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Multi-level fleet size optimization for containers handling using double-cycling strategy
•Optimization of fleet size combination in containers terminal is presented.•The optimization minimizes both vessel turnaround time and handling cost.•A new double-cycling handling strategy is incorporated in the optimization process.•The new strategy saved 20% in both time and cost and increased pr...
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Published in: | Expert systems with applications 2021-05, Vol.170, p.114526, Article 114526 |
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description | •Optimization of fleet size combination in containers terminal is presented.•The optimization minimizes both vessel turnaround time and handling cost.•A new double-cycling handling strategy is incorporated in the optimization process.•The new strategy saved 20% in both time and cost and increased productivity by 25%
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. |
doi_str_mv | 10.1016/j.eswa.2020.114526 |
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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.</description><identifier>ISSN: 0957-4174</identifier><identifier>EISSN: 1873-6793</identifier><identifier>DOI: 10.1016/j.eswa.2020.114526</identifier><language>eng</language><publisher>New York: Elsevier Ltd</publisher><subject>Container Handling ; Containers ; Cycles ; Double-Cycling ; Fleet Size ; Genetic algorithms ; Handling ; Handling equipment ; Multi-level Optimization ; NSGA-II ; Optimization ; Productivity ; Sorting algorithms ; Vessels</subject><ispartof>Expert systems with applications, 2021-05, Vol.170, p.114526, Article 114526</ispartof><rights>2020 Elsevier Ltd</rights><rights>Copyright Elsevier BV May 15, 2021</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c328t-ee781340ca6f7d703217e0730b6cdd5248e150218565597f45ae0215657df1693</citedby><cites>FETCH-LOGICAL-c328t-ee781340ca6f7d703217e0730b6cdd5248e150218565597f45ae0215657df1693</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids></links><search><creatorcontrib>El-Abbasy, Mohammed Saeed</creatorcontrib><creatorcontrib>Ahmed, Essmeil</creatorcontrib><creatorcontrib>Zayed, Tarek</creatorcontrib><creatorcontrib>Alfalah, Ghasan</creatorcontrib><creatorcontrib>Alkass, Sabah</creatorcontrib><title>Multi-level fleet size optimization for containers handling using double-cycling strategy</title><title>Expert systems with applications</title><description>•Optimization of fleet size combination in containers terminal is presented.•The optimization minimizes both vessel turnaround time and handling cost.•A new double-cycling handling strategy is incorporated in the optimization process.•The new strategy saved 20% in both time and cost and increased productivity by 25%
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.</description><subject>Container Handling</subject><subject>Containers</subject><subject>Cycles</subject><subject>Double-Cycling</subject><subject>Fleet Size</subject><subject>Genetic algorithms</subject><subject>Handling</subject><subject>Handling equipment</subject><subject>Multi-level Optimization</subject><subject>NSGA-II</subject><subject>Optimization</subject><subject>Productivity</subject><subject>Sorting algorithms</subject><subject>Vessels</subject><issn>0957-4174</issn><issn>1873-6793</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><recordid>eNp9kD9PwzAQxS0EEqXwBZgsMafYThw7Eguq-CcVscDAZLn2pThK42I7oPbTkxBmljvd03t3px9Cl5QsKKHldbOA-K0XjLBBoAVn5RGaUSnyrBRVfoxmpOIiK6goTtFZjA0hVBAiZuj9uW-Ty1r4ghbXLUDC0R0A-11yW3fQyfkO1z5g47ukXQch4g_d2dZ1G9zHsVrfr1vIzN78ijEFnWCzP0cntW4jXPz1OXq7v3tdPmarl4en5e0qMzmTKQMQkuYFMbqshRUkZ1QAETlZl8ZazgoJlBNGJS85r0RdcA3DOEzC1rSs8jm6mvbugv_sISbV-D50w0nFOJFcSl7xwcUmlwk-xgC12gW31WGvKFEjQtWoEaEaEaoJ4RC6mUIw_P_lIKhoHHQGrAtgkrLe_Rf_ASKxegs</recordid><startdate>20210515</startdate><enddate>20210515</enddate><creator>El-Abbasy, Mohammed Saeed</creator><creator>Ahmed, Essmeil</creator><creator>Zayed, Tarek</creator><creator>Alfalah, Ghasan</creator><creator>Alkass, Sabah</creator><general>Elsevier Ltd</general><general>Elsevier BV</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>20210515</creationdate><title>Multi-level fleet size optimization for containers handling using double-cycling strategy</title><author>El-Abbasy, Mohammed Saeed ; Ahmed, Essmeil ; Zayed, Tarek ; Alfalah, Ghasan ; Alkass, Sabah</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c328t-ee781340ca6f7d703217e0730b6cdd5248e150218565597f45ae0215657df1693</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Container Handling</topic><topic>Containers</topic><topic>Cycles</topic><topic>Double-Cycling</topic><topic>Fleet Size</topic><topic>Genetic algorithms</topic><topic>Handling</topic><topic>Handling equipment</topic><topic>Multi-level Optimization</topic><topic>NSGA-II</topic><topic>Optimization</topic><topic>Productivity</topic><topic>Sorting algorithms</topic><topic>Vessels</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>El-Abbasy, Mohammed Saeed</creatorcontrib><creatorcontrib>Ahmed, Essmeil</creatorcontrib><creatorcontrib>Zayed, Tarek</creatorcontrib><creatorcontrib>Alfalah, Ghasan</creatorcontrib><creatorcontrib>Alkass, Sabah</creatorcontrib><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>Expert systems with applications</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>El-Abbasy, Mohammed Saeed</au><au>Ahmed, Essmeil</au><au>Zayed, Tarek</au><au>Alfalah, Ghasan</au><au>Alkass, Sabah</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Multi-level fleet size optimization for containers handling using double-cycling strategy</atitle><jtitle>Expert systems with applications</jtitle><date>2021-05-15</date><risdate>2021</risdate><volume>170</volume><spage>114526</spage><pages>114526-</pages><artnum>114526</artnum><issn>0957-4174</issn><eissn>1873-6793</eissn><abstract>•Optimization of fleet size combination in containers terminal is presented.•The optimization minimizes both vessel turnaround time and handling cost.•A new double-cycling handling strategy is incorporated in the optimization process.•The new strategy saved 20% in both time and cost and increased productivity by 25%
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.</abstract><cop>New York</cop><pub>Elsevier Ltd</pub><doi>10.1016/j.eswa.2020.114526</doi></addata></record> |
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subjects | Container Handling Containers Cycles Double-Cycling Fleet Size Genetic algorithms Handling Handling equipment Multi-level Optimization NSGA-II Optimization Productivity Sorting algorithms Vessels |
title | Multi-level fleet size optimization for containers handling using double-cycling strategy |
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