Loading…
An Enhanced NSGA-II for Solving Berth Allocation and Quay Crane Assignment Problem With Stochastic Arrival Times
The berth allocation and quay crane assignment problem (BACAP) is an important port operation planning problem. To obtain an effective and reliable schedule of berth and quay crane (QC), this study addresses the BACAP with stochastic arrival times of vessels. An efficient method combining scenario g...
Saved in:
| Published in: | IEEE transactions on intelligent transportation systems 2023-01, Vol.24 (1), p.459-473 |
|---|---|
| Main Authors: | , , |
| Format: | Article |
| Language: | English |
| Subjects: | |
| Citations: | Items that this one cites Items that cite this one |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| cited_by | cdi_FETCH-LOGICAL-c293t-321654828d3438fe9cc2260493119524aae2c3f7792946406a61fbf213fab3173 |
|---|---|
| cites | cdi_FETCH-LOGICAL-c293t-321654828d3438fe9cc2260493119524aae2c3f7792946406a61fbf213fab3173 |
| container_end_page | 473 |
| container_issue | 1 |
| container_start_page | 459 |
| container_title | IEEE transactions on intelligent transportation systems |
| container_volume | 24 |
| creator | Ji, Bin Huang, Han Yu, Samson S. |
| description | The berth allocation and quay crane assignment problem (BACAP) is an important port operation planning problem. To obtain an effective and reliable schedule of berth and quay crane (QC), this study addresses the BACAP with stochastic arrival times of vessels. An efficient method combining scenario generation is presented to simulate the stochastic arrival times. After then, a mixed integer linear programming (MILP) model is established, aiming to minimize the expectation of the vessels' total stay time in port. A multi-objective constraint-handling (MOCH) strategy is adopted to reformulate the developed model, which converts constraint violations into an objective, thus transforming the single-objective optimization model with complex constraints into a dual-objective optimization model with only easy-handling constraints. Then an enhanced non-dominated sorting genetic algorithm II (ENSGA-II) is proposed to solve the dual-objective model, in which a neighborhood search algorithm and a search bias mechanism are incorporated to strengthen the local exploitation capability. Furthermore, a repair method (RM), penalty function (PF) and the superiority of feasible solutions (SF) strategy for constraint handling are designed respectively and incorporated with genetic algorithm to solve the original single-objective optimization model. Finally, numerical experiments on instances in the literature are conducted to validate the effectiveness of the MOCH and the proposed ENSGA-II. The results show that the average total stay time of vessels is reduced when stochastic arrival times are considered. Comparison results with another two multi-objective methods and three single-objective methods combined with different constraint-handling strategies corroborate the superiority of the proposed ENSGA-II and MOCH. |
| doi_str_mv | 10.1109/TITS.2022.3213834 |
| format | article |
| fullrecord | <record><control><sourceid>proquest_ieee_</sourceid><recordid>TN_cdi_proquest_journals_2770779683</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>9929279</ieee_id><sourcerecordid>2770779683</sourcerecordid><originalsourceid>FETCH-LOGICAL-c293t-321654828d3438fe9cc2260493119524aae2c3f7792946406a61fbf213fab3173</originalsourceid><addsrcrecordid>eNo9kFtLAzEQhRdRsFZ_gPgS8HlrbnvJ41pqXShe2BUfQ5ombco2qcm20H9vlopPMwznm5lzkuQewQlCkD21ddtMMMR4QjAiJaEXyQhlWZlCiPLLocc0ZTCD18lNCNs4pRlCo2RfWTCzG2GlWoG3Zl6ldQ2086Bx3dHYNXhWvt-AquucFL1xFgi7Ap8HcQJTL6wCVQhmbXfK9uDDu2WnduDbRKLpndyI0BsJKu_NUXSgNTsVbpMrLbqg7v7qOPl6mbXT13TxPq-n1SKVmJE-jS7yjJa4XBFKSq2YlBjnkDKCEItehFBYEl0UDDOaU5iLHOmljt61WBJUkHHyeN679-7noELPt-7gbTzJcVHACOYliSp0VknvQvBK8703O-FPHEE-BMuHYPkQLP8LNjIPZ8Yopf71LD6CC0Z-AYmCclk</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2770779683</pqid></control><display><type>article</type><title>An Enhanced NSGA-II for Solving Berth Allocation and Quay Crane Assignment Problem With Stochastic Arrival Times</title><source>IEEE Electronic Library (IEL) Journals</source><creator>Ji, Bin ; Huang, Han ; Yu, Samson S.</creator><creatorcontrib>Ji, Bin ; Huang, Han ; Yu, Samson S.