A hybrid Genetic Algorithm for the Heterogeneous Dial-A-Ride Problem
•A new hybrid Genetic Algorithm for the Heterogeneous Dial-A-Ride Problem (H-DARP).•Efficient crossover operators and local search techniques.•Experiments on existing DARP and H-DARP instances and new, larger, H-DARP instances.•Computational experiments show the effectiveness of our algorithm compar...
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Veröffentlicht in: | Computers & operations research 2017-05, Vol.81, p.1-13 |
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creator | Masmoudi, Mohamed Amine Braekers, Kris Masmoudi, Malek Dammak, Abdelaziz |
description | •A new hybrid Genetic Algorithm for the Heterogeneous Dial-A-Ride Problem (H-DARP).•Efficient crossover operators and local search techniques.•Experiments on existing DARP and H-DARP instances and new, larger, H-DARP instances.•Computational experiments show the effectiveness of our algorithm compared to the current state-of-the-art algorithms.
This paper investigates the Heterogeneous Dial-A-Ride Problem (H-DARP) that consists of determining a vehicle route planning for heterogeneous users’ transportation with a heterogeneous fleet of vehicles. A hybrid Genetic Algorithm (GA) is proposed to solve the problem. Efficient construction heuristics, crossover operators and local search techniques, specifically tailored to the characteristics of the H-DARP, are provided. The proposed algorithm is tested on 92 benchmarks instances and 40 newly introduced larger instances. Computational experiments show the effectiveness of our approach compared to the current state-of-the-art algorithms for the DARP and H-DARP. When tested on the existing instances, we achieved average gaps of only 0.47% to the best-known solutions for the DARP, and 0.05% to the optimal solutions for the H-DARP, compared to 0.85% and 0.10%, respectively, obtained by the current state-of-the-art algorithms. For the 40 newly generated instances, average gaps of the hybrid GA are 0.35% smaller compared to the current state-of-the-art method. Besides, our method provides best results for 31 of these instances and ties with the existing method on 8 other instances. |
doi_str_mv | 10.1016/j.cor.2016.12.008 |
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This paper investigates the Heterogeneous Dial-A-Ride Problem (H-DARP) that consists of determining a vehicle route planning for heterogeneous users’ transportation with a heterogeneous fleet of vehicles. A hybrid Genetic Algorithm (GA) is proposed to solve the problem. Efficient construction heuristics, crossover operators and local search techniques, specifically tailored to the characteristics of the H-DARP, are provided. The proposed algorithm is tested on 92 benchmarks instances and 40 newly introduced larger instances. Computational experiments show the effectiveness of our approach compared to the current state-of-the-art algorithms for the DARP and H-DARP. When tested on the existing instances, we achieved average gaps of only 0.47% to the best-known solutions for the DARP, and 0.05% to the optimal solutions for the H-DARP, compared to 0.85% and 0.10%, respectively, obtained by the current state-of-the-art algorithms. For the 40 newly generated instances, average gaps of the hybrid GA are 0.35% smaller compared to the current state-of-the-art method. Besides, our method provides best results for 31 of these instances and ties with the existing method on 8 other instances.</description><identifier>ISSN: 0305-0548</identifier><identifier>EISSN: 1873-765X</identifier><identifier>EISSN: 0305-0548</identifier><identifier>DOI: 10.1016/j.cor.2016.12.008</identifier><identifier>CODEN: CMORAP</identifier><language>eng</language><publisher>New York: Elsevier Ltd</publisher><subject>Benchmarks ; Construction heuristics ; Genetic Algorithm (GA) ; Genetic algorithms ; Heterogeneous Dial-A-Ride Problem (H-DARP) ; Heuristic ; Hybrid algorithm ; Local Search (LS) ; Studies</subject><ispartof>Computers & operations research, 2017-05, Vol.81, p.1-13</ispartof><rights>2016 Elsevier Ltd</rights><rights>Copyright Pergamon Press Inc. May 2017</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c482t-a67997e1e075206da998cbe8bf32b26819ac709d8cc2aea03c331ec8e20cbaba3</citedby><cites>FETCH-LOGICAL-c482t-a67997e1e075206da998cbe8bf32b26819ac709d8cc2aea03c331ec8e20cbaba3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.cor.2016.12.008$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,780,784,3550,27924,27925,45995</link.rule.ids></links><search><creatorcontrib>Masmoudi, Mohamed Amine</creatorcontrib><creatorcontrib>Braekers, Kris</creatorcontrib><creatorcontrib>Masmoudi, Malek</creatorcontrib><creatorcontrib>Dammak, Abdelaziz</creatorcontrib><title>A hybrid Genetic Algorithm for the Heterogeneous Dial-A-Ride Problem</title><title>Computers & operations research</title><description>•A new hybrid Genetic Algorithm for the Heterogeneous Dial-A-Ride Problem (H-DARP).•Efficient crossover operators and local search techniques.•Experiments on existing DARP and H-DARP instances and new, larger, H-DARP instances.•Computational experiments show the effectiveness of our algorithm compared to the current state-of-the-art algorithms.
