Método de diseño y asignación dinámica de espacios de almacenamiento

dc.contributor.advisorAdarme Jaimes, Wilsonspa
dc.contributor.authorBallesteros Riveros, Frank Alexanderspa
dc.contributor.researchgroupSOCIEDAD, ECONOMIA Y PRODUCTIVIDAD - \'SEPRO\'spa
dc.date.accessioned2021-10-25T14:43:39Z
dc.date.available2021-10-25T14:43:39Z
dc.date.issued2021
dc.descriptionilustraciones, gráficas, tablasspa
dc.description.abstractEsta tesis propuso un método para el diseño y asignación dinámica de los espacios con el fin de reducir los tiempos de preparación de pedidos en el ámbito de la logística de almacenamiento. Para ello, caracterizó los almacenes a través de encuestas aplicadas en una zona industrial, en donde identificó oportunidades de mejora. Formuló el problema a través de un modelo matemático multi-objetivo. El análisis realizado permitió proponer un método que agrupa a las familias de productos de acuerdo a su afinidad, demanda y tiempo de vida útil, a través de una etapa inicial de pre-procesamiento. El proceso se simuló a través de Flexsim para comparar su desempeño frente a reglas de asignación tradicionales. El diseño experimental permitió analizar el impacto del tamaño de los almacenes y las reglas en los indicadores claves. Los resultados mostraron que en dos de los cuatro escenarios el método obtiene el menor tiempo de preparación de pedidos y llegó a mejorar hasta en el 30% los tiempos frente a otras reglas de asignación. Las pruebas estadísticas mostraron que ninguno de los resultados sigue una distribución normal y que las reglas de asignación sí inciden sobre el tiempo de preparación de pedidos del almacén, con lo cual se confirmó la hipótesis de investigación. La política de almacén compartido para la adopción del método obtuvo un incremento de su utilización. El aporte primordial de este proyecto es la formulación de un método eficiente que integra decisiones dinámicas del almacén. (Texto tomado de la fuente).spa
dc.description.abstractThis thesis proposed a method for the design and dynamic storage allocation to reduce order picking times in the field of warehouse logistics. For this, it characterized warehouses through surveys applied in an industrial zone, where opportunities for improvement were identified. It formulated the problem through a multi-objective mathematical model. The analysis carried out made it possible to propose a method that groups the families of products according to their affinity, demand, and useful life, through an initial pre-processing stage. It simulated the process through Flexsim to compare its performance against traditional allocation rules. The experimental design allowed it to analyze the impact of warehouse size and rules on key indicators. The results showed that in two of the four scenarios, the method obtained the shortest order picking time and improved in up to 30% of the times compared to other rules. The statistical tests showed that none of the results follow a normal distribution and that the allocation rules do affect the warehouse's order picking time, thus accepting the research hypothesis. The shared warehouse policy for the adoption of the method obtained a utilization increase. The primary contribution of this project is the formulation of an efficient method that integrates dynamic warehouse decisions.eng
dc.description.curricularareaDepartamento de Ingeniería de Sistemas e Industrialspa
dc.description.degreelevelDoctoradospa
dc.description.degreenameDoctor en Ingenieríaspa
dc.description.notesIncluye anexosspa
dc.description.researchareaMétodos y modelos de optimización y estadística en ingeniería industrial y administrativaspa
dc.format.extentxx, 178 páginasspa
dc.format.mimetypeapplication/pdfspa
dc.identifier.instnameUniversidad Nacional de Colombiaspa
dc.identifier.reponameRepositorio Institucional Universidad Nacional de Colombiaspa
dc.identifier.repourlhttps://repositorio.unal.edu.co/spa
dc.identifier.urihttps://repositorio.unal.edu.co/handle/unal/80606
dc.language.isospaspa
dc.publisherUniversidad Nacional de Colombiaspa
dc.publisher.branchUniversidad Nacional de Colombia - Sede Bogotáspa
dc.publisher.departmentDepartamento de Ingeniería de Sistemas e Industrialspa
dc.publisher.facultyFacultad de Ingenieríaspa
dc.publisher.placeBogotá, Colombiaspa
dc.publisher.programBogotá - Ingeniería - Doctorado en Ingeniería - Industria y Organizacionesspa
dc.relation.referencesAbdel-Basset, M., Manogaran, G., El-Shahat, D., & Mirjalili, S. (2018), Integrating the whale algorithm with tabu search for quadratic assignment problem: a new approach for locating hospital departments. Applied soft computing, 73, pp. 530-546.spa
dc.relation.referencesAccorsi, R., Baruffaldi, G., & Manzini, R. (2018), Picking efficiency and stock safety: A bi-objective storage assignment policy for temperature-sensitive products. Computers & Industrial Engineering, 115, pp. 240-252.spa
dc.relation.referencesAccorsi, R., Bortolini, M., Ferrari, E., Gamberi, M., & Pilati, F. (2018), Class-based storage warehouse design with diagonal cross-aisle. LogForum, 14(1), pp. 101-112.spa
dc.relation.referencesAccorsi, R., Manzini, R., & Maranesi, F. (2014), A decision-support system for the design and management of warehousing systems. Computers in Industry, 65(1), pp. 175-186.spa
dc.relation.referencesAdarme, W., Otero, M.A., Rodríguez, T.A. & Tejeda, L. (2012), Optimization of a warehouse layout used for storage of materials used in ship construction and repair. Ship Science and Technology, 5(10), pp. 59-70.spa
dc.relation.referencesAhmed, M., Seraj, R., & Islam, S. M. S. (2020), The k-means algorithm: A comprehensive survey and performance evaluation. Electronics, 9(8), 1295.spa
dc.relation.referencesAhmed Bouh, M., & Riopel, D. (2015), Material handling equipment selection: new classifications of equipments and attributes. Proceedings of the 6th International Conference on Industrial Engineering and Systems Management (IESM), 21-23 Oct. 2015, IEEE, Seville, Spain, pp. 1-8.spa
dc.relation.referencesAltarazi, S.A., & Ammouri, M.M. (2018), Concurrent manual-order-picking warehouse design: a simulation-based design of experiments approach. International Journal of Production Research, 56(23), pp. 7103-7121.spa
dc.relation.referencesAng, M., & Lim, Y.F. (2019), How to optimize storage classes in a unit-load warehouse. European Journal of Operational Research, 278(1), pp. 186-201.spa
dc.relation.referencesArdila-Gamboa, C.D. & Ballesteros-Riveros, F.A. (2018), Data Envelopment Analysis to measure relative performance based on key indicators from a supply network with Reverse Logistics. INGE CUC, 14(2), pp. 137-146.spa
dc.relation.referencesAudy, J. F., Lehoux, N., D'Amours, S., & Rönnqvist, M. (2012), A framework for an efficient implementation of logistics collaborations. International transactions in operational research, 19(5), pp. 633-657.spa
dc.relation.referencesAutry, C. W., Griffis, S. E., Goldsby, T. J., & Bobbitt, L. M. (2005), Warehouse management systems: resource commitment, capabilities, and organizational performance. Journal of Business Logistics, 26(2), pp. 165-183.spa
