaces and various activities to form teams is proposed. The novelty of the proposed model lies in considering specific features, such as the skills matrix and the affinity matrix, between the different actors in a labour enclave. The model is validated using real instances from a case study, and several objectives are tested and discussed. The decision support system that sustains the model is introduced and managerial issues are discussed.Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature.Maheut, J.; García Sabater, JP.; García Sabater, JJ.; Garcia-Manglano, S. (2023). Solving the multisite staff planning and scheduling problem in a sheltered employment centre that employs workers with intellectual disabilities by MILP: a Spanish case study. Central European Journal of Operations Research. https://doi.org/10.1007/s10100-023-00864-

A data generator for covid-19 patients’ care requirements inside hospitals

[EN] A Spanish version of the article is provided (see section before references). This paper presents the generation of a plausible data set related to the needs of COVID-19 patients with severe or critical symptoms. Possible illness’ stages were proposed within the context of medical knowledge as of January 2021. The parameters chosen in this data set were customized to fit the population data of the Valencia region (Spain) with approximately 2.5 million inhabitants. They were based on the evolution of the pandemic between September 2020 and March 2021, a period that included two complete waves of the pandemic. Contrary to expectation and despite the European and national transparency laws (BOE-A2013-12887, 2013; European Parliament and Council of the European Union, 2019), the actual COVID-19 pandemic-related data, at least in Spain, took considerable time to be updated and made available (usually a week or more). Moreover, some relevant data necessary to develop and validate hospital bed management models were not publicly accessible. This was either because these data were not collected, because public agencies failed to make them public (despite having them indexed in their databases), the data were processed within indicators and not shown as raw data, or they simply published the data in a format that was difficult to process (e.g., PDF image documents versus CSV tables). Despite the potential of hospital information systems, there were still data that were not adequately captured within these systems. Moreover, the data collected in a hospital depends on the strategies and practices specific to that hospital or health system. This limits the generalization of "real" data, and it encourages working with "realistic" or plausible data that are clean of interactions with local variables or decisions (Gunal, 2012; Marin-Garcia et al., 2020). Besides, one can parameterize the model and define the data structure that would be necessary to run the model without delaying till the real data become available. Conversely, plausible data sets can be generated from publicly available information and, later, when real data become available, the accuracy of the model can be evaluated (Garcia-Sabater and Maheut, 2021). This work opens lines of future research, both theoretical and practical. From a theoretical point of view, it would be interesting to develop machine learning tools that, by analyzing specific data samples in real hospitals, can identify the parameters necessary for the automatic prototyping of generators adapted to each hospital. Regarding the lines of research applied, it is evident that the formalism proposed for the generation of sound patients is not limited to patients affected by SARS-CoV-2 infection. The generation of heterogeneous patients can represent the needs of a specific population and serve as a basis for studying complex health service delivery systems.[ES] En este trabajo se presenta cómo se ha generado un conjunto de datos verosímiles relacionados con las necesidades de pacientes covid-19 con síntomas severe or critical. Se considerarán las etapas posibles con los conocimientos médicos a fecha de enero de 2021. Los parámetros elegidos en este data set están personalizados para adecuarse a los valores poblacionales de la región de Valencia (Spain), unos 2.5 Millones de habitantes y la evolución de la pandemia entre los meses de septiembre 2020 y marzo 2021, un periodo de tiempo que contemple dos olas completas de pandemia.En contra de lo que cabría esperar, a pesar de la ley de transparencia europea y nacional (BOE-A-2013-12887, 2013; Parlamento Europeo y del Consejo de la Unión Europea, 2019), los datos reales relacionados con la pandemia covid-19, al menos en España, tardan mucho en actualizarse y estar disponibles (normalmente una semana o más días). Además, algunos datos relevantes para trabajar los modelos de gestión de camas de hospital no están accesibles públicamente. Bien porque no se hayan recogido esos datos, o porque los organismos públicos no los ofrecen (a pesar de tenerlos indexados en sus bases de datos), o los ofrecen camuflados en indicadores procesados y no muestran los datos en bruto, o simplemente los publican en un formato de difícil reutilización (por ejemplo, en documentos PDF en lugar de en tablas CSV). A pesar de que los sistemas de información de los hospitales son bastante potentes, siguen existiendo datos que ni siquiera están recogidos adecuadamente en el sistema de información de salud.Por otra parte, los datos recogidos en un hospital dependen de las estrategias y practicas propias de ese hospital o sistema de salud. Este efecto limita la generalización de los datos “reales” y es necesario trabajar con datos “realistas” o verosímiles que están limpios de interacciones con variables o decisiones locales (Gunal, 2012; Marin-Garcia et al., 2020). Por un lado, se puede parametrizar el modelo y definir la estructura de datos que sería necesaria para ejecutar el modelo con datos reales. Por otro lado, se pueden generar conjuntos de datos verosímiles a partir de la información pública disponible y, posteriormente, cuando se disponga de los datos reales evaluar la bondad del modelo (Garcia-Sabater & Maheut, 2021).Marin-Garcia, JA.; Ruiz, A.; Julien, M.; Garcia-Sabater, JP. (2021). A data generator for covid-19 patients’ care requirements inside hospitals. WPOM-Working Papers on Operations Management. 