This paper investigates the Heterogeneous Dial-A-Ride Problem (H-DARP) that consists of determining a vehicle route planning for heterogeneous users’ transportation with a heterogeneous fleet of vehicles. A hybrid Genetic Algorithm (GA) is proposed to solve the problem. Efficient construction heuristics, crossover operators and local search techniques, specifically tailored to the characteristics of the H-DARP, are provided. The proposed algorithm is tested on 92 benchmarks instances and 40 newly introduced larger instances. Computational experiments show the effectiveness of our approach compared to the current state-of-the-art algorithms for the DARP and H-DARP. When tested on the existing instances, we achieved average gaps of only 0.47% to the best-known solutions for the DARP, and 0.05% to the optimal solutions for the H-DARP, compared to 0.85% and 0.10%, respectively, obtained by the current state-of-the-art algorithms. For the 40 newly generated instances, average gaps of the hybrid GA are 0.35% smaller compared to the current state-of-the-art method. Besides, our method provides best results for 31 of these instances and ties with the existing method on 8 other instances.</description><subject>Benchmarks</subject><subject>Construction heuristics</subject><subject>Genetic Algorithm (GA)</subject><subject>Genetic algorithms</subject><subject>Heterogeneous Dial-A-Ride Problem (H-DARP)</subject><subject>Heuristic</subject><subject>Hybrid algorithm</subject><subject>Local Search (LS)</subject><subject>Studies</subject><issn>0305-0548</issn><issn>1873-765X</issn><issn>0305-0548</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><recordid>eNp9kN9LwzAQx4MoOKd_gG8Bn1sv6dqk-FQ2twkDRRR8C2l63VK6ZSadsP_ejPnsvdzBfb_340PIPYOUASseu9Q4n_JYpoynAPKCjJgUWSKK_OuSjCCDPIF8Iq_JTQgdxBCcjcisoptj7W1DF7jDwRpa9Wvn7bDZ0tZ5OmyQLnFA79ax7w6Bzqzukyp5tw3SN-_qHre35KrVfcC7vzwmn_Pnj-kyWb0uXqbVKjETyYdEF6IsBTIEkXMoGl2W0tQo6zbjNS8kK7URUDbSGK5RQ2ayjKGRyMHUutbZmDyc5-69-z5gGFTnDn4XV6r4qxT5RHCIKnZWGe9C8Niqvbdb7Y-KgTrBUp2KsNQJlmJcRVjR83T2YDz_x6JXwVjcGWysRzOoxtl_3L8zyXE6</recordid><startdate>20170501</startdate><enddate>20170501</enddate><creator>Masmoudi, Mohamed Amine</creator><creator>Braekers, Kris</creator><creator>Masmoudi, Malek</creator><creator>Dammak, Abdelaziz</creator><general>Elsevier Ltd</general><general>Pergamon Press Inc</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>20170501</creationdate><title>A hybrid Genetic Algorithm for the Heterogeneous Dial-A-Ride Problem</title><author>Masmoudi, Mohamed Amine ; Braekers, Kris ; Masmoudi, Malek ; Dammak, Abdelaziz</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c482t-a67997e1e075206da998cbe8bf32b26819ac709d8cc2aea03c331ec8e20cbaba3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Benchmarks</topic><topic>Construction heuristics</topic><topic>Genetic Algorithm (GA)</topic><topic>Genetic algorithms</topic><topic>Heterogeneous Dial-A-Ride Problem (H-DARP)</topic><topic>Heuristic</topic><topic>Hybrid algorithm</topic><topic>Local Search (LS)</topic><topic>Studies</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Masmoudi, Mohamed Amine</creatorcontrib><creatorcontrib>Braekers, Kris</creatorcontrib><creatorcontrib>Masmoudi, Malek</creatorcontrib><creatorcontrib>Dammak, Abdelaziz</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>Computers & operations research</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Masmoudi, Mohamed Amine</au><au>Braekers, Kris</au><au>Masmoudi, Malek</au><au>Dammak, Abdelaziz</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A hybrid Genetic Algorithm for the Heterogeneous Dial-A-Ride Problem</atitle><jtitle>Computers & operations research</jtitle><date>2017-05-01</date><risdate>2017</risdate><volume>81</volume><spage>1</spage><epage>13</epage><pages>1-13</pages><issn>0305-0548</issn><eissn>1873-765X</eissn><eissn>0305-0548</eissn><coden>CMORAP</coden><abstract>•A new hybrid Genetic Algorithm for the Heterogeneous Dial-A-Ride Problem (H-DARP).•Efficient crossover operators and local search techniques.•Experiments on existing DARP and H-DARP instances and new, larger, H-DARP instances.•Computational experiments show the effectiveness of our algorithm compared to the current state-of-the-art algorithms.
This paper investigates the Heterogeneous Dial-A-Ride Problem (H-DARP) that consists of determining a vehicle route planning for heterogeneous users’ transportation with a heterogeneous fleet of vehicles. A hybrid Genetic Algorithm (GA) is proposed to solve the problem. Efficient construction heuristics, crossover operators and local search techniques, specifically tailored to the characteristics of the H-DARP, are provided. The proposed algorithm is tested on 92 benchmarks instances and 40 newly introduced larger instances. Computational experiments show the effectiveness of our approach compared to the current state-of-the-art algorithms for the DARP and H-DARP. When tested on the existing instances, we achieved average gaps of only 0.47% to the best-known solutions for the DARP, and 0.05% to the optimal solutions for the H-DARP, compared to 0.85% and 0.10%, respectively, obtained by the current state-of-the-art algorithms. For the 40 newly generated instances, average gaps of the hybrid GA are 0.35% smaller compared to the current state-of-the-art method. Besides, our method provides best results for 31 of these instances and ties with the existing method on 8 other instances.</abstract><cop>New York</cop><pub>Elsevier Ltd</pub><doi>10.1016/j.cor.2016.12.008</doi><tpages>13</tpages><oa>free_for_read</oa></addata></record> |
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subjects | Benchmarks Construction heuristics Genetic Algorithm (GA) Genetic algorithms Heterogeneous Dial-A-Ride Problem (H-DARP) Heuristic Hybrid algorithm Local Search (LS) Studies |
title | A hybrid Genetic Algorithm for the Heterogeneous Dial-A-Ride Problem |
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