dc.relation.referencesAvendano, R., Melguizo, A., & Miner, S. (2017), Chinese FDI in Latin America: new trends with global implications. Washington: Atlantic Council.spa
dc.relation.referencesAylak, B. L., Noche, B., Cantepe, M. B., & Karakaya, A. (2013), Simulation Model of an Ultra-Light Overhead Conveyor System; Analysis of the Process in the Warehouse. World Academy of Science, Engineering and Technology, International Journal of Mechanical, Aerospace, Industrial, Mechatronic and Manufacturing Engineering, 7(10), pp. 1931-1935.spa
dc.relation.referencesAytaç, E. (2020), Unsupervised learning approach in defining the similarity of catchments: Hydrological response unit based k-means clustering, a demonstration on Western Black Sea Region of Turkey. International Soil and Water Conservation Research.spa
dc.relation.referencesBaker, P., Canessa, M. (2009), Warehouse design: a structured approach. European Journal of Operational Research 193, pp. 425–436.spa
dc.relation.referencesBallesteros-Riveros, F.A., Arango-Serna, M.D., Adarme-Jaimes, W. & Zapata-Cortes, J.A. (2019), Storage allocation optimization model in a Colombian company. DYNA, 86(209), pp. 255-260.spa
dc.relation.referencesBallestín, F., Pérez, Á, & Quintanilla, S. (2020), A multistage heuristic for storage and retrieval problems in a warehouse with random storage. International Transactions in Operational Research, 27(3), pp. 1699-1728.spa
dc.relation.referencesBanco Mundial (2018), Índice de Desempeño Logístico. Último acceso el 19 de octubre de 2020. Base de datos disponible en la página web oficial: http://lpi.worldbank.org/spa
dc.relation.referencesBanks, J., Carson, J.S., Nelson, B.L., & Nicol, D.M. (2013), Discrete-event system simulation: Pearson new international edition. 5th Edition, Pearson Higher Education, New Jersey, NJ.spa
dc.relation.referencesBartholdi, J.J. & Hackman, S.T. (2019), Warehouse & Distribution Science, Release 0.98.1. The Supply Chain & Logistics Institute, Atlanta, GA.spa
dc.relation.referencesBartholdi, J.J., & Gue, K. R. (2004), The best shape for a crossdock. Transportation Science, 38(2), pp. 235-244.spa
dc.relation.referencesBaruffaldi, G., Accorsi, R., & Manzini, R. (2019), Warehouse management system customization and information availability in 3pl companies: a decision-support tool. Industrial Management & Data Systems, 119(2), pp. 251-273.spa
dc.relation.referencesBattini D., Calzavara M., Persona A. & Sgarbossa, F. (2015), A comparative analysis of different paperless picking systems, Industrial Management & Data Systems, 115(3), pp. 483-503.spa
dc.relation.referencesBeckman, M. (2007), Training needs assessment for warehouse employees (Master of Science Thesis). University of Wisconsin-Stout, Menomonie, WI.spa
dc.relation.referencesBehnamian, J., & Eghtedari, B. (2009), Storage System Layout. In Facility Location (pp. 419-450). Physica-Verlag HD.spa
dc.relation.referencesBelle, J., Valckenaers, P., & Cattrysse, D. (2012), Cross-docking: State of the art. Omega, 40(6), pp. 827-846.spa
dc.relation.referencesBerry, J. R. (1968), Elements of warehouse layout. The International Journal of Production Research, 7(2), pp. 105-121.spa
dc.relation.referencesBisenieks, J., & Ozols, E. (2010), The problem of warehouse operation, its improvement and development in company's logistics system. Human Resources: The Main Factor of Regional Development, 3, pp. 206-213.spa
dc.relation.referencesBlömer, J., Lammersen, C., Schmidt, M., & Sohler, C. (2016), Theoretical analysis of the k-means algorithm–a survey. In Algorithm Engineering (pp. 81-116). Springer, Cham.spa
dc.relation.referencesBortolini, M., Faccio, M., Gamberi, M., & Manzini, R. (2015), Diagonal cross-aisles in unit load warehouses to increase handling performance. International Journal of Production Economics, 170, pp. 838-849.spa
dc.relation.referencesBortolini, M., Faccio, M., Ferrari, E., Gamberi, M., & Pilati, F. (2019), Design of diagonal cross-aisle warehouses with class-based storage assignment strategy. The International Journal of Advanced Manufacturing Technology, 100(9), pp. 2521-2536.spa
dc.relation.referencesBottani, E., Cecconi, M., Vignali, G., Montanari, R. (2012), Optimisation of storage allocation in order picking operations through a genetic algorithm, International Journal of Logistics Research and Applications: A Leading Journal of Supply Chain Management 15 (2), pp. 127-146.spa
dc.relation.referencesBrinkø, R., Nielsen, S.B. and van Meel, J. (2015), Access over ownership – a typology of shared space, Facilities, 33(11/12), pp. 736-751.spa
dc.relation.referencesCahn, A.S. (1948), The Warehouse Problem. Bulletin of the American Mathematical Society, 54(11), pp. 1073.spa
dc.relation.referencesCano, J.A. (2020), Order Picking Optimization Based on a Picker Routing Heuristic: Minimizing Total Traveled Distance in Warehouses. In Handbook of Research on the Applications of International Transportation and Logistics for World Trade (pp. 74-96). IGI Global.spa
dc.relation.referencesCano, J.A., Campo, E.A., Correa-Espinal, A.A., & Gómez-Montoya, R.A. (2021), Optimización por colonia de hormigas para el ruteo de la preparación de pedidos en almacenes de múltiples bloques. Información tecnológica, 32(3), pp.121-130.spa
dc.relation.referencesCano, J.A., Correa-Espinal, A.A., & Gómez-Montoya, R.A. (2017), An evaluation of picking routing policies to improve warehouse efficiency. International Journal of Industrial Engineering and Management, 8(4), pp. 229-238.spa
dc.relation.referencesCano, J.A., Correa-Espinal, A.A., Gómez-Montoya, R.A., & Cortés, P. (2019), Genetic algorithms for the picker routing problem in multi-block warehouses. In International Conference on Business Information Systems. Ed. Springer, Cham, pp. 313-322.spa
dc.relation.referencesCao, W., & Jiang, P. (2013), Modelling on service capability maturity and resource configuration for public warehouse product service systems. International Journal of Production Research, 51(6), pp. 1898-1921.spa
dc.relation.referencesCapó, M., Pérez, A., & Lozano, J. A. (2017), An efficient approximation to the K-means clustering for massive data. Knowledge-Based Systems, 117, pp. 56-69.spa
dc.relation.referencesCardona, L. F., Soto, D. F., Rivera, L., & Martínez, H. J. (2015), Detailed design of fishbone warehouse layouts with vertical travel. International Journal of Production Economics, 170, pp. 825-837.spa
dc.relation.referencesCardona, L.F., Rivera, L., Martínez, H.J. (2012), Analytical study of the Fishbone warehouse layout. International Journal of Logistics Research and Applications: A leading journal of Supply Chain Management 15 (6), pp. 365-388.spa
dc.relation.referencesCaserta, M., Voss, S., Sniedovich, M. (2011), Applying the corridor method to a blocks relocation problem. OR Spectrum 33, pp. 915-929.spa
dc.relation.referencesÇelik, M., & Süral, H. (2016), Order picking in a parallel-aisle warehouse with turn penalties. International Journal of Production Research, 54(14), pp. 4340-4355.spa
dc.relation.referencesChabot, T., Lahyani, R., Coelho, L.C., & Renaud, J. (2017), Order picking problems underweight, fragility and category constraints. International Journal of Production Research, 55(21), pp. 6361-6379.spa