12(1):76-115. https://doi.org/10.4995/wpom.1533276115121Alexander, G. L. (2007). The nurse-patient trajectory framework. Medinfo. MEDINFO, 12(Pt 2), 910- 914.Belciug, S., Bejinariu, S. I., & Costin, H. (2020). An artificial immune system approach for a multicompartment queuing model for improving medical resources and inpatient bed occupancy in pandemics. 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Estructura propuesta del artículo de datos como publicación científica. Revista Espanola de Documentacion Cientifica, 40(1), 1-12. https://doi.org/10.3989/redc.2017.1.1375Romeo Casabona, C. M., & Urruela Mora, A. (2020). Informe Del Ministerio De Sanidad Sobre Los Aspectos Éticos En Situaciones De Pandemia: El Sars-Cov-2. 12.RStudio Team. (2020). RStudio: Integrated Development for R. RStudio, PBC. Rubio-Rivas, M., Corbella, X., Mora-Luján, J. M., Loureiro-Amigo, J., López Sampalo, A., Yera Bergua, C., Esteve Atiénzar, P. J., Díez García, L. F., Gonzalez Ferrer, R., Plaza Canteli, S., Pérez Piñeiro, A., Cortés Rodríguez, B., Jorquer Vidal, L., Pérez Catalán, I., León Téllez, M., Martín Oterino, J. Á., Martín González, M. C., Serrano Carrillo de Albornoz, J. L., García Sardon, E., … GómezHuelgas, R. (2020). Predicting Clinical Outcome with Phenotypic Clusters in COVID-19 Pneumonia: An Analysis of 12,066 Hospitalized Patients from the Spanish Registry SEMI-COVID19. Journal of Clinical Medicine, 9(11), 3488. https://doi.org/10.3390/jcm9113488Ruckdeschel, P., Kohl, M., Stabla, T., & Camphausen, F. (2006). S4 Classes for Distributions. R News, 6(2), 2-6. Ruza, F. (2008). Cuidados Intensivos Pediatricos. 6(6), 336. Schauberger, P., & Walker, A. (2020). openxlsx: Read, Write and Edit xlsx Files.Stang, A., Stang, M., & Jöckel, K. H. (2020). Estimated use of intensive care beds due to COVID-19 in Germany over time. Deutsches Arzteblatt International, 117(19). https://doi.org/10.3238/arztebl.2020.0329Unroe, M., Kahn, J. M., Carson, S. S., Govert, J. A., Martinu, T., Sathy, S. J., Clay, A. S., Chia, J., Gray, A., Tulsky, J. A., & Cox, C. E. (2010). One-year trajectories of care and resource utilization for recipients of prolonged mechanical ventilation: A cohort study. Annals of Internal Medicine, 153(3), 167-175. https://doi.org/10.7326/0003-4819-153-3-201008030-00007Venables, W. N., & Ripley, B. D. (2002). Modern Applied Statistics with S (Fourth). Springer. https://doi.org/10.1007/978-0-387-21706-2Wang, Y., Wang, Y., Chen, Y., & Qin, Q. (2020). Unique epidemiological and clinical features of the emerging 2019 novel coronavirus pneumonia (COVID-19) implicate special control measures. In Journal of Medical Virology (Vol. 92, Issue 6, pp. 568-576). John Wiley and Sons Inc. https://doi.org/10.1002/jmv.25748Wickham, H. (2007). Reshaping Data with the {reshape} Package. Journal of Statistical Software, 21(12), 1-20. https://doi.org/10.18637/jss.v021.i12Wickham, H. (2011). The Split-Apply-Combine Strategy for Data Analysis. Journal of Statistical Software, 40(1), 1-29. https://doi.org/10.18637/jss.v040.i01Wiersema, U. F. (2007). Noninvasive Respiratory Support. In Cardiothoracic Critical Care (pp. 410- 418). Elsevier Inc. https://doi.org/10.1016/B978-075067572-7.50031-XWinck, J. C., & Scala, R. (2021). Non-invasive respiratory support paths in hospitalized patients with COVID-19: proposal of an algorithm. Pulmonology. https://doi.org/10.1016/j.pulmoe.2020.12.005Wong, G. N., Weiner, Z. J., Tkachenko, A. V., Elbanna, A., Maslov, S., & Goldenfeld, N. (2020). Modeling COVID-19 dynamics in Illinois under non-pharmaceutical interventions. In medRxiv. https://doi.org/10.1101/2020.06.03.20120691Wu, H., Godfrey, A. J. R., Govindaraju, K., & Pirikahu, S. (2020). ExtDist: Extending the Range of Functions for Probability Distributions.Xia, W., & Sun, J. (2013). Simulation guided value stream mapping and lean improvement: A case study of a tubular machining facility. Journal of Industrial Engineering and Management, 6(2), 456-476. https://doi.org/10.3926/jiem.532Xu, X. W., Wu, X. X., Jiang, X. G., Xu, K. J., Ying, L. J., Ma, C. L., Li, S. B., Wang, H. Y., Zhang, S., Gao, H. N., Sheng, J. F., Cai, H. L., Qiu, Y. Q., & Li, L. J. (2020). Clinical findings in a group of patients infected with the 2019 novel coronavirus (SARS-Cov-2) outside of Wuhan, China: Retrospective case series. The BMJ, 368. https://doi.org/10.1136/bmj.m606Zheng, Z., Peng, F., Xu, B., Zhao, J., Liu, H., Peng, J., Li, Q., Jiang, C., Zhou, Y., Liu, S., Ye, C., Zhang, P., Xing, Y., Guo, H., & Tang, W. (2020). Risk factors of critical & mortal COVID-19 cases: A systematic literature review and meta-analysis. In Journal of Infection (Vol. 81, Issue 2, pp. e16- e25). W.B. Saunders Ltd. https://doi.org/10.1016/j.jinf.2020.04.02

