Improving cross-docking operations for consumer goods sector using metaheuristics

This paper aims to model a consumer goods cross-docking problem, which is solved using metaheuristics to minimize makespan and determine the capacity in terms of inbound and outbound docks. The consumer-goods cross-docking problem is represented through inbound and outbound docks, customer orders (p...

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Tipo de recurso:
Fecha de publicación:
2021
Institución:
Universidad de Medellín
Repositorio:
Repositorio UDEM
Idioma:
eng
OAI Identifier:
oai:repository.udem.edu.co:11407/5888
Acceso en línea:
http://hdl.handle.net/11407/5888
Palabra clave:
Consumer goods sector
Cross-docking
Distribution center
Particle swarm optimization
Simulated annealing
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http://purl.org/coar/access_right/c_16ec
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oai_identifier_str oai:repository.udem.edu.co:11407/5888
network_acronym_str REPOUDEM2
network_name_str Repositorio UDEM
repository_id_str
dc.title.none.fl_str_mv Improving cross-docking operations for consumer goods sector using metaheuristics
title Improving cross-docking operations for consumer goods sector using metaheuristics
spellingShingle Improving cross-docking operations for consumer goods sector using metaheuristics
Consumer goods sector
Cross-docking
Distribution center
Particle swarm optimization
Simulated annealing
title_short Improving cross-docking operations for consumer goods sector using metaheuristics
title_full Improving cross-docking operations for consumer goods sector using metaheuristics
title_fullStr Improving cross-docking operations for consumer goods sector using metaheuristics
title_full_unstemmed Improving cross-docking operations for consumer goods sector using metaheuristics
title_sort Improving cross-docking operations for consumer goods sector using metaheuristics
dc.subject.spa.fl_str_mv Consumer goods sector
Cross-docking
Distribution center
Particle swarm optimization
Simulated annealing
topic Consumer goods sector
Cross-docking
Distribution center
Particle swarm optimization
Simulated annealing
description This paper aims to model a consumer goods cross-docking problem, which is solved using metaheuristics to minimize makespan and determine the capacity in terms of inbound and outbound docks. The consumer-goods cross-docking problem is represented through inbound and outbound docks, customer orders (products to be delivered to customers), and metaheuristics as a solution method. Simulated annealing (SA) and particle swarm optimization (PSO) are implemented to solve the cross-docking problem. Based on the results of statistical analysis, it was identified that the two-way interaction effect between inbound and outbound docks, outbound docks and items, and items and metaheuristics are the most statistically significant on the response variable. The best solution provides the minimum makespan of 973.42 minutes considering nine inbound docks and twelve outbound docks. However, this study detected that the combination of six inbound docks and nine outbound docks represents the most efficient solution for a crossdocking design since it reduces the requirement of docks by 28.6% and increases the makespan by only 4.2% when compared to the best solution, representing a favorable trade-off for the cross-docking platform design. © 2021, Institute of Advanced Engineering and Science. All rights reserved.