dc.relation.referencesChain, P., & Arunyanart, S. (2019), Using cluster analysis for location decision problem. In IOP Conference Series: Materials Science and Engineering, IOP Publishing, Vol. 673, No. 1, pp. 1-6, DOI: 10.1088/1757-899X/673/1/012086spa
dc.relation.referencesChen, F., Wang, H., Qi, C., & Xie, Y. (2013), An ant colony optimization routing algorithm for two order pickers with congestion consideration. Computers & Industrial Engineering, 66(1), pp. 77-85.spa
dc.relation.referencesChen, L., Riopel, D., & Langevin, A. (2008), Minimising the peak load in a shared storage system based on the duration-of-stay of unit loads. International Journal of Shipping and Transport Logistics, 1(1), pp. 20-36.spa
dc.relation.referencesChinello, E., Herbert-Hansen, Z. N. L., & Khalid, W. (2020), Assessment of the impact of inventory optimization drivers in a multi-echelon supply chain: Case of a toy manufacturer. Computers & Industrial Engineering, 141, 106232.spa
dc.relation.referencesCoindreau, M. A., Gallay, O., Zufferey, N., & Laporte, G. (2021), Inbound and Outbound Flow Integration for Cross-Docking Operations. European Journal of Operational Research, in press, DOI: 10.1016/j.ejor.2021.02.031.spa
dc.relation.referencesCorrea-Espinal, A.A., Gómez-Montoya, R.A., & Cano-Arenas, J.A. (2010), Gestión de almacenes y tecnologías de la información y comunicación (TIC). Estudios gerenciales, 26(117), pp.145-171.spa
dc.relation.referencesCristóbal, L.A., Ascencio, E.G., & Robles, M.L. (2017), The Inventory as a determinant in the profitability of pharmaceutical distributors. Retos: Revista de Ciencias de la Administración y Economía, 13(7), pp. 251-269.spa
dc.relation.referencesCunha Reis, A., de Souza, C.G., da Costa, N.N., Stender, G.H.C., Vieira, P.S., & Pizzolato, N.D. (2017), Warehouse design: a systematic literature review. Brazilian Journal of Operations & Production Management, 14(4), pp. 542-555.spa
dc.relation.referencesDANE [Departamento Administrativo Nacional de Estadística] (2021), Cuentas Nacionales, Colombia. Último acceso el 31 de marzo de 2021. Disponible en: https://www.dane.gov.co/index.php/estadisticas-por-tema/cuentas-nacionales/cuentas-nacionales-anualesspa
dc.relation.referencesDavarzani, H., & Norrman, A. (2015), Toward a relevant agenda for warehousing research: literature review and practitioners’ input. Logistics Research, 8(1), pp. 1-18.spa
dc.relation.referencesDe Azevedo, R. C., Ensslin, L., & Jungles, A. E. (2014), A review of risk management in construction: opportunities for improvement. Modern Economy, 5(04), pp. 367.spa
dc.relation.referencesDe Koster, R., Le Luc, T., Roodbergen, K.J. (2007), Design and control of warehouse order picking: A literature review. European Journal of Operational Research 182, pp. 481–501.spa
dc.relation.referencesDe Koster, R.B.M. (2008), Warehouse assessment in a single tour. In: Facility Logistics: Approaches and Solutions to Next Generation Challenges. Editor: Lahmar, M. Boca Raton, FL: Taylor & Francis Group.spa
dc.relation.referencesDe Koster, R.B.M. (2012), Warehouse assessment in a single tour. In: Warehousing in the Global Supply Chain: Advanced Models, Tools and Applications for Storage Systems. Editor: Manzini, R. Springer-Verlag, London, pp. 457-473.spa
dc.relation.referencesDe Koster, R.B.M., Johnson, A.L. & Roy, D. (2017), Warehouse design and management, International Journal of Production Research, 55(21), pp. 6327-6330.spa
dc.relation.referencesDe Leeuw, S., & Wiers, V. C. (2015), Warehouse manpower planning strategies in times of financial crisis: evidence from logistics service providers and retailers in the Netherlands. Production Planning & Control, 26(4), pp. 328-337.spa
dc.relation.referencesDe Sousa Junior, W. T., Montevechi, J. A. B., de Carvalho Miranda, R., & Campos, A. T. (2019), Discrete simulation-based optimization methods for industrial engineering problems: A systematic literature review. Computers & Industrial Engineering, 128, pp. 526-540.spa
dc.relation.referencesDe Vries, J., de Koster, R., & Stam, D. (2016), Exploring the role of picker personality in predicting picking performance with pick by voice, pick to light and RF-terminal picking. International Journal of Production Research, 54(8), pp. 2260-2274.spa
dc.relation.referencesDepartamento Administrativo Nacional de Estadística [DANE] (2018), Encuesta Anual Manufacturera (EAM) 2017, Bogotá.spa
dc.relation.referencesDerhami, S., Smith, J.S., & Gue, K.R. (2017), Optimising space utilisation in block stacking warehouses. International Journal of Production Research, 55(21), pp. 6436-6452.spa
dc.relation.referencesDerhami, S., Smith, J. S., & Gue, K. R. (2020), A simulation-based optimization approach to design optimal layouts for block stacking warehouses. International Journal of Production Economics, 223, 107525.spa
dc.relation.referencesDirección Nacional de Planeación (2018), Encuesta Nacional de Logística: Resultados Nacionales, Colombia. Disponible en: https://onl.dnp.gov.co/es/enl/Paginas/2018.aspxspa
dc.relation.referencesDey, B., Bairagi, B., Sarkar, B., & Sanyal, S. K. (2016), Warehouse location selection by fuzzy multi-criteria decision making methodologies based on subjective and objective criteria. International Journal of Management Science and Engineer, 11(4), pp. 262-278.spa
dc.relation.referencesDuan, Y., Yao, Y. O., Zhang, X., & Huo, J. (2016), An Empirical Analysis of Cross Docking: Performance and Learning Spillover. Working paper SSRN.spa
dc.relation.referencesDuba, M.G., Das, D.P., Ghadai, S.K., & Bajpai, A. (2019), The Effect of Integrated Warehouse Operation Efficiency on Organizations Performance. International Journal of Recent Technology and Engineering, 8(2), pp. 1664-1668.spa
dc.relation.referencesDubey, A., & Choubey, A. (2017), A Systematic Review on K-Means Clustering Techniques. International Journal of Scientific Research Engineering & Technology 6 (6), pp. 624-627.spa
dc.relation.referencesDuro, D.C., Franklin, S.E., & Dubé, M.G. (2012), A comparison of pixel-based and object-based image analysis with selected machine learning algorithms for the classification of agricultural landscapes using SPOT-5 HRG imagery. Remote sensing of environment, 118, pp. 259-272.spa
dc.relation.referencesDutra, A., Ripoll-Feliu, V. M., Fillol, A. G., Ensslin, S. R., & Ensslin, L. (2015), The construction of knowledge from the scientific literature about the theme seaport performance evaluation. International Journal of Productivity and Performance Management, 64(2), pp. 243-269.spa
dc.relation.referencesElbert, R., & Knigge, J. K. (2018), How order placement influences resource allocation and order processing times inside a multi-user warehouse. In 2018 Winter Simulation Conference (WSC). IEEE, pp. 2921-2932.spa
dc.relation.referencesEnsslin, L., Ensslin, S. R., Lacerda, R. T. D. O., & Tasca, J. E. (2010), ProKnow-C, knowledge development process-constructivist. Processo técnico com patente de registro pendente junto ao INPI. Brasil, 10(4), pp. 2015.spa