Operations Management at the service of health care management: Example of a proposal for action research to plan and schedule health resources in scenarios derived from the COVID-19 outbreak

[EN] With this work, we intend to promote research on the application of Operations Management tools in order to assist with decision-making in health crisis situations. During the first six weeks of the COVID-19 crisis in Spain, we have contacted a large number of hospital and health department managers in the Valencian Community and other regions of Spain. The result is that very few, at least when contact was made and at the time of writing this article, had consulted staff members in the Operations Management area for advice on this situation, and they are quite reluctant to do so. This is in spite of the fact that some medical sources also consider this crisis to be one of resources, not merely a medical crisis. Our opinion is that Operations Management can make a useful and valuable contribution to anticipate and improve the management of scarce resources, even in times of crisis. If those responsible for public health or heads of hospitals do not see this usefulness, then there is a huge gap between research and practice in Operations Management and what is transmitted to the healthcare sector. Our aim is to help reduce this gap.Marin-Garcia, JA.; García Sabater, JP.; Ruiz, A.; Maheut, J.; García Sabater, JJ. (2020). Operations Management at the service of health care management: Example of a proposal for action research to plan and schedule health resources in scenarios derived from the COVID-19 outbreak. Journal of Industrial Engineering and Management. 13(2):213-227. https://doi.org/10.3926/jiem.3190S21322713