</creatorcontrib><description>The berth allocation and quay crane assignment problem (BACAP) is an important port operation planning problem. To obtain an effective and reliable schedule of berth and quay crane (QC), this study addresses the BACAP with stochastic arrival times of vessels. An efficient method combining scenario generation is presented to simulate the stochastic arrival times. After then, a mixed integer linear programming (MILP) model is established, aiming to minimize the expectation of the vessels' total stay time in port. A multi-objective constraint-handling (MOCH) strategy is adopted to reformulate the developed model, which converts constraint violations into an objective, thus transforming the single-objective optimization model with complex constraints into a dual-objective optimization model with only easy-handling constraints. Then an enhanced non-dominated sorting genetic algorithm II (ENSGA-II) is proposed to solve the dual-objective model, in which a neighborhood search algorithm and a search bias mechanism are incorporated to strengthen the local exploitation capability. Furthermore, a repair method (RM), penalty function (PF) and the superiority of feasible solutions (SF) strategy for constraint handling are designed respectively and incorporated with genetic algorithm to solve the original single-objective optimization model. Finally, numerical experiments on instances in the literature are conducted to validate the effectiveness of the MOCH and the proposed ENSGA-II. The results show that the average total stay time of vessels is reduced when stochastic arrival times are considered. Comparison results with another two multi-objective methods and three single-objective methods combined with different constraint-handling strategies corroborate the superiority of the proposed ENSGA-II and MOCH.</description><identifier>ISSN: 1524-9050</identifier><identifier>EISSN: 1558-0016</identifier><identifier>DOI: 10.1109/TITS.2022.3213834</identifier><identifier>CODEN: ITISFG</identifier><language>eng</language><publisher>New York: IEEE</publisher><subject>Assignment problem ; Berth allocation and quay crane assignment ; Constraint modelling ; Costs ; Cranes ; Genetic algorithms ; Handling ; Heuristic algorithms ; Integer programming ; Linear programming ; Mixed integer ; multi-objective constraint handling ; Operations research ; Optimization ; Optimization models ; Penalty function ; Programming ; Quays ; Resource management ; scenario simulation ; Seaports ; Search algorithms ; Sorting algorithms ; Stochastic processes ; stochastic programming ; Strategy ; Vessels</subject><ispartof>IEEE transactions on intelligent transportation systems, 2023-01, Vol.24 (1), p.459-473</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2023</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c293t-321654828d3438fe9cc2260493119524aae2c3f7792946406a61fbf213fab3173</citedby><cites>FETCH-LOGICAL-c293t-321654828d3438fe9cc2260493119524aae2c3f7792946406a61fbf213fab3173</cites><orcidid>0000-0003-4713-4391 ; 0000-0002-2983-7344 ; 0000-0003-3452-8308</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/9929279$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>315,786,790,27957,27958,55147</link.rule.ids></links><search><creatorcontrib>Ji, Bin</creatorcontrib><creatorcontrib>Huang, Han</creatorcontrib><creatorcontrib>Yu, Samson S.</creatorcontrib><title>An Enhanced NSGA-II for Solving Berth Allocation and Quay Crane Assignment Problem With Stochastic Arrival Times</title><title>IEEE transactions on intelligent transportation systems</title><addtitle>TITS</addtitle><description>The berth allocation and quay crane assignment problem (BACAP) is an important port operation planning problem. To obtain an effective and reliable schedule of berth and quay crane (QC), this study addresses the BACAP with stochastic arrival times of vessels. An efficient method combining scenario generation is presented to simulate the stochastic arrival times. After then, a mixed integer linear programming (MILP) model is established, aiming to minimize the expectation of the vessels' total stay time in port. A multi-objective constraint-handling (MOCH) strategy is adopted to reformulate the developed model, which converts constraint violations into an objective, thus transforming the single-objective optimization model with complex constraints into a dual-objective optimization model with only easy-handling constraints. Then an enhanced non-dominated sorting genetic algorithm II (ENSGA-II) is proposed to solve the dual-objective model, in which a neighborhood