publishDate 2021
dc.date.accessioned.none.fl_str_mv 2021-02-05T14:57:33Z
dc.date.available.none.fl_str_mv 2021-02-05T14:57:33Z
dc.date.none.fl_str_mv 2021
dc.type.eng.fl_str_mv Article
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dc.identifier.issn.none.fl_str_mv 20893191
dc.identifier.uri.none.fl_str_mv http://hdl.handle.net/11407/5888
dc.identifier.doi.none.fl_str_mv 10.11591/eei.v10i1.2710
identifier_str_mv 20893191
10.11591/eei.v10i1.2710
url http://hdl.handle.net/11407/5888
dc.language.iso.none.fl_str_mv eng
language eng
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dc.relation.citationvolume.none.fl_str_mv 10
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dc.relation.citationstartpage.none.fl_str_mv 524
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dc.relation.references.none.fl_str_mv Chandriah, K. K., Raghavendra, N. V, Multi-objective optimization for preemptive and predictive supply chain operation (2020) Int. J. Electr. Comput. Eng, 10 (2), pp. 1533-1543
Mezouar, H., El Afia, A., Proposal for an approach to evaluate continuity in service supply chains: case of the Moroccan electricity supply chain (2019) Int. J. Electr. Comput. Eng, 9 (6), pp. 5552-5559
Rijal, A., Bijvank, M., De Koster, R., Integrated scheduling and assignment of trucks at unit-load cross-dock terminals with mixed service mode dock doors (2019) Eur. J. Oper. Res, 278 (3), pp. 752-771
Monaco, M. F., Sammarra, M., Managing loading and discharging operations at cross-docking terminals (2020) Procedia Manuf, 42, pp. 475-482
Gaudioso, M., Flavia, M., Sammarra, M., A Lagrangian heuristics for the truck scheduling problem in multidoor, multi-product Cross-Docking with constant processing time (2020) Omega, , press
Nogueira, T. H., Coutinho, F. P., Ribeiro, R. P., Ravetti, M. G., Parallel-machine scheduling methodology for a multi-dock truck sequencing problem in a cross-docking center (2020) Comput. Ind. Eng, 143, p. 106391. , March
Fonseca, G. B., Nogueira, T. H., Ravetti, M. G., A hybrid Lagrangian metaheuristic for the cross-docking flow shop scheduling problem (2019) Eur. J. Oper. Res, 275 (1), pp. 139-154
Assadi, M. T., Bagheri, M., Engineering differential evolution and population-based simulated annealing for truck scheduling problem in multiple door cross-docking systems (2016) Comput. Ind. Eng, 96, pp. 149-161
Nikolopoulou, A. I., Repoussis, P. P., Tarantilis, C. D., Zachariadis, E. E., Moving products between location pairs: Cross-docking versus (2017) Eur. J. Oper. Res, 256 (3), pp. 803-819
Wisittipanich, W., Hengmeechai, P., Truck scheduling in multi-door cross docking terminal by modified particle swarm optimization (2017) Comput. Ind. Eng, 113, pp. 793-802
Goodarzi, A. H., Zegordi, S. H., A location-routing problem for cross-docking networks: A biogeography-based optimization algorithm (2016) Comput. Ind. Eng, 102, pp. 132-146
Maknoon, M. Y., Soumis, F., Baptiste, P., Optimizing transshipment workloads in less-than-truckload (2016) Intern. J. Prod. Econ, 179, pp. 90-100
Kusolpuchong, S., Chusap, K., Alhawari, O., Suer, G., A Genetic Algorithm Approach for Multi Objective Cross Dock Scheduling in Supply Chains (2019) Procedia Manuf, 39, pp. 1139-1148
Aziz, M. A., Ninggal, I. H., Scalable workflow scheduling algorithm for minimizing makespan and failure probability (2019) Bull. Electr. Eng. Informatics, 8 (1), pp. 283-290
Golshahi-roudbaneh, A., Hajiaghaei-keshteli, M., Paydar, M. M., Developing a lower bound and strong heuristics for a truck scheduling problem in a cross-docking center (2017) Knowl.-Based Syst, 129, pp. 17-38
Correa, A. A., Gómez, R. A., Cano, J. A., Warehouse management and information and communication technology (2010) Estud. Gerenciales, 26 (117), pp. 145-171
Van Belle, J., Valckenaers, P., Cattrysse, D., Cross-docking: State of the art (2012) Omega, 40 (6), pp. 827-846