Revisión de la literatura sobre la flexibilidad de decisión operacional

[ES] En este artículo se presenta una revisión de la literatura sobre la flexibilidad en la toma de decisiones operacionales en el contexto de planificación y gestión en las cadenas de suministro. Esta revisión reflexiona sobre algunas de las definiciones propuestas en la literatura sobre la flexibilidad en la planificación estratégica. Se propone una caracterización de los diferentes tipos de flexibilidad presentes en la literatura en función de las diferentes tareas de planificación existentes y de las diferentes consideraciones en el uso de los materiales.El presente trabajo se ha desarrollado gracias a la ayuda DPI2010-18243 del Ministerio de Ciencia e Innovación del Gobierno de España dentro del programa de Proyectos de Investigación Fundamental no orientada, con el título "COORDINACION DE OPERACIONES EN REDES DE SUMINISTRO/DEMANDA AJUSTADAS, RESILIENTES A LA INCERTIDUMBRE: MODELOS Y ALGORITMOS PARA LA GESTION DE LA INCERTIDUMBRE Y LA COMPLEJIDAD". Asimismo, esta investigación también ha sido financiada mediante una beca doctoral concedida por la Generalitat Valenciana de España a Julien Maheut (Ref. ACIF/2010).Maheut, J. (2011). Revisión de la literatura sobre la flexibilidad de decisión operacional. Working Papers on Operations Management. 2(1):39-48. https://doi.org/10.4995/wpom.v2i1.814SWORD394821Aissaoui, N., Haouari, M., & Hassini, E. (2007). Supplier selection and order lot sizing modeling: A review. Computers & Operations Research, 34(12), 3516-3540. doi:10.1016/j.cor.2006.01.016Akkerman, R., & van Donk, D. P. (2009). Product mix variability with correlated demand in two-stage food manufacturing with intermediate storage. International Journal of Production Economics, 121(2), 313-322. doi:10.1016/j.ijpe.2006.11.021Arunapuram, S., Mathur, K., & Solow, D. (2003). Vehicle Routing and Scheduling with Full Truckloads. Transportation Science, 37(2), 170-182. doi:10.1287/trsc.37.2.170.15248Balakrishnan, A., & Geunes, J. (2000). Requirements Planning with Substitutions: Exploiting Bill-of-Materials Flexibility in Production Planning. Manufacturing & Service Operations Management, 2(2), 166-185. doi:10.1287/msom.2.2.166.12349Bilgen, B.; Günther, H. O. (2009). Integrated production and distribution planning in the fast moving consumer goods industry: a block planning application. OR Spectrum.Calderon-Lama, J. L.; Garcia-Sabater, J. P.; Lario, F. C. (2009). Modelo para la planificación de Operaciones en Cadenas de Suministro de Productos de Innovación. DYNA Ingeniería e Industria, Vol. 84, nº. 6, pp. 517-526.Caner Taşkın, Z., & Tamer Ünal, A. (2009). Tactical level planning in float glass manufacturing with co-production, random yields and substitutable products. European Journal of Operational Research, 199(1), 252-261. doi:10.1016/j.ejor.2008.11.024Carrillo, J. E., & Franza, R. M. (2006). Investing in product development and production capabilities: The crucial linkage between time-to-market and ramp-up time. European Journal of Operational Research, 171(2), 536-556. doi:10.1016/j.ejor.2004.08.040Carvalho, J., Moreira, N., & Pires, L. (2005). Autonomous Production Systems in virtual enterprises. International Journal of Computer Integrated Manufacturing, 18(5), 357-366. doi:10.1080/09511920500081445Clement, J.; Coldrick, A.; Sari, J. (1995). Manufacturing data structures: building foundations for excellence with bills of materials and process information. Wiley.Crama, Y., Pochet, Y., & Wera, R. (2001). Production planning aproaches in the process industry. UCL, Belgium.David, F., Pierreval, H., & Caux, C. (2006). Advanced planning and scheduling systems in aluminium conversion industry. International Journal of Computer Integrated Manufacturing, 19(7), 705-715. doi:10.1080/09511920500504545De Kok, T. G., & Fransoo, J. C. (2003). Planning Supply Chain Operations: Definition and Comparison of Planning Concepts. Handbooks in Operations Research and Management Science, 597-675. doi:10.1016/s0927-0507(03)11012-2Deuermeyer, B. L., & Pierskalla, W. P. (1978). A By-Product Production System with an Alternative. Management Science, 24(13), 1373-1383. doi:10.1287/mnsc.24.13.1373Escudero, L. F. (1994). CMIT, capacitated multi-level implosion tool. European Journal of Operational Research, 76(3), 