TY - JOUR
T1 - Multi-level fleet size optimization for containers handling using double-cycling strategy
AU - El-Abbasy, Mohammed Saeed
AU - Ahmed, Essmeil
AU - Zayed, Tarek
AU - Alfalah, Ghasan
AU - Alkass, Sabah
N1 - Publisher Copyright:
© 2020 Elsevier Ltd
PY - 2021/5/15
Y1 - 2021/5/15
N2 - Every few years, larger containerized vessels are introduced to the market to accommodate the increase in global trade. Although increasing the capacity of vessels results in maximizing the amount of imported and exported goods per voyage, yet it is accompanied with new challenges to terminal planners. One of the primary challenges is minimizing the vessel turnaround time with the least possible cost. In this context, this paper presents the development of a multi-level optimization model using the elitist non-dominated sorting genetic algorithm (NSGA-II) to determine the optimal or near-optimal fleet size combination of the different container handling equipment used in the terminal. The model aims to minimize two conflicting objective functions, namely, vessel turnaround time and total handling cost. Furthermore, the model considers a double-cycling strategy for the container handling process to achieve increased productivity and eventually more reduction in the vessel turnaround time. The model was implemented on a real-life case study to demonstrate its efficiency and the benefit of employing the double-cycling strategy compared with the traditional single-cycling strategy. The results demonstrated the efficiency of employing the double-cycling strategy by providing a reduction of above 20% in both the vessel turnaround time and the total handling cost and an increase of above 25% in the productivity when compared to the traditional single-cycling strategy.
AB - Every few years, larger containerized vessels are introduced to the market to accommodate the increase in global trade. Although increasing the capacity of vessels results in maximizing the amount of imported and exported goods per voyage, yet it is accompanied with new challenges to terminal planners. One of the primary challenges is minimizing the vessel turnaround time with the least possible cost. In this context, this paper presents the development of a multi-level optimization model using the elitist non-dominated sorting genetic algorithm (NSGA-II) to determine the optimal or near-optimal fleet size combination of the different container handling equipment used in the terminal. The model aims to minimize two conflicting objective functions, namely, vessel turnaround time and total handling cost. Furthermore, the model considers a double-cycling strategy for the container handling process to achieve increased productivity and eventually more reduction in the vessel turnaround time. The model was implemented on a real-life case study to demonstrate its efficiency and the benefit of employing the double-cycling strategy compared with the traditional single-cycling strategy. The results demonstrated the efficiency of employing the double-cycling strategy by providing a reduction of above 20% in both the vessel turnaround time and the total handling cost and an increase of above 25% in the productivity when compared to the traditional single-cycling strategy.
KW - Container Handling
KW - Double-Cycling
KW - Fleet Size
KW - Multi-level Optimization
KW - NSGA-II
UR - http://www.scopus.com/inward/record.url?scp=85098991055&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85098991055&partnerID=8YFLogxK
U2 - 10.1016/j.eswa.2020.114526
DO - 10.1016/j.eswa.2020.114526
M3 - Article
AN - SCOPUS:85098991055
SN - 0957-4174
VL - 170
JO - Expert Systems with Applications
JF - Expert Systems with Applications
M1 - 114526
ER -