dc.relation.referencesEnsslin, S. R., Ensslin, L., Imlau, J. M., & Chaves, L. C. (2014), Processo de mapeamento das publicações científicas de um tema: portfólio bibliográfico e análise bibliométrica sobre avaliação de desempenho de cooperativas de produção agropecuária. Revista de Economia e Sociologia Rural, 52(3), pp. 587-608.spa
dc.relation.referencesFaber, N., De Koster, R.B.M. & Smidts, A. (2013), Organizing warehouse management, International Journal of Operations & Production Management, 33(9), pp. 1230-1256.spa
dc.relation.referencesFaber, N., De Koster, R.B.M., & Van de Velde, S.L. (2002), Linking warehouse complexity to warehouse planning and control structure: an exploratory study of the use of warehouse management information systems. International Journal of Physical Distribution & Logistics Management, 32(5), pp. 381-395.spa
dc.relation.referencesFeng, Z.K., Niu, W. J., Zhang, R., Wang, S., & Cheng, C.T. (2019), Operation rule derivation of hydropower reservoir by k-means clustering method and extreme learning machine based on particle swarm optimization. Journal of Hydrology, 576, pp. 229-238.spa
dc.relation.referencesForo Económico Mundial (2019), Reporte de Competitividad Mundial 2019. Disponible en la página web oficial: http://reports.weforum.org/global-competitiveness-report-2019spa
dc.relation.referencesFränti, P., & Sieranoja, S. (2019), How much can k-means be improved by using better initialization and repeats?. Pattern Recognition, 93, pp. 95-112.spa
dc.relation.referencesGan, G., & Ng, M. K. P. (2017), K-means clustering with outlier removal. Pattern Recognition Letters, 90, pp. 8-14.spa
dc.relation.referencesGiannikas, V., Lu, W., Robertson, B., & McFarlane, D. (2017), An interventionist strategy for warehouse order picking: evidence from two case studies. International Journal of Production Economics, 189, pp. 63-76.spa
dc.relation.referencesGodet, M. (1995), De la anticipación a la acción: manual de prospectiva y estrategia. México: Alfaomega.spa
dc.relation.referencesGómez-Montoya, R. A., Correa-Espinal, A. A., & Hernández-Vahos, J. D. (2016), Picking Routing Problem with K homogenous material handling equipment for a refrigerated warehouse. Revista Facultad de Ingeniería Universidad de Antioquia, 80, pp.9-20.spa
dc.relation.referencesGoodson, R.E. (2002), Read a Plant – Fast. Harvard Business Review, 80(5), pp. 105-121.spa
dc.relation.referencesGoswami, M. (2019), Modeling M Warehouse N Manpower-Team Allocation Problem Using Dynamic Programming Approach. International Journal of Strategic Decision Sciences (IJSDS), 10(4), pp. 100-112.spa
dc.relation.referencesGovender, P., & Sivakumar, V. (2020), Application of k-means and hierarchical clustering techniques for analysis of air pollution: A review (1980-2019). Atmospheric Pollution Research, 11(1), pp. 40-56.spa
dc.relation.referencesGrosse, E.H., Glock, C.H., & Neumann, W. P. (2017), Human factors in order picking: a content analysis of the literature. International Journal of Production Research, 55(5), pp. 1260-1276.spa
dc.relation.referencesGu, J., Goetschalckx, M., & McGinnis, L. F. (2007), Research on warehouse operation: A comprehensive review. European journal of operational research, 177(1), pp.1-21.spa
dc.relation.referencesGu, J., Goetschalckx, M., & McGinnis, L. F. (2010), Research on warehouse design and performance evaluation: A comprehensive review. European Journal of Operational Research, 203(3), pp. 539-549.spa
dc.relation.referencesGue, K. R., & Meller, R. D. (2009), Aisle configurations for unit-load warehouses. IIE Transactions, 41(3), pp. 171-182.spa
dc.relation.referencesGuerriero, F., Musmanno, R., Pisacane, O. & Rende, F. (2013), A mathematical model for the multi-levels product allocation problem in a warehouse with compatibility constraints. Applied Mathematical Modelling 37, pp. 4385-4398.spa
dc.relation.referencesGuerriero, F., Pisacane, O., & Rende, F. (2015), Comparing heuristics for the product allocation problem in multi-level warehouses under compatibility constraints. Applied Mathematical Modelling, 39(23-24), pp. 7375-7389.spa
dc.relation.referencesGuo, X., Yu, Y., & De Koster, R. B. (2016), Impact of required storage space on storage policy performance in a unit-load warehouse. International Journal of Production Research, 54(8), pp. 2405-2418.spa
dc.relation.referencesHaase, J., & Beimborn, D. (2017), Acceptance of Warehouse Picking Systems: A Literature Review. In Proceedings of the 2017 ACM SIGMIS Conference on Computers and People Research, pp. 53-60.spa
dc.relation.referencesHeragu, S.S., Du, L., Mantel, R.J., & Schuur, P.C. (2005), Mathematical model for warehouse design and product allocation. International Journal of Production Research, 43(2), pp. 327-338.spa
dc.relation.referencesHernández Sampieri, R., Fernández Collado, C., & Baptista Lucio, P. (2014). Metodología de la investigación (6th ed.). McGraw-Hill Education.spa
dc.relation.referencesHertog, M. L., Uysal, I., McCarthy, U., Verlinden, B. M., & Nicolaï, B. M. (2014), Shelf life modelling for first-expired-first-out warehouse management. Philosophical Transactions of the Royal Society of London A: Mathematical, Physical and Engineering Sciences, 372 (2017), ID: 20130306, pp. 1-15.spa
dc.relation.referencesHong, S., Kim, Y. (2017), A route-selecting order batching model with the S-shape routes in a parallel-aisle order picking system. European Journal of Operational Research 257 (16), pp. 185-197.spa
dc.relation.referencesHorta, M., Coelho, F., & Relvas, S. (2016), Layout design modelling for a real world just-in-time warehouse. Computers & Industrial Engineering, 101, pp. 1-9.spa
dc.relation.referencesHoseini Shekarabi, S. A., Gharaei, A., & Karimi, M. (2019), Modelling and optimal lot-sizing of integrated multi-level multi-wholesaler supply chains under the shortage and limited warehouse space: generalised outer approximation. International Journal of Systems Science: Operations & Logistics, 6(3), pp. 237-257.spa
dc.relation.referencesHuihui, S., Xiaoxia, M., & Xiangguo, M. (2016), Simulation and optimization of warehouse operation based on Flexsim. Journal of Applied Science and Engineering Innovation, 3(4), pp.125-128.spa
dc.relation.referencesIsler, C. A., Righetto, G. M., & Morabito, R. (2016), Optimizing the order picking of a scholar and office supplies warehouse. The International Journal of Advanced Manufacturing Technology, 87(5-8), pp. 2327-2336.spa
dc.relation.referencesIto, Y., & Kato, S. (2016), Dynamic product placement method in order picking using correlation between products. In Consumer Electronics, October 2016, IEEE 5th Global Conference on (pp. 1-3). IEEE.spa
dc.relation.referencesJachimowski, R., Gołębiowski, P., Izdebski, M., Pyza, D., & Szczepański, E. (2017), Designing and efficiency of database for simulation of processes in systems. Case study for the simulation of warehouse processes. Archives of Transport, 41 (1), pp. 31-42.spa
dc.relation.referencesJahangiri, A., & Rakha, H.A. (2015), Applying machine learning techniques to transportation mode recognition using mobile phone sensor data. IEEE transactions on intelligent transportation systems, 16(5), pp. 2406-2417.spa
