A Hybrid Estimation of Distribution Algorithm for the Quay Crane Scheduling Problem
The aim of the quay crane scheduling problem (QCSP) is to identify the best sequence of discharging and loading operations for a set of quay cranes. This problem is solved with a new hybrid estimation of distribution algorithm (EDA). The approach is proposed to tackle the drawbacks of the EDAs, i.e....
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-09-01
|
| Series: | Mathematical and Computational Applications |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2297-8747/26/3/64 |
| _version_ | 1797518310746619904 |
|---|---|
| author | Ricardo Pérez-Rodríguez |
| author_facet | Ricardo Pérez-Rodríguez |
| author_sort | Ricardo Pérez-Rodríguez |
| collection | DOAJ |
| description | The aim of the quay crane scheduling problem (QCSP) is to identify the best sequence of discharging and loading operations for a set of quay cranes. This problem is solved with a new hybrid estimation of distribution algorithm (EDA). The approach is proposed to tackle the drawbacks of the EDAs, i.e., the lack of diversity of solutions and poor ability of exploitation. The hybridization approach, used in this investigation, uses a distance based ranking model and the moth-flame algorithm. The distance based ranking model is in charge of modelling the solution space distribution, through an exponential function, by measuring the distance between solutions; meanwhile, the heuristic moth-flame determines who would be the offspring, with a spiral function that identifies the new locations for the new solutions. Based on the results, the proposed scheme, called QCEDA, works to enhance the performance of those other EDAs that use complex probability models. The dispersion results of the QCEDA scheme are less than the other algorithms used in the comparison section. This means that the solutions found by the QCEDA are more concentrated around the best value than other algorithms, i.e., the average of the solutions of the QCEDA converges better than other approaches to the best found value. Finally, as a conclusion, the hybrid EDAs have a better performance, or equal in effectiveness, than the so called pure EDAs. |
| first_indexed | 2024-03-10T07:28:05Z |
| format | Article |
| id | doaj.art-7e2edc38331a46369086ef681f90e4a2 |
| institution | Directory Open Access Journal |
| issn | 1300-686X 2297-8747 |
| language | English |
| last_indexed | 2024-03-10T07:28:05Z |
| publishDate | 2021-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematical and Computational Applications |
| spelling | doaj.art-7e2edc38331a46369086ef681f90e4a22023-11-22T14:07:17ZengMDPI AGMathematical and Computational Applications1300-686X2297-87472021-09-012636410.3390/mca26030064A Hybrid Estimation of Distribution Algorithm for the Quay Crane Scheduling ProblemRicardo Pérez-Rodríguez0Circuito Universitario, Faculty of Engineering, CONACYT—UAQ, Autonomous University of Queretaro, Cerro de las Campanas s/n, Santiago de Queretaro 76010, MexicoThe aim of the quay crane scheduling problem (QCSP) is to identify the best sequence of discharging and loading operations for a set of quay cranes. This problem is solved with a new hybrid estimation of distribution algorithm (EDA). The approach is proposed to tackle the drawbacks of the EDAs, i.e., the lack of diversity of solutions and poor ability of exploitation. The hybridization approach, used in this investigation, uses a distance based ranking model and the moth-flame algorithm. The distance based ranking model is in charge of modelling the solution space distribution, through an exponential function, by measuring the distance between solutions; meanwhile, the heuristic moth-flame determines who would be the offspring, with a spiral function that identifies the new locations for the new solutions. Based on the results, the proposed scheme, called QCEDA, works to enhance the performance of those other EDAs that use complex probability models. The dispersion results of the QCEDA scheme are less than the other algorithms used in the comparison section. This means that the solutions found by the QCEDA are more concentrated around the best value than other algorithms, i.e., the average of the solutions of the QCEDA converges better than other approaches to the best found value. Finally, as a conclusion, the hybrid EDAs have a better performance, or equal in effectiveness, than the so called pure EDAs.https://www.mdpi.com/2297-8747/26/3/64estimation of distribution algorithmMallows modelmoth-flame algorithmjob shop scheduling problemquay crane scheduling problem |
| spellingShingle | Ricardo Pérez-Rodríguez A Hybrid Estimation of Distribution Algorithm for the Quay Crane Scheduling Problem Mathematical and Computational Applications estimation of distribution algorithm Mallows model moth-flame algorithm job shop scheduling problem quay crane scheduling problem |
| title | A Hybrid Estimation of Distribution Algorithm for the Quay Crane Scheduling Problem |
| title_full | A Hybrid Estimation of Distribution Algorithm for the Quay Crane Scheduling Problem |
| title_fullStr | A Hybrid Estimation of Distribution Algorithm for the Quay Crane Scheduling Problem |
| title_full_unstemmed | A Hybrid Estimation of Distribution Algorithm for the Quay Crane Scheduling Problem |
| title_short | A Hybrid Estimation of Distribution Algorithm for the Quay Crane Scheduling Problem |
| title_sort | hybrid estimation of distribution algorithm for the quay crane scheduling problem |
| topic | estimation of distribution algorithm Mallows model moth-flame algorithm job shop scheduling problem quay crane scheduling problem |
| url | https://www.mdpi.com/2297-8747/26/3/64 |
| work_keys_str_mv | AT ricardoperezrodriguez ahybridestimationofdistributionalgorithmforthequaycraneschedulingproblem AT ricardoperezrodriguez hybridestimationofdistributionalgorithmforthequaycraneschedulingproblem |