Cano, J. A., Order picking optimization based on a picker routing heuristic: minimizing total traveled distance in warehouses (2020) Handbook of Research on the Applications of International Transportation and Logistics for World Trade, pp. 74-96. , G. Ç. Ceyhun, Ed. PA, USA: IGI Global
Cano, J. A., Baena, J. J., Trends in the use of information and communication technologies for international negotiation (2015) Estud. Gerenciales, 31 (136), pp. 335-346
Cano, J. A., Baena, J. J., Impact of information and communication technologies in international negotiation performance (2015) Revista Brasileira de Gestão de Negócios, 17 (54), pp. 751-768
Amini, A., Tavakkoli-moghaddam, R., Omidvar, A., Cross-docking truck scheduling with the arrival times for inbound trucks and the learning effect for unloading/loading processes (2014) Prod. Manuf. Res, 2 (1), pp. 784-804
Gelareh, S., Glover, F., Guemri, O., Hanafi, S., Nduwayo, P., Todosijevic, R., A comparative study of formulations for a cross-dock door assignment problem (2020) Omega, 91, p. 102015
Cano, J. A., Formulations for joint order picking problems in low-level picker-to-part systems (2020) Bull. Electr. Eng. Informatics, 9 (2), pp. 836-844
Cano, J. A., Correa-Espinal, A. A., Gómez-Montoya, R. A., Mathematical programming modeling for joint order batching, sequencing and picker routing problems in manual order picking systems (2019) J. King Saud Univ.-Eng. Sci, 32 (3), pp. 219-228
Baizal, Z. K. A., Lhaksmana, K. M., Rahmawati, A. A., Kirom, M., Mubarok, Z., Travel route scheduling based on user’s preferences using simulated annealing (2019) Int. J. Electr. Comput. Eng, 9 (2), pp. 1275-1287
Shahmardan, A., Sajadieh, M. S., Truck scheduling in a multi-door cross-docking center with partial unloading – Reinforcement learning-based simulated annealing approaches (2020) Comput. Ind. Eng, 139, p. 106134
Mousavi, S. M., Tavakkoli-Moghaddam, R., Siadat, A., Optimal design of the cross-docking in distribution networks: Heuristic solution approach (2014) Int. J. Eng. Trans. A Basics, 27 (4), pp. 533-544
Abdul-Adheem, W. R., An enhanced particle swarm optimization algorithm (2019) Int. J. Electr. Comput. Eng, 9 (6), pp. 4904-4907
Shin, T. M., Adam, A., Abidin, A. F. Z., A comparative study of PSO, GSA and SCA in parameters optimization of surface grinding process (2019) Bull. Electr. Eng. Informatics, 8 (3), pp. 1117-1127
Montgomery, D. C., (2019) Design and analysis of experiments, , 10th ed. Massachusetts: John Wiley & Sons Inc
dc.rights.coar.fl_str_mv http://purl.org/coar/access_right/c_16ec
rights_invalid_str_mv http://purl.org/coar/access_right/c_16ec
dc.publisher.none.fl_str_mv Institute of Advanced Engineering and Science
dc.publisher.program.spa.fl_str_mv Administración de Empresas
dc.publisher.faculty.spa.fl_str_mv Facultad de Ciencias Económicas y Administrativas
publisher.none.fl_str_mv Institute of Advanced Engineering and Science
dc.source.none.fl_str_mv Bulletin of Electrical Engineering and Informatics
institution Universidad de Medellín
repository.name.fl_str_mv Repositorio Institucional Universidad de Medellin
repository.mail.fl_str_mv repositorio@udem.edu.co
_version_ 1814159121682792448