dc.relation.referencesJami, N., & Schröder, M. (2016), Tactical and Operational Models for the Management of a Warehouse. In Dynamics in Logistics (pp. 655-665). Springer International Publishing.spa
dc.relation.referencesJang, D. W., Kim, S. W., & Kim, K. H. (2013), The optimization of mixed block stacking requiring relocations. International Journal of Production Economics, 143(2), pp. 256-262.spa
dc.relation.referencesJha, M.K., Raut, R.D., Gardas, B.B., & Raut, V. (2018), A sustainable warehouse selection: an interpretive structural modelling approach. International Journal of Procurement Management, 11(2), pp. 201-232.spa
dc.relation.referencesJohnson, A. & McGinnis, L. (2011), Performance measurement in the warehousing industry, IIE Transactions, 43(3), pp. 220-230.spa
dc.relation.referencesJung, S.H., Kim, K.J., Lim, E.C., & Sim, C.B. (2017), A Novel on Automatic K Value for Efficiency Improvement of K-means Clustering. Advanced Multimedia and Ubiquitous Engineering, pp. 181–186.spa
dc.relation.referencesKan, A. (2017), Machine learning applications in cell image analysis. Immunology and Cell Biology, 95(6), pp. 525-530.spa
dc.relation.referencesKarakis, I., Baskak, M., & Tanyaş, M. (2015), Analytical Model for Optimum Warehouse Dimensions. Research in Logistics & Production, 5(3), pp. 255-269.spa
dc.relation.referencesKaria, N. & Wong, C.Y. (2013), The impact of logistics resources on the performance of Malaysian logistics service providers. Production Planning & Control: The Management of Operations, 24(7), pp. 589-606.spa
dc.relation.referencesKarim, N.H., Rahman, N.S.F.A., & Shah, S.F.S.S.J. (2018), Empirical evidence on failure factors of warehouse productivity in Malaysian logistic service sector. The Asian Journal of Shipping and Logistics, 34(2), pp. 151-160.spa
dc.relation.referencesKassambara, A. (2017), Practical Guide to Cluster Analysis in R: Unsupervised Machine Learning. Volumen 1 de Multivariate Analysis. STHDA.spa
dc.relation.referencesKavakiotis, I., Tsave, O., Salifoglou, A., Maglaveras, N., Vlahavas, I., & Chouvarda, I. (2017), Machine learning and data mining methods in diabetes research. Computational and structural biotechnology journal, 15, pp. 104-116.spa
dc.relation.referencesKembro, J.H. and Norrman, A. (2020), Warehouse configuration in omni-channel retailing: a multiple case study, International Journal of Physical Distribution & Logistics Management, 50(5), pp. 509-533.spa
dc.relation.referencesKhanmohammadi, S., Adibeig, N., & Shanehbandy, S. (2017), An improved overlapping k-means clustering method for medical applications. Expert Systems with Applications, 67, pp. 12-18.spa
dc.relation.referencesKhemavuk, P., & Hasan, M. (2014), A Qualitative Study for Measuring Warehouse Performance Using Triangulation Approach. Journal of Modern Accounting and Auditing, 10(6), pp. 701-707.spa
dc.relation.referencesKlausnitzer, A., & Lasch, R. (2019), Optimal facility layout and material handling network design. Computers & Operations Research, 103, pp. 237-251.spa
dc.relation.referencesKłodawski, M., Lewczuk, K., Jacyna-Gołda, I. & Żak, J. (2017), Decision making strategies for warehouse operations. Archives of Transport, 41(1), pp. 43-53.spa
dc.relation.referencesKostrzewski, M. (2012), Mathematical Models of an Order-Picking Process Time Computing and its Relevance to Real Warehousing Processes. In Carpathian Logistics Congress.spa
dc.relation.referencesKourou, K., Exarchos, T.P., Exarchos, K.P., Karamouzis, M.V., & Fotiadis, D.I. (2015), Machine learning applications in cancer prognosis and prediction. Computational and structural biotechnology journal, 13, pp. 8-17.spa
dc.relation.referencesKrosnick, J. A., Presser, S. (2010), Question and questionnaire design. In Marsden, P. V., Wright, J. D. (Eds.), Handbook of survey research. Bingley, UK: Emerald Group, pp. 263–314.spa
dc.relation.referencesKumar, S., Narkhede, B. E., & Jain, K. (2021), Revisiting the warehouse research through an evolutionary lens: a review from 1990 to 2019. International Journal of Production Research, pp. 1-23.spa
dc.relation.referencesKumar, R., & Singh, S. P. (2017), Designing robust stochastic bi-objective cellular layout in manufacturing systems. International Journal of Management Concepts and Philosophy, 10(2), pp. 147-164.spa
dc.relation.referencesKumar, A., Sah, B., Singh, A. R., Deng, Y., He, X., Kumar, P., & Bansal, R. C. (2017), A review of multi criteria decision making (MCDM) towards sustainable renewable energy development. Renewable and Sustainable Energy Reviews, 69, pp. 596-609.spa
dc.relation.referencesLadier, A. L., & Alpan, G. (2016), Cross-docking operations: Current research versus industry practice. Omega, 62, pp.145-162.spa
dc.relation.referencesLaosirihongthong, T., Adebanjo, D., Samaranayake, P., Subramanian, N. & Boon-itt, S. (2018), Prioritizing warehouse performance measures in contemporary supply chains, International Journal of Productivity and Performance Management, 67(9), pp. 1703-1726.spa
dc.relation.referencesLarco, J.A., De Koster, M.B.M., Roodbergen, K.J. & Dul, J. (2017), Managing warehouse efficiency and worker discomfort through enhanced storage assignment decisions, International Journal of Production Research, 55(21), pp. 6407-6422.spa
dc.relation.referencesLee, I.G., Chung, S.H., & Yoon, S.W. (2020), Two-stage storage assignment to minimize travel time and congestion for warehouse order picking operations. Computers & Industrial Engineering, 139, pp. 106-129.spa
dc.relation.referencesLee, J.A., Chang, Y.S., Shim, H.J., & Cho, S.J. (2015), A study on the picking process time. Procedia Manufacturing, 3, pp. 731-738.spa
dc.relation.referencesLewczuk K., Kłodawski M. & Jacyna-Gołda I. (2018), Selected Aspects of Warehouse Process Control and the Quality of Warehouse Services. In: Mikulski J. (eds) Management Perspective for Transport Telematics. Communications in Computer and Information Science, vol. 897. Springer, Cham, pp. 445-459.spa
dc.relation.referencesLi, J., Moghaddam, M., & Nof, S. Y. (2016), Dynamic storage assignment with product affinity and ABC classification—a case study. The International Journal of Advanced Manufacturing Technology, 84(9-12), pp. 2179-2194.spa
dc.relation.referencesLi, S., Sari, Y.A., & Kumral, M. (2020), Optimization of Mining–Mineral Processing Integration Using Unsupervised Machine Learning Algorithms, Natural Resources Research, pp. 1-12.spa
dc.relation.referencesLi, M. L., Wolf, E., & Wintz, D. (2019), Application of Duration-of-Stay Storage Assignment with Deep Neural Networks under Uncertainty. International Conference on Learning Representations.spa
dc.relation.referencesLibbrecht, M.W., & Noble, W.S. (2015), Machine learning applications in genetics and genomics. Nature Reviews Genetics, 16(6), pp. 321–332.spa
dc.relation.referencesLiu, J., Zhang, H., He, K., & Jiang, S. (2018), Multi-objective particle swarm optimization algorithm based on objective space division for the unequal-area facility layout problem. Expert Systems with Applications, 102, pp. 179-192.spa
dc.relation.referencesLiu, T., Duan, Y., & Liu, Y. (2016), Simulation and optimization of the AS/RS based on Flexsim. In Frontier Computing. Springer, Singapore. pp. 855-863.spa