search algorithm and a search bias mechanism are incorporated to strengthen the local exploitation capability. Furthermore, a repair method (RM), penalty function (PF) and the superiority of feasible solutions (SF) strategy for constraint handling are designed respectively and incorporated with genetic algorithm to solve the original single-objective optimization model. Finally, numerical experiments on instances in the literature are conducted to validate the effectiveness of the MOCH and the proposed ENSGA-II. The results show that the average total stay time of vessels is reduced when stochastic arrival times are considered. Comparison results with another two multi-objective methods and three single-objective methods combined with different constraint-handling strategies corroborate the superiority of the proposed ENSGA-II and MOCH.</description><subject>Assignment problem</subject><subject>Berth allocation and quay crane assignment</subject><subject>Constraint modelling</subject><subject>Costs</subject><subject>Cranes</subject><subject>Genetic algorithms</subject><subject>Handling</subject><subject>Heuristic algorithms</subject><subject>Integer programming</subject><subject>Linear programming</subject><subject>Mixed integer</subject><subject>multi-objective constraint handling</subject><subject>Operations research</subject><subject>Optimization</subject><subject>Optimization models</subject><subject>Penalty function</subject><subject>Programming</subject><subject>Quays</subject><subject>Resource management</subject><subject>scenario simulation</subject><subject>Seaports</subject><subject>Search algorithms</subject><subject>Sorting algorithms</subject><subject>Stochastic processes</subject><subject>stochastic programming</subject><subject>Strategy</subject><subject>Vessels</subject><issn>1524-9050</issn><issn>1558-0016</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><recordid>eNo9kFtLAzEQhRdRsFZ_gPgS8HlrbnvJ41pqXShe2BUfQ5ombco2qcm20H9vlopPMwznm5lzkuQewQlCkD21ddtMMMR4QjAiJaEXyQhlWZlCiPLLocc0ZTCD18lNCNs4pRlCo2RfWTCzG2GlWoG3Zl6ldQ2086Bx3dHYNXhWvt-AquucFL1xFgi7Ap8HcQJTL6wCVQhmbXfK9uDDu2WnduDbRKLpndyI0BsJKu_NUXSgNTsVbpMrLbqg7v7qOPl6mbXT13TxPq-n1SKVmJE-jS7yjJa4XBFKSq2YlBjnkDKCEItehFBYEl0UDDOaU5iLHOmljt61WBJUkHHyeN679-7noELPt-7gbTzJcVHACOYliSp0VknvQvBK8703O-FPHEE-BMuHYPkQLP8LNjIPZ8Yopf71LD6CC0Z-AYmCclk</recordid><startdate>202301</startdate><enddate>202301</enddate><creator>Ji, Bin</creator><creator>Huang, Han</creator><creator>Yu, Samson S.</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>8FD</scope><scope>FR3</scope><scope>JQ2</scope><scope>KR7</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><orcidid>https://orcid.org/0000-0003-4713-4391</orcidid><orcidid>https://orcid.org/0000-0002-2983-7344</orcidid><orcidid>https://orcid.org/0000-0003-3452-8308</orcidid></search><sort><creationdate>202301</creationdate><title>An Enhanced NSGA-II for Solving Berth Allocation and Quay Crane Assignment Problem With Stochastic Arrival Times</title><author>Ji, Bin ; Huang, Han ; Yu, Samson S.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c293t-321654828d3438fe9cc2260493119524aae2c3f7792946406a61fbf213fab3173</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Assignment problem</topic><topic>Berth allocation and quay crane assignment</topic><topic>Constraint modelling</topic><topic>Costs</topic><topic>Cranes</topic><topic>Genetic algorithms</topic><topic>Handling</topic><topic>Heuristic algorithms</topic><topic>Integer programming</topic><topic>Linear programming</topic><topic>Mixed integer</topic><topic>multi-objective constraint handling</topic><topic>Operations research</topic><topic>Optimization</topic><topic>Optimization models</topic><topic>Penalty function</topic><topic>Programming</topic><topic>Quays</topic><topic>Resource management</topic><topic>scenario simulation</topic><topic>Seaports</topic><topic>Search algorithms</topic><topic>Sorting algorithms</topic><topic>Stochastic processes</topic><topic>stochastic programming</topic><topic>Strategy</topic><topic>Vessels</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Ji, Bin</creatorcontrib><creatorcontrib>Huang, Han</creatorcontrib><creatorcontrib>Yu, Samson S.</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Civil Engineering Abstracts</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>IEEE transactions on intelligent transportation systems</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Ji, Bin</au><au>Huang, Han</au><au>Yu, Samson S.