511-528. doi:10.1016/0377-2217(94)90284-4Garcia-Sabater, J. P., Maheut, J., & Garcia-Sabater, J. J. (2009a). A Capacited Material Requierements Planning Model considering Delivery Constraints, in 3rd International Conference on Industrial Engineering and Industrial Management, pp. 793-803.Garcia-Sabater, J. P., Maheut, J., & Garcia-Sabater, J. J. (2009b). A Capacited Material Requierements Planning Model considering Delivery Constraints: A Case Study from the Automotive Industry, in 39th International Conference on Computers & Industrial Engineering, pp. 378-383.Geunes, J. (2003). Solving large-scale requirements planning problems with component substitution options. Computers & Industrial Engineering, 44(3), 475-491. doi:10.1016/s0360-8352(02)00232-2Gronalt, M., Hartl, R. F., & Reimann, M. (2003). New savings based algorithms for time constrained pickup and delivery of full truckloads. European Journal of Operational Research, 151(3), 520-535. doi:10.1016/s0377-2217(02)00650-1GUPTA, S. M., & TALEB, K. N. (1994). Scheduling disassembly. International Journal of Production Research, 32(8), 1857-1866. doi:10.1080/00207549408957046Hachicha, W., Masmoudi, F., & Haddar, M. (2008). A Taguchi method application for the part routing selection in generalised group technology. International Journal of Materials and Structural Integrity, 2(4), 396. doi:10.1504/ijmsi.2008.022999Hachicha, W., Masmoudi, F., & Haddar, M. (2009). Plans d’expérience et analyse des corrélations pour la résolution du problème de formation de cellules avec gammes alternatives. Mécanique & Industries, 10(5), 337-350. doi:10.1051/meca/2009068Inderfurth, K., & Langella, I. M. (2005). Heuristics for solving disassemble-to-order problems with stochastic yields. OR Spectrum, 28(1), 73-99. doi:10.1007/s00291-005-0007-2Lang, J. C., & Domschke, W. (2008). Efficient reformulations for dynamic lot-sizing problems with product substitution. OR Spectrum, 32(2), 263-291. doi:10.1007/s00291-008-0148-1Lin, J. T., Chen, T.-L., & Lin, Y.-T. (2009). Critical material planning for TFT-LCD production industry. International Journal of Production Economics, 122(2), 639-655. doi:10.1016/j.ijpe.2009.05.027Lyon, P., Milne, R. J., Orzell, R., & Rice, R. (2001). Matching Assets with Demand in Supply-Chain Management at IBM Microelectronics. Interfaces, 31(1), 108-124. doi:10.1287/inte.31.1.108.9693Matta, A., Tomasella, M., & Valente, A. (2007). Impact of ramp-up on the optimal capacity-related reconfiguration policy. 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The role of Bill of Materials and Movements (BOMM) in the virtual enterprises environment. International Journal of Production Research, 46(4), 1163-1185. doi:10.1080/00207540600943951Ram, B., Naghshineh-Pour, M. R., & Yu†, X. (2006). Material requirements planning with flexible bills-of-material. International Journal of Production Research, 44(2), 399-415. doi:10.1080/00207540500251505Sabri, E. H., & Beamon, B. M. (2000). A multi-objective approach to simultaneous strategic and operational planning in supply chain design. Omega, 28(5), 581-598. doi:10.1016/s0305-0483(99)00080-8Schütz, P., Tomasgard, A., & Ahmed, S. (2009). Supply chain design under uncertainty using sample average approximation and dual decomposition. European Journal of Operational Research, 199(2), 409-419. doi:10.1016/j.ejor.2008.11.040Segerstedt, A. (1996). A capacity-constrained multi-level inventory and production control problem. 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European Journal of Operational Research, 163(3), 575-588. doi:10.1016/j.ejor.2004.03.001Stadtler, H., & Kilger, C. (Eds.). (2002). Supply Chain Management and Advanced Planning. doi:10.1007/978-3-662-10142-1Tagaras, G. (1999). Pooling in multi-location periodic inventory distribution systems. Omega, 27(1), 39-59. doi:10.1016/s0305-0483(98)00030-9Vidal-Carreras, P. I., & Garcia-Sabater, J. P. (2009). Comparison of heuristics for an economic lot scheduling problem with deliberated coproduction. Journal of Industrial Engineering and Management, 2(3). doi:10.3926/jiem.2009.v2n3.p437-463Weidema, B. P. (1999). System expansions to handle co-products of renewable materials, pp. 45-48.Wilson, S., & Platts, K. (2010). How do companies achieve mix flexibility? International Journal of Operations & Production Management, 30(9), 978-1003. doi:10.1108/0144357101107507

