TY - GEN
T1 - Driver Analysis to Solve Dynamic Facility Layout Problems
T2 - 32nd International Conference on Flexible Automation and Intelligent Manufacturing, FAIM 2023
AU - Sotamba, Luis Miguel
AU - Peña, Mario
AU - Siguenza-Guzman, Lorena
N1 - Publisher Copyright:
© 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2024
Y1 - 2024
N2 - The Dynamic Facility Layout Problem (DFLP) is concerned with finding an optimal facility design considering changes in the planning horizon. Since DFLP belongs to the non-polynomial hard class problem, different solutions have been used to find an optimal solution. However, a correct performance evaluation is needed to validate and compare the results with others. This performance evaluation refers to using both statistical and computational tests. When searching the literature on related papers, none consider these tests. The lack of such information and the constant evolution of algorithms motivated this work. The current document reviews the solution methodologies applied to solve DFLP and the manner the performance evaluation is done. In addition, the methods used to mix solution methodologies, called hybrid approaches, are included. This work was carried out using the Barbara Kitchenham methodology, in which studies from 2015 to 2022 were considered. A sample of 59 articles was analyzed, all about DFLP. As a result, this study identified two commonly used categories when solving DFLPs: hybrid and metaheuristic approaches. Furthermore, performance evaluation is done using different statistical methods in some cases, comparisons of some numerical results obtained from the algorithm output, and studies without comparisons. Finally, the results do not find any instances in which a methodology is applied to compose the algorithm when a hybrid approach is used. To the best of our knowledge, this work is the first in which performance evaluation is considered.
AB - The Dynamic Facility Layout Problem (DFLP) is concerned with finding an optimal facility design considering changes in the planning horizon. Since DFLP belongs to the non-polynomial hard class problem, different solutions have been used to find an optimal solution. However, a correct performance evaluation is needed to validate and compare the results with others. This performance evaluation refers to using both statistical and computational tests. When searching the literature on related papers, none consider these tests. The lack of such information and the constant evolution of algorithms motivated this work. The current document reviews the solution methodologies applied to solve DFLP and the manner the performance evaluation is done. In addition, the methods used to mix solution methodologies, called hybrid approaches, are included. This work was carried out using the Barbara Kitchenham methodology, in which studies from 2015 to 2022 were considered. A sample of 59 articles was analyzed, all about DFLP. As a result, this study identified two commonly used categories when solving DFLPs: hybrid and metaheuristic approaches. Furthermore, performance evaluation is done using different statistical methods in some cases, comparisons of some numerical results obtained from the algorithm output, and studies without comparisons. Finally, the results do not find any instances in which a methodology is applied to compose the algorithm when a hybrid approach is used. To the best of our knowledge, this work is the first in which performance evaluation is considered.
KW - Dynamic Facility Layout Problem
KW - Heuristics
KW - Hybrid methods
KW - Literature Review
KW - Metaheuristics
KW - Optimization
UR - https://www.scopus.com/pages/publications/85172734513
U2 - 10.1007/978-3-031-38165-2_29
DO - 10.1007/978-3-031-38165-2_29
M3 - Contribución a la conferencia
AN - SCOPUS:85172734513
SN - 9783031381645
T3 - Lecture Notes in Mechanical Engineering
SP - 242
EP - 249
BT - Flexible Automation and Intelligent Manufacturing
A2 - Silva, Francisco J.
A2 - Ferreira, Luís Pinto
A2 - Sá, José Carlos
A2 - Pereira, Maria Teresa
A2 - Pinto, Carla M.A.
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 18 June 2023 through 22 June 2023
ER -