spelling 20212021-02-05T14:57:33Z2021-02-05T14:57:33Z20893191http://hdl.handle.net/11407/588810.11591/eei.v10i1.2710This paper aims to model a consumer goods cross-docking problem, which is solved using metaheuristics to minimize makespan and determine the capacity in terms of inbound and outbound docks. The consumer-goods cross-docking problem is represented through inbound and outbound docks, customer orders (products to be delivered to customers), and metaheuristics as a solution method. Simulated annealing (SA) and particle swarm optimization (PSO) are implemented to solve the cross-docking problem. Based on the results of statistical analysis, it was identified that the two-way interaction effect between inbound and outbound docks, outbound docks and items, and items and metaheuristics are the most statistically significant on the response variable. The best solution provides the minimum makespan of 973.42 minutes considering nine inbound docks and twelve outbound docks. However, this study detected that the combination of six inbound docks and nine outbound docks represents the most efficient solution for a crossdocking design since it reduces the requirement of docks by 28.6% and increases the makespan by only 4.2% when compared to the best solution, representing a favorable trade-off for the cross-docking platform design. © 2021, Institute of Advanced Engineering and Science. All rights reserved.engInstitute of Advanced Engineering and ScienceAdministración de EmpresasFacultad de Ciencias Económicas y Administrativashttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85095776438&doi=10.11591%2feei.v10i1.2710&partnerID=40&md5=8dd0205401ca0b07f630ae46a6e6251e101524532Chandriah, K. K., Raghavendra, N. V, Multi-objective optimization for preemptive and predictive supply chain operation (2020) Int. J. Electr. Comput. Eng, 10 (2), pp. 1533-1543Mezouar, H., El Afia, A., Proposal for an approach to evaluate continuity in service supply chains: case of the Moroccan electricity supply chain (2019) Int. J. Electr. Comput. Eng, 9 (6), pp. 5552-5559Rijal, A., Bijvank, M., De Koster, R., Integrated scheduling and assignment of trucks at unit-load cross-dock terminals with mixed service mode dock doors (2019) Eur. J. Oper. Res, 278 (3), pp. 752-771Monaco, M. F., Sammarra, M., Managing loading and discharging operations at cross-docking terminals (2020) Procedia Manuf, 42, pp. 475-482Gaudioso, M., Flavia, M., Sammarra, M., A Lagrangian heuristics for the truck scheduling problem in multidoor, multi-product Cross-Docking with constant processing time (2020) Omega, , pressNogueira, T. H., Coutinho, F. P., Ribeiro, R. P., Ravetti, M. G., Parallel-machine scheduling methodology for a multi-dock truck sequencing problem in a cross-docking center (2020) Comput. Ind. Eng, 143, p. 106391. , MarchFonseca, G. B., Nogueira, T. H., Ravetti, M. G., A hybrid Lagrangian metaheuristic for the cross-docking flow shop scheduling problem (2019) Eur. J. Oper. Res, 275 (1), pp. 139-154Assadi, M. T., Bagheri, M., Engineering differential evolution and population-based simulated annealing for truck scheduling problem in multiple door cross-docking systems (2016) Comput. Ind. Eng, 96, pp. 149-161Nikolopoulou, A. I., Repoussis, P. P., Tarantilis, C. D., Zachariadis, E. E., Moving products between location pairs: Cross-docking versus (2017) Eur. J. Oper. Res, 256 (3), pp. 803-819Wisittipanich, W., Hengmeechai, P., Truck scheduling in multi-door cross docking terminal by modified particle swarm optimization (2017) Comput. Ind. Eng, 113, pp. 793-802Goodarzi, A. H., Zegordi, S. H., A location-routing problem for cross-docking networks: A biogeography-based optimization algorithm (2016) Comput. Ind. Eng, 102, pp. 132-146Maknoon, M. Y., Soumis, F., Baptiste, P., Optimizing transshipment workloads in less-than-truckload (2016) Intern. J. Prod. Econ, 179, pp. 90-100Kusolpuchong, S., Chusap, K., Alhawari, O., Suer, G., A Genetic Algorithm Approach for Multi Objective Cross Dock Scheduling in Supply Chains (2019) Procedia Manuf, 39, pp. 1139-1148Aziz, M. A., Ninggal, I. H., Scalable workflow scheduling algorithm for minimizing makespan and failure probability (2019) Bull. Electr. Eng. Informatics, 8 (1), pp. 283-290Golshahi-roudbaneh, A., Hajiaghaei-keshteli, M., Paydar, M. M., Developing a lower bound and strong heuristics for a truck scheduling problem in a cross-docking center (2017) Knowl.