Optimizing the Allocation of Quay Cranes and Prime Movers for Container Handling Operations: A Data Envelopment Analysis Approach
Congestion arises due to the significant influx of containers and ships from various global locations, primarily resulting from inadequate resource allocation or inaccurate resource configuration. This paper examines the optimal configuration of quay cranes and prime movers required for a vessel to...
Published in: | Journal of Advanced Research in Applied Mechanics |
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Semarak Ilmu Publishing
2024
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2-s2.0-85200881302 Aznam N.H.Z.; Khurizan N.S.N.; Awang N.; Yusoff N.S.M.; Moftar M.F. Optimizing the Allocation of Quay Cranes and Prime Movers for Container Handling Operations: A Data Envelopment Analysis Approach 2024 Journal of Advanced Research in Applied Mechanics 119 1 10.37934/aram.119.1.145161 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85200881302&doi=10.37934%2faram.119.1.145161&partnerID=40&md5=7c47a0ad1eeff64e1d533126c02f74d4 Congestion arises due to the significant influx of containers and ships from various global locations, primarily resulting from inadequate resource allocation or inaccurate resource configuration. This paper examines the optimal configuration of quay cranes and prime movers required for a vessel to complete unloading and loading operations efficiently. Through simulation, the model generates performance metrics such as the average container waiting time, the average utilization of quay cranes and prime movers separately, and the number of containers handled within an observed time interval. The efficiency of handling equipment configurations is evaluated using the Charnes-Cooper-Rhodes Data Envelopment Analysis (CCR DEA) model and the Bi-Objective Multi-Criteria Data Envelopment Analysis (BiO-MCDEA). The optimal handling equipment configuration is then determined using the super-efficiency of both models. The model includes quay crane number, prime mover number and average container waiting time as inputs and the other three simulation performance measures and Gross Moves Per Hour (GMPH) for quay cranes and prime movers as outputs. Three quay cranes with fifteen prime movers are the best unloading and loading configuration for super-efficiency CCR DEA. Alternatively, super-efficiency BiO-MCDEA recommends two quay cranes with eighteen prime movers. The obtained results provide valuable insights for decision-makers to enhance terminal productivity and optimize handling equipment efficiency. © 2024, Semarak Ilmu Publishing. All rights reserved. Semarak Ilmu Publishing 22897895 English Article All Open Access; Gold Open Access |
author |
Aznam N.H.Z.; Khurizan N.S.N.; Awang N.; Yusoff N.S.M.; Moftar M.F. |
spellingShingle |
Aznam N.H.Z.; Khurizan N.S.N.; Awang N.; Yusoff N.S.M.; Moftar M.F. Optimizing the Allocation of Quay Cranes and Prime Movers for Container Handling Operations: A Data Envelopment Analysis Approach |
author_facet |
Aznam N.H.Z.; Khurizan N.S.N.; Awang N.; Yusoff N.S.M.; Moftar M.F. |
author_sort |
Aznam N.H.Z.; Khurizan N.S.N.; Awang N.; Yusoff N.S.M.; Moftar M.F. |
title |
Optimizing the Allocation of Quay Cranes and Prime Movers for Container Handling Operations: A Data Envelopment Analysis Approach |
title_short |
Optimizing the Allocation of Quay Cranes and Prime Movers for Container Handling Operations: A Data Envelopment Analysis Approach |
title_full |
Optimizing the Allocation of Quay Cranes and Prime Movers for Container Handling Operations: A Data Envelopment Analysis Approach |
title_fullStr |
Optimizing the Allocation of Quay Cranes and Prime Movers for Container Handling Operations: A Data Envelopment Analysis Approach |
title_full_unstemmed |
Optimizing the Allocation of Quay Cranes and Prime Movers for Container Handling Operations: A Data Envelopment Analysis Approach |
title_sort |
Optimizing the Allocation of Quay Cranes and Prime Movers for Container Handling Operations: A Data Envelopment Analysis Approach |
publishDate |
2024 |
container_title |
Journal of Advanced Research in Applied Mechanics |
container_volume |
119 |
container_issue |
1 |
doi_str_mv |
10.37934/aram.119.1.145161 |
url |
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85200881302&doi=10.37934%2faram.119.1.145161&partnerID=40&md5=7c47a0ad1eeff64e1d533126c02f74d4 |
description |
Congestion arises due to the significant influx of containers and ships from various global locations, primarily resulting from inadequate resource allocation or inaccurate resource configuration. This paper examines the optimal configuration of quay cranes and prime movers required for a vessel to complete unloading and loading operations efficiently. Through simulation, the model generates performance metrics such as the average container waiting time, the average utilization of quay cranes and prime movers separately, and the number of containers handled within an observed time interval. The efficiency of handling equipment configurations is evaluated using the Charnes-Cooper-Rhodes Data Envelopment Analysis (CCR DEA) model and the Bi-Objective Multi-Criteria Data Envelopment Analysis (BiO-MCDEA). The optimal handling equipment configuration is then determined using the super-efficiency of both models. The model includes quay crane number, prime mover number and average container waiting time as inputs and the other three simulation performance measures and Gross Moves Per Hour (GMPH) for quay cranes and prime movers as outputs. Three quay cranes with fifteen prime movers are the best unloading and loading configuration for super-efficiency CCR DEA. Alternatively, super-efficiency BiO-MCDEA recommends two quay cranes with eighteen prime movers. The obtained results provide valuable insights for decision-makers to enhance terminal productivity and optimize handling equipment efficiency. © 2024, Semarak Ilmu Publishing. All rights reserved. |
publisher |
Semarak Ilmu Publishing |
issn |
22897895 |
language |
English |
format |
Article |
accesstype |
All Open Access; Gold Open Access |
record_format |
scopus |
collection |
Scopus |
_version_ |
1809678469883232256 |