dc.relation.referencesLoos, M. J., Merino, E., & Rodriguez, C. M. T. (2016), Mapping the state of the art of ergonomics within logistics. Scientometrics, 109(1), pp. 85-101.spa
dc.relation.referencesLu, W., McFarlane, D., Giannikas, V., & Zhang, Q. (2016), An algorithm for dynamic order-picking in warehouse operations. European Journal of Operational Research, 248(1), pp. 107-122.spa
dc.relation.referencesMa, Y., Zhang, Z., Ihler, A., & Pan, B. (2018), Estimating warehouse rental price using machine learning techniques. International Journal of Computers Communications & Control, 13(2), pp. 235-250.spa
dc.relation.referencesMaciel, J. N., Junior, O. H. A., & Ledesma, J. J. G. (2020), The Forecasting Solar Power Output Generation: A Systematic Review with the Proknow-C. IEEE Latin America Transactions, 19(4), pp. 612-624.spa
dc.relation.referencesMacro, J. G., & Salmi, R. E. (2002), Warehousing and inventory management: a simulation tool to determine warehouse efficiencies and storage allocations. In Proceedings of the 34th conference on Winter simulation: exploring new frontiers (pp. 1274-1281). Winter Simulation Conference.spa
dc.relation.referencesMakaci, M., Reaidy, P., Evrard-Samuel, K., Botta-Genoulaz, V., & Monteiro, T. (2017), Pooled warehouse management: An empirical study. Computers & Industrial Engineering, 112, pp. 526-536.spa
dc.relation.referencesManzini, R., Accorsi, R., Gamberi, M., & Penazzi, S. (2015), Modeling class-based storage assignment over life cycle picking patterns. International Journal of Production Economics, 170, 790-800.spa
dc.relation.referencesMasae, M., Glock, C.H., & Vichitkunakorn, P. (2020), Optimal order picker routing in the chevron warehouse. IISE Transactions, 52(6), pp, 665-687.spa
dc.relation.referencesMathlouthi, W., Saoud, N. B. B., & Sboui, S. (2015), Agent-based modeling and simulation of pooled warehouse intelligent management. In Proceedings of the Conference on Summer Computer Simulation, pp. 1-8.spa
dc.relation.referencesMatson, J.O., Sonnentag, J.J., White, J.A., & Imhoff, R.C. (2014), An Analysis of Block Stacking with Lot Splitting. In IIE Annual Conference. Proceedings. Institute of Industrial Engineers-Publisher, pp. 497.spa
dc.relation.referencesMcKinnon, A., Flöthmann, C., Hoberg, K., & Busch, C. (2017), Logistics Competencies, Skills, and Training: A Global Overview. The World Bank. Washington, DC.spa
dc.relation.referencesMeller, R.D., Gue, K.R. (2009), The application of new aisle design for unit-load warehouses. Proceedings of 2009 NSF Engineering Research and Innovation Conference, Honolulu, Hawaii, pp. 1-8.spa
dc.relation.referencesMelnykov, I., & Melnykov, V. (2014), On K-means algorithm with the use of Mahalanobis distances. Statistics & Probability Letters, 84, pp. 88-95.spa
dc.relation.referencesMontgomery, D.C. (2017), Design and analysis of experiments. 9th Edition, Hoboken, NJ: John Wiley & Sons.spa
dc.relation.referencesMuha, R. (2019). An Overview of the Problematic Issues in Logistics Cost Management. Pomorstvo, 33(1), pp. 102-109.spa
dc.relation.referencesMuhalia, E. J., Ngugi, P. K., & Moronge, M. (2021), Effect of warehouse management systems on supply chain performance of fast-moving consumer goods manufacturers in Kenya. International Journal of Supply Chain Management, 6(1), pp. 1-11.spa
dc.relation.referencesMüller, A.C., & Guido, S. (2017), Introduction to machine learning with Python: a guide for data scientists, O'Reilly Media, Inc. Sebastopol. ISBN: 978-1449369415.spa
dc.relation.referencesMuchanga, M. (2020), Reflexive Debate on Use of Philosophy in Scientific Research. International Journal of Humanities, Social Sciences and Education, 7(6), pp. 208-2013.spa
dc.relation.referencesNa, S., Xumin, L., & Yong, G. (2010), Research on k-means clustering algorithm: An improved k-means clustering algorithm. In 2010 Third International Symposium on intelligent information technology and security informatics. IEEE, pp. 63-67.spa
dc.relation.referencesNikolopoulou, A. I., Repoussis, P. P., Tarantilis, C. D., & Zachariadis, E. E. (2017), Moving products between location pairs: Cross-docking versus direct-shipping. European Journal of Operational Research, 256(3), pp. 803-819.spa
dc.relation.referencesOcicka, B., & Wieteska, G. (2017), Sharing economy in logistics and supply chain management. LogForum, 13(2), pp. 183-193.spa
dc.relation.referencesOgbuabor, G., & Ugwoke, F. N. (2018), Clustering algorithm for a healthcare dataset using silhouette score value. International Journal of Computer Science & Information Technology, 10(2), pp. 27-37.spa
dc.relation.referencesOsorio, M. A., & Suárez, A. B. (2014), Importancia de la probabilidad y la estadística en la formación del Ingeniero. I3+ 1(2), pp. 26-37.spa
dc.relation.referencesÖztürkoğlu, Ö., Gue, K. R., & Meller, R. D. (2014), A constructive aisle design model for unit-load warehouses with multiple pickup and deposit points. European Journal of Operational Research, 236(1), pp. 382-394.spa
dc.relation.referencesÖzyer, T., & Alhajj, R. (2018), Machine Learning Techniques for Online Social Networks. Editorial Springer. Cham.spa
dc.relation.referencesPan, F., Zhou, W., Fan, T., Li, S., & Zhang, C. (2021), Deterioration rate variation risk for sustainable cross-docking service operations. International Journal of Production Economics, 232, in press, 107932.spa
dc.relation.referencesPan, J.C.H., Shih, P.H., Wu, M.H. (2012), Storage assignment problem with travel distance and blocking considerations for a picker-to-part order picking system. Computers & Industrial Engineering 62, pp. 527–535.spa
dc.relation.referencesPan, L., Liu, G., Lin, F., Zhong, S., Xia, H., Sun, X., & Liang, H. (2017), Machine learning applications for prediction of relapse in childhood acute lymphoblastic leukemia. Scientific reports, 7(1), pp. 1-9.spa
dc.relation.referencesPang, K.W., & Chan, H.L. (2017), Data mining-based algorithm for storage location assignment in a randomized warehouse. International Journal of Production Research, 55(14), pp. 4035-4052.spa
dc.relation.referencesPanhwar, A.H., Ansari, S., & Shah, A.A. (2017), Post-positivism: An effective paradigm for social and educational research. International Research Journal of Arts & Humanities, 45(45), pp. 253-260.spa
dc.relation.referencesPatten, M. L. (2017), Questionnaire research: A practical guide. 4ᵃ edición. Routledge.spa
dc.relation.referencesPohl, L.M., Meller, R.D., Gue, K.R. (2009), An analysis of dual-command operations in common warehouse designs. Transportation Research Part E 45, pp. 367–379.spa
dc.relation.referencesPrause, M. (2019), Challenges of Industry 4.0 technology adoption for SMEs: The case of Japan. Sustainability, 11(20), 5807.spa
dc.relation.referencesPyza, D., Jachimowski, R., Jacyna-Gołda, I., & Lewczuk, K. (2017), Performance of equipment and means of internal transport and efficiency of implementation of warehouse processes. Procedia Engineering, 187, pp. 706-711.spa
dc.relation.referencesQuintanilla, S., Pérez, Á., Ballestín, F., & Lino, P. (2015), Heuristic algorithms for a storage location assignment problem in a chaotic warehouse. Engineering Optimization, 47(10), pp. 1405-1422.spa