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>An Enhanced NSGA-II for Solving Berth Allocation and Quay Crane Assignment Problem With Stochastic Arrival Times</atitle><jtitle>IEEE transactions on intelligent transportation systems</jtitle><stitle>TITS</stitle><date>2023-01</date><risdate>2023</risdate><volume>24</volume><issue>1</issue><spage>459</spage><epage>473</epage><pages>459-473</pages><issn>1524-9050</issn><eissn>1558-0016</eissn><coden>ITISFG</coden><abstract>The berth allocation and quay crane assignment problem (BACAP) is an important port operation planning problem. To obtain an effective and reliable schedule of berth and quay crane (QC), this study addresses the BACAP with stochastic arrival times of vessels. An efficient method combining scenario generation is presented to simulate the stochastic arrival times. After then, a mixed integer linear programming (MILP) model is established, aiming to minimize the expectation of the vessels' total stay time in port. A multi-objective constraint-handling (MOCH) strategy is adopted to reformulate the developed model, which converts constraint violations into an objective, thus transforming the single-objective optimization model with complex constraints into a dual-objective optimization model with only easy-handling constraints. Then an enhanced non-dominated sorting genetic algorithm II (ENSGA-II) is proposed to solve the dual-objective model, in which a neighborhood search algorithm and a search bias mechanism are incorporated to strengthen the local exploitation capability. Furthermore, a repair method (RM), penalty function (PF) and the superiority of feasible solutions (SF) strategy for constraint handling are designed respectively and incorporated with genetic algorithm to solve the original single-objective optimization model. Finally, numerical experiments on instances in the literature are conducted to validate the effectiveness of the MOCH and the proposed ENSGA-II. The results show that the average total stay time of vessels is reduced when stochastic arrival times are considered. Comparison results with another two multi-objective methods and three single-objective methods combined with different constraint-handling strategies corroborate the superiority of the proposed ENSGA-II and MOCH.</abstract><cop>New York</cop><pub>IEEE</pub><doi>10.1109/TITS.2022.3213834</doi><tpages>15</tpages><orcidid>https://orcid.org/0000-0003-4713-4391</orcidid><orcidid>https://orcid.org/0000-0002-2983-7344</orcidid><orcidid>https://orcid.org/0000-0003-3452-8308</orcidid></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1524-9050 |
| ispartof | IEEE transactions on intelligent transportation systems, 2023-01, Vol.24 (1), p.459-473 |
| issn | 1524-9050 1558-0016 |
| language | eng |
| recordid | cdi_proquest_journals_2770779683 |
| source | IEEE Electronic Library (IEL) Journals |
| subjects | Assignment problem Berth allocation and quay crane assignment Constraint modelling Costs Cranes Genetic algorithms Handling Heuristic algorithms Integer programming Linear programming Mixed integer multi-objective constraint handling Operations research Optimization Optimization models Penalty function Programming Quays Resource management scenario simulation Seaports Search algorithms Sorting algorithms Stochastic processes stochastic programming Strategy Vessels |
| title | An Enhanced NSGA-II for Solving Berth Allocation and Quay Crane Assignment Problem With Stochastic Arrival Times |
| url | http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-09-21T14%3A24%3A36IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_ieee_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=An%20Enhanced%20NSGA-II%20for%20Solving%20Berth%20Allocation%20and%20Quay%20Crane%20Assignment%20Problem%20With%20Stochastic%20Arrival%20Times&rft.jtitle=IEEE%20transactions%20on%20intelligent%20transportation%20systems&rft.au=Ji,%20Bin&rft.date=2023-01&rft.volume=24&rft.issue=1&rft.spage=459&rft.epage=473&rft.pages=459-473&rft.issn=1524-9050&rft.eissn=1558-0016&rft.coden=ITISFG&rft_id=info:doi/10.1109/TITS.2022.3213834&rft_dat=%3Cproquest_ieee_%3E2770779683%3C/proquest_ieee_%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c293t-321654828d3438fe9cc2260493119524aae2c3f7792946406a61fbf213fab3173%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=2770779683&rft_id=info:pmid/&rft_ieee_id=9929279&rfr_iscdi=true |