Introducción a la Mejora Continua

Una breve introducción a los conceptos básicos de la innovación continua también conocida como mejora continuaGarcía Sabater, JP.; Garcia Sabater. Julio Juan (2020). Introducción a la Mejora Continua. http://hdl.handle.net/10251/15589

Applying Value Stream Mapping to Improve the Delivery of Patient Care in the Oncology Day Hospital

[EN] Improving the delivery of patient care is an ongoing challenge in the National Health Service (NHS). This challenge is not insignificant in the process of chemotherapy administration for oncology patients. The present research is motivated by a public Spanish hospital in which oncology patients receive medical care in the Oncology Day Hospital (ODH). At the ODH, oncology patients receive different health services by different specialists on a single day. Any discoordination in patient flow will contribute to longer waiting times and stays in the ODH. As oncology patients tend to have special health conditions, any extra time in the hospital is a source of risk and discomfort. This study applies value stream mapping methodology in a Spanish ODH to improve this situation, reducing hospital waiting times and shorting the length of stay. For that purpose, the path of the oncology patients is mapped and the current state of the system is analyzed. Working at takt time and levelling the workload are proposed for improving the working conditions for healthcare personnel. As a result, the quality of service for oncology patients who need a well-defined care profile is improved. The singular characteristics of the Spanish NHS make it challenging to implement new ways of working, so this study has significant theoretical and managerial implications offering directions in which improvement is possible.Vidal-Carreras, PI.; García Sabater, JJ.; Marin-Garcia, JA. (2022). Applying Value Stream Mapping to Improve the Delivery of Patient Care in the Oncology Day Hospital. International Journal of Environmental research and Public Health. 19(7):1-18. https://doi.org/10.3390/ijerph1907426511819

The Role of Value Stream Mapping in Healthcare Services: A Scoping Review

[EN] Lean healthcare aims to manage and improve the processes in the healthcare sector by eliminating everything that adds no value by improving quality of services, ensuring patient safety and facilitating health professionals' work to achieve a flexible and reliable organization. Value Stream Mapping (VSM) is considered the starting point of any lean implementation. Some papers report applications of VSM in healthcare services, but there has been less attention paid to their contribution on sustainability indicators. The purpose of this work is to analyze the role of VSM in this context. To do so, a scoping review of works from recent years (2015 to 2019) was done. The results show that most applications of VSM reported are in the tertiary level of care, and the United States of America (USA) is the country which leads most of the applications published. In relation with the development of VSM, a heterogeneity in the maps and the sustainability indicators is remarkable. Moreover, only operational and social sustainability indicators are commonly included. We can conclude that more standardization is required in the development of the VSM in the healthcare sector, also including the environmental indicators.Marin-Garcia, JA.; Vidal-Carreras, PI.; García Sabater, JJ. (2021). The Role of Value Stream Mapping in Healthcare Services: A Scoping Review. International Journal of Environmental research and Public Health (Online). 18(3):1-25. https://doi.org/10.3390/ijerph18030951S12518