dc.relation.referencesRachad, S., El Idrissi Larabi, Z., Nsiri, B., & Bensassi, B. (2017), Inventory management in closed loop structure using KPIs. International Journal of Applied Engineering Research, 12(15), pp. 4864-4869.spa
dc.relation.referencesRahman, M. S. (2020), The advantages and disadvantages of using qualitative and quantitative approaches and methods in language “testing and assessment” research: A literature review. Journal of Education and Learning, 6 (1), pp. 102-112.spa
dc.relation.referencesRai, P., & Singh, S. (2010), A survey of clustering techniques. International Journal of Computer Applications, 7(12), pp. 1-5.spa
dc.relation.referencesRamaa, A., Subramanya, K. N., & Rangaswamy, T. M. (2012), Impact of warehouse management system in a supply chain. International Journal of Computer Applications, 54(1), pp. 14-20.spa
dc.relation.referencesRamírez, T.J. (2014), Recuperación de zonas industriales: una oportunidad de desarrollo. El caso de Puente Aranda. Tesis de Maestría en Urbanismo, Universidad Nacional de Colombia. Bogotá.spa
dc.relation.referencesRamli, A., Bakar, M.S., Pulka, B.M., & Ibrahim, N.A. (2017), Linking Human Capital, Information Technology and Material Handling Equipment to Warehouse Operations Performance. International Journal of Supply Chain Management, 6(4), pp. 254-259.spa
dc.relation.referencesRao, S.S., & Adil, G.K. (2017), Analytical models for a new turnover-based hybrid storage policy in unit-load warehouses. International Journal of Production Research, 55(2), pp. 327-346.spa
dc.relation.referencesRaschka, S., & Mirjalili, V. (2019), Python Machine Learning: Aprendizaje automático y aprendizaje profundo con Python, scikit-learn y TensorFlow 2. Segunda edición, Bogotá, Marcombo S.A. ISBN: 978-84-267-2720-6.spa
dc.relation.referencesRaut, R.D., Narkhede, B.E., Gardas, B.B., & Raut, V. (2017), Multi-criteria decision making approach: a sustainable warehouse location selection problem. International Journal of Management Concepts and Philosophy, 10(3), pp. 260-281.spa
dc.relation.referencesRichards, G. (2018), Warehouse management: a complete guide to improving efficiency and minimizing costs in the modern warehouse. 3rd. edition, Kogan Page Publishers.spa
dc.relation.referencesRodríguez, J.J. (2013), Diseño prospectivo de escenarios para la ciencia, tecnología e innovación al 2040. Industrial Data, 16(2), pp. 92-105.spa
dc.relation.referencesRoodbergen, K. J., & De Koster, R. (2001b), Routing order pickers in a warehouse with a middle aisle. European Journal of Operational Research, 133(1), pp. 32-43.spa
dc.relation.referencesRoodbergen, K. J., & De Koster, R. (2001a), Routing methods for warehouses with multiple cross aisles. International Journal of Production Research, 39(9), pp. 1865-1883.spa
dc.relation.referencesRoodbergen, K. J., Sharp, G. P., & Vis, I. F. (2008), Designing the layout structure of manual order picking areas in warehouses. IIE Transactions, 40(11), pp. 1032-1045.spa
dc.relation.referencesRosa, F. S., & Silva, L. C. (2017), Environmental sustainability in hotels, theoretical and methodological contribution. Revista Brasileira de Pesquisa em Turismo, 11(1), pp. 39-60.spa
dc.relation.referencesRouwenhorst, B., Reuter, B., Stockrahm, V., van Houtum, G.J., Mantel, R.J., & Zijm, W.H. (2000), Warehouse design and control: Framework and literature review. European journal of operational research, 122(3), pp. 515-533.spa
dc.relation.referencesRybakov, A.A., & Shumilin, S.S. (2019), Outliers detection by voting method during hierarchical data clustering. Software Journal: Theory and Applications, 3, pp. 1-7.spa
dc.relation.referencesSajana, T., Rani, C. S., & Narayana, K. V. (2016), A survey on clustering techniques for big data mining. Indian journal of Science and Technology, 9(3), pp. 1-12.spa
dc.relation.referencesSaleheen, F., Miraz, M. H., Habib, M. M., & Hanafi, Z. (2014), Challenges of Warehouse Operations: A Case Study in Retail Supermarket. International Journal of Supply Chain Management, 3(4), pp. 63-67.spa
dc.relation.referencesSchwarz, L.B., Graves, S.C. & Hausman, W.H. (1978), Scheduling Policies for Automatic Warehousing Systems: Simulation Results, AIIE Transactions, 10 (3), pp. 260-270.spa
dc.relation.referencesShah, B., & Khanzode, V. (2015), A comprehensive review and proposed framework to design lean storage and handling systems. International Journal of Advanced Operations Management, 7(4), pp. 274-299.spa
dc.relation.referencesSharma, P. (2015), Discrete-event simulation. International journal of scientific & technology research, 4(4), pp. 136-140.spa
dc.relation.referencesSharma, S., Abouee‐Mehrizi, H., & Sartor, G. (2020), Inventory management under storage and order restrictions. Production and Operations Management, 29(1), pp. 101-117.spa
dc.relation.referencesShi, Y., Guo, X., & Yu, Y. (2018), Dynamic warehouse size planning with demand forecast and contract flexibility. International Journal of Production Research, 56(3), pp. 1313-1325.spa
dc.relation.referencesShin, E. J., & Kim, K. H. (2015), Hierarchical remarshaling operations in block stacking storage systems considering duration of stay. Computers & Industrial Engineering, 89, pp. 43-52.spa
dc.relation.referencesShteren, H., & Avrahami, A. (2017), The Value of Inventory Accuracy in Supply Chain Management: Case Study of the Yedioth Communication Press. Journal of theoretical and applied electronic commerce research, 12(2), pp. 71-86.spa
dc.relation.referencesSilva, A., Coelho, L. C., Darvish, M., & Renaud, J. (2020), Integrating storage location and order picking problems in warehouse planning. Transportation Research Part E: Logistics and Transportation Review, 140, pp. 1-22.spa
dc.relation.referencesSchweidtmann, A. M., Clayton, A. D., Holmes, N., Bradford, E., Bourne, R. A., & Lapkin, A. A. (2018), Machine learning meets continuous flow chemistry: Automated optimization towards the Pareto front of multiple objectives. Chemical Engineering Journal, 352, pp. 277-282.spa
dc.relation.referencesSingh, S. (2017), Survey of Literature on Various Factors Affecting Inventory Management. Universal Journal of Materials Science, 5(1), pp. 1-6.spa
dc.relation.referencesSohail, A., & Arif, F. (2020), Supervised and unsupervised algorithms for bioinformatics and data science. Progress in Biophysics and Molecular Biology, 151, pp. 14-22.spa
dc.relation.referencesSohn, S.Y., Han, H.K., & Jeon, H.J. (2007), Development of an Air Force Warehouse Logistics Index to continuously improve logistics capabilities. European Journal of Operational Research, 183(1), pp. 148-161.spa
dc.relation.referencesSprock, T., Murrenhoff, A., & McGinnis, L.F. (2017), A hierarchical approach to warehouse design. International Journal of Production Research, 55(21), pp. 6331-6343.spa
dc.relation.referencesStaudt, F.H., Alpan, G., Di Mascolo, M., & Rodriguez, C.M.T. (2015), Warehouse performance measurement: a literature review. International Journal of Production Research, 53(18), pp. 5524-5544.spa
dc.relation.referencesTan, K.S., Ahmed, M.D., & Sundaram, D. (2009), Sustainable warehouse management. In Proceedings of the International Workshop on Enterprises & Organizational Modeling and Simulation, 8, pp. 1-15.spa