-Based Syst, 129, pp. 17-38Correa, A. A., Gómez, R. A., Cano, J. A., Warehouse management and information and communication technology (2010) Estud. Gerenciales, 26 (117), pp. 145-171Van Belle, J., Valckenaers, P., Cattrysse, D., Cross-docking: State of the art (2012) Omega, 40 (6), pp. 827-846Cano, J. A., Order picking optimization based on a picker routing heuristic: minimizing total traveled distance in warehouses (2020) Handbook of Research on the Applications of International Transportation and Logistics for World Trade, pp. 74-96. , G. Ç. Ceyhun, Ed. PA, USA: IGI GlobalCano, J. A., Baena, J. J., Trends in the use of information and communication technologies for international negotiation (2015) Estud. Gerenciales, 31 (136), pp. 335-346Cano, J. A., Baena, J. J., Impact of information and communication technologies in international negotiation performance (2015) Revista Brasileira de Gestão de Negócios, 17 (54), pp. 751-768Amini, A., Tavakkoli-moghaddam, R., Omidvar, A., Cross-docking truck scheduling with the arrival times for inbound trucks and the learning effect for unloading/loading processes (2014) Prod. Manuf. Res, 2 (1), pp. 784-804Gelareh, S., Glover, F., Guemri, O., Hanafi, S., Nduwayo, P., Todosijevic, R., A comparative study of formulations for a cross-dock door assignment problem (2020) Omega, 91, p. 102015Cano, J. A., Formulations for joint order picking problems in low-level picker-to-part systems (2020) Bull. Electr. Eng. Informatics, 9 (2), pp. 836-844Cano, J. A., Correa-Espinal, A. A., Gómez-Montoya, R. A., Mathematical programming modeling for joint order batching, sequencing and picker routing problems in manual order picking systems (2019) J. King Saud Univ.-Eng. Sci, 32 (3), pp. 219-228Baizal, Z. K. A., Lhaksmana, K. M., Rahmawati, A. A., Kirom, M., Mubarok, Z., Travel route scheduling based on user’s preferences using simulated annealing (2019) Int. J. Electr. Comput. Eng, 9 (2), pp. 1275-1287Shahmardan, A., Sajadieh, M. S., Truck scheduling in a multi-door cross-docking center with partial unloading – Reinforcement learning-based simulated annealing approaches (2020) Comput. Ind. Eng, 139, p. 106134Mousavi, S. M., Tavakkoli-Moghaddam, R., Siadat, A., Optimal design of the cross-docking in distribution networks: Heuristic solution approach (2014) Int. J. Eng. Trans. A Basics, 27 (4), pp. 533-544Abdul-Adheem, W. R., An enhanced particle swarm optimization algorithm (2019) Int. J. Electr. Comput. Eng, 9 (6), pp. 4904-4907Shin, T. M., Adam, A., Abidin, A. F. Z., A comparative study of PSO, GSA and SCA in parameters optimization of surface grinding process (2019) Bull. Electr. Eng. Informatics, 8 (3), pp. 1117-1127Montgomery, D. C., (2019) Design and analysis of experiments, , 10th ed. Massachusetts: John Wiley & Sons IncBulletin of Electrical Engineering and InformaticsConsumer goods sectorCross-dockingDistribution centerParticle swarm optimizationSimulated annealingImproving cross-docking operations for consumer goods sector using metaheuristicsArticleinfo:eu-repo/semantics/articlehttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Gómez-Montoya, R.A., Faculty of Engineering, Politécnico Colombiano Jaime Isaza Cadavid, ColombiaCano, J.A., Faculty of Economics and Administrative Sciences, Universidad de Medellín, ColombiaCampo, E.A., ESACS-Escuela Superior en Administración de Cadena de Suministro, ColombiaSalazar, F., Faculty of Economics and Administrative Sciences, Pontificia Universidad Javeriana, Colombiahttp://purl.org/coar/access_right/c_16ecGómez-Montoya R.A.Cano J.A.Campo E.A.Salazar F.11407/5888oai:repository.udem.edu.co:11407/58882021-02-05 09:57:33.9Repositorio Institucional Universidad de Medellinrepositorio@udem.edu.co

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