dc.relation.referencesTarczyński, G. (2017), The impact of COI-based storage on order-picking times. LogForum, 13(3), pp. 313-326.spa
dc.relation.referencesTheys, C., Bräysy, O., Dullaert, W., & Raa, B. (2010), Using a TSP heuristic for routing order pickers in warehouses. European Journal of Operational Research, 200(3), 755-763.spa
dc.relation.referencesTompkins, J.A., White, J.A., Bozer, Y.A., Tanchoco, J.M.A. (2010), Facilities planning (4th Ed.) New York: John Wiley & Sons.spa
dc.relation.referencesTutam, M., & White, J.A. (2019), Multi-dock unit-load warehouse designs with a cross-aisle. Transportation Research Part E: Logistics and Transportation Review, 129, pp. 247-262.spa
dc.relation.referencesUday S.V., Hamritha, C.G. (2021), Linear Programming in Market Management Using Artificial Intelligence. In: Vijayan S., Subramanian N., Sankaranarayanasamy K. (eds) Trends in Manufacturing and Engineering Management. Lecture Notes in Mechanical Engineering. Springer, Singapore, in press.spa
dc.relation.referencesVan den Berg, J.P., & Zijm, W.H. (1999), Models for warehouse management: Classification and examples. International Journal of Production Economics, 59(1-3), pp. 519-528.spa
dc.relation.referencesVan Gils, T., Caris, A., Ramaekers, K., Braekers, K., & de Koster, R. B. (2019), Designing efficient order picking systems: The effect of real-life features on the relationship among planning problems. Transportation Research Part E: Logistics and Transportation Review, 125, pp. 47-73.spa
dc.relation.referencesVan Gils, T., Ramaekers, K., Caris, A., & Cools, M. (2017), The use of time series forecasting in zone order picking systems to predict order pickers’ workload. International Journal of Production Research, 55(21), pp. 6380-6393.spa
dc.relation.referencesVan Gils, T., Ramaekers, K., Caris, A., & de Koster, R. B. (2018), Designing efficient order picking systems by combining planning problems: State-of-the-art classification and review. European Journal of Operational Research, 267(1), pp. 1-15.spa
dc.relation.referencesVenkitasubramony, R., & Adil, G. K. (2016), Analytical models for pick distances in fishbone warehouse based on exact distance contour. International Journal of Production Research, 54(14), pp. 4305-4326.spa
dc.relation.referencesVenkitasubramony, R., & Adil, G. K. (2017), Design of an order-picking warehouse factoring vertical travel and space sharing. The International Journal of Advanced Manufacturing Technology, 91(5-8), pp. 1921-1934.spa
dc.relation.referencesWalter, R., Boysen, N., & Scholl, A. (2013), The discrete forward–reserve problem–Allocating space, selecting products, and area sizing in forward order picking. European Journal of Operational Research, 229(3), pp. 585-594.spa
dc.relation.referencesWang, Y., Geng, X., Zhang, F., & Ruan, J. (2018), An immune genetic algorithm for multi-echelon inventory cost control of IOT based supply chains. IEEE Access, 6, pp. 8547-8555.spa
dc.relation.referencesYang, Y., & Wang, H. (2018), Multi-view clustering: A survey. Big Data Mining and Analytics, 1(2), pp. 83-107.spa
dc.relation.referencesYee, O.S., Sagadevan, S., & Malim, N.H.A.H. (2018), Credit card fraud detection using machine learning as data mining technique. Journal of Telecommunication, Electronic and Computer Engineering, 10(1-4), pp. 23-27.spa
dc.relation.referencesYener, F., & Yazgan, H.R. (2019), Optimal warehouse design: Literature review and case study application. Computers & Industrial Engineering, 129, pp. 1-13.spa
dc.relation.referencesYu, Y., Koster, R., & Guo, X. (2015), Class‐Based Storage with a Finite Number of Items: Using More Classes is not Always Better. Production and operations management, 24(8), pp. 1235-1247.spa
dc.relation.referencesYuan, X. (2017), An improved Apriori algorithm for mining association rules. In AIP conference proceedings (Vol. 1820, No. 1, p. 080005). AIP Publishing LLC.spa
dc.relation.referencesZaerpour, N., de Koster, R. B., & Yu, Y. (2013), Storage policies and optimal shape of a storage system. International Journal of Production Research, 51(23-24), pp. 6891-6899.spa
dc.relation.referencesZhang, G., Shang, X., Alawneh, F., Yang, Y., & Nishi, T. (2021), Integrated production planning and warehouse storage assignment problem: An IoT assisted case. International Journal of Production Economics, in press, 108058.spa
dc.relation.referencesZhang, G., Nishi, T., Turner, S. D., Oga, K., & Li, X. (2017), An integrated strategy for a production planning and warehouse layout problem: Modeling and solution approaches. Omega, 68, pp. 85-94.spa
dc.relation.referencesZhu, Y. (2017), Application of Information System in Warehouse Management. DEStech Transactions on Computer Science and Engineering, 2nd International Conference on Computer Engineering, Information Science and Internet Technology.spa
dc.relation.referencesZunic, E., Besirevic, A., Skrobo, R., Hasic, H., Hodzic, K., & Djedovic, A. (2017), Design of optimization system for warehouse order picking in real environment. In 2017 XXVI International Conference on Information, Communication and Automation Technologies (ICAT). IEEE, pp. 1-6.spa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.rights.licenseAtribución-NoComercial-CompartirIgual 4.0 Internacionalspa
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/spa
dc.subject.ddc620 - Ingeniería y operaciones afinesspa
dc.subject.lembWarehouseseng
dc.subject.lembAlmacenes generales de depósitospa
dc.subject.lembPhysical distribution of goodseng
dc.subject.lembDistribución física de mercancíasspa
dc.subject.lembBusiness logisticseng
dc.subject.lembLogística en los negociosspa
dc.subject.proposalLogística de almacenamientospa
dc.subject.proposalAsignación de espacios de almacénspa
dc.subject.proposalTiempo de preparación de pedidosspa
dc.subject.proposalDiseño de almacenesspa
dc.subject.proposalWarehouse logisticseng
dc.subject.proposalStorage allocationfra
dc.subject.proposalOrder picking timeeng
dc.subject.proposalWarehouse designeng
dc.titleMétodo de diseño y asignación dinámica de espacios de almacenamientospa
dc.title.translatedMethod for the design and dynamic storage allocation spaceseng
dc.typeTrabajo de grado - Doctoradospa
dc.type.coarhttp://purl.org/coar/resource_type/c_db06spa
dc.type.coarversionhttp://purl.org/coar/version/c_ab4af688f83e57aaspa
dc.type.contentTextspa
dc.type.driverinfo:eu-repo/semantics/doctoralThesisspa
dc.type.redcolhttp://purl.org/redcol/resource_type/TDspa
dc.type.versioninfo:eu-repo/semantics/acceptedVersionspa
dcterms.audience.professionaldevelopmentPúblico generalspa
oaire.accessrightshttp://purl.org/coar/access_right/c_abf2spa
oaire.fundernameUniversidad Militar Nueva Granadaspa

Archivos

Bloque original

Mostrando 1 - 1 de 1
Cargando...
Miniatura
Nombre:
80060174.2021.pdf
Tamaño:
4.72 MB
Formato:
Adobe Portable Document Format
Descripción:
Tesis de Doctorado en Ingeniería - Industria y Organizaciones

Bloque de licencias

Mostrando 1 - 1 de 1
Cargando...
Miniatura
Nombre:
license.txt
Tamaño:
3.87 KB
Formato:
Item-specific license agreed upon to submission
Descripción: