A hybrid artificial neural network, genetic algorithm and column generation heuristic for minimizing makespan in manual order picking operations
•A soft computing-based column generation heuristic for order picking is proposed.•The proposed algorithm is compared against PSA-ACO and an exact method.•Based on numerical experiments some managerial insights are proposed. At an operational level, order picking is the main activity in fulfillment...
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| Veröffentlicht in: | Expert systems with applications 2020-11, Vol.159, p.113566, Article 113566 |
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| creator | Ardjmand, Ehsan Ghalehkhondabi, Iman Young II, William A. Sadeghi, Azadeh Weckman, Gary R. Shakeri, Heman |
| description | •A soft computing-based column generation heuristic for order picking is proposed.•The proposed algorithm is compared against PSA-ACO and an exact method.•Based on numerical experiments some managerial insights are proposed.
At an operational level, order picking is the main activity in fulfillment centers. Motivated by and through collaboration with a third party logistic company, this study presents a novel hybrid column generation (CG), genetic algorithm (GA), and artificial neural network (ANN) heuristic for minimizing makespan in manual order picking operations. The results of column generation heuristic is compared against a mixed integer programming model solved by Gurobi, and a parallel simulated annealing and ant colony optimization (PSA-ACO) previously proposed in the literature. Through numerical experiments, the superiority of CG heuristic compared to other methods is shown, and some managerial insights regarding the relationship between makespan optimization, workload balance, picking capacity, and number of pickers in order picking operations is presented. |
| doi_str_mv | 10.1016/j.eswa.2020.113566 |
| format | Article |
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At an operational level, order picking is the main activity in fulfillment centers. Motivated by and through collaboration with a third party logistic company, this study presents a novel hybrid column generation (CG), genetic algorithm (GA), and artificial neural network (ANN) heuristic for minimizing makespan in manual order picking operations. The results of column generation heuristic is compared against a mixed integer programming model solved by Gurobi, and a parallel simulated annealing and ant colony optimization (PSA-ACO) previously proposed in the literature. Through numerical experiments, the superiority of CG heuristic compared to other methods is shown, and some managerial insights regarding the relationship between makespan optimization, workload balance, picking capacity, and number of pickers in order picking operations is presented.</description><identifier>ISSN: 0957-4174</identifier><identifier>EISSN: 1873-6793</identifier><identifier>DOI: 10.1016/j.eswa.2020.113566</identifier><language>eng</language><publisher>New York: Elsevier Ltd</publisher><subject>Ant colony optimization ; Artificial neural networks ; Column generation ; Genetic algorithm ; Genetic algorithms ; Heuristic ; Heuristic methods ; Integer programming ; Mixed integer ; Neural networks ; Order batching ; Order picking ; Simulated annealing</subject><ispartof>Expert systems with applications, 2020-11, Vol.159, p.113566, Article 113566</ispartof><rights>2020 Elsevier Ltd</rights><rights>Copyright Elsevier BV Nov 30, 2020</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c328t-fd48dd2f697236c28cfdb5c874f06b82f644783369df950575501e5238860e8b3</citedby><cites>FETCH-LOGICAL-c328t-fd48dd2f697236c28cfdb5c874f06b82f644783369df950575501e5238860e8b3</cites><orcidid>0000-0002-2831-7048 ; 0000-0002-0166-2159 ; 0000-0001-6296-2940</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.sciencedirect.com/science/article/pii/S0957417420303900$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,776,780,3537,27901,27902,65534</link.rule.ids></links><search><creatorcontrib>Ardjmand, Ehsan</creatorcontrib><creatorcontrib>Ghalehkhondabi, Iman</creatorcontrib><creatorcontrib>Young II, William A.</creatorcontrib><creatorcontrib>Sadeghi, Azadeh</creatorcontrib><creatorcontrib>Weckman, Gary R.</creatorcontrib><creatorcontrib>Shakeri, Heman</creatorcontrib><title>A hybrid artificial neural network, genetic algorithm and column generation heuristic for minimizing makespan in manual order picking operations</title><title>Expert systems with applications</title><description>•A soft computing-based column generation heuristic for order picking is proposed.•The proposed algorithm is compared against PSA-ACO and an exact method.•Based on numerical experiments some managerial insights are proposed.
At an operational level, order picking is the main activity in fulfillment centers. Motivated by and through collaboration with a third party logistic company, this study presents a novel hybrid column generation (CG), genetic algorithm (GA), and artificial neural network (ANN) heuristic for minimizing makespan in manual order picking operations. The results of column generation heuristic is compared against a mixed integer programming model solved by Gurobi, and a parallel simulated annealing and ant colony optimization (PSA-ACO) previously proposed in the literature. Through numerical experiments, the superiority of CG heuristic compared to other methods is shown, and some managerial insights regarding the relationship between makespan optimization, workload balance, picking capacity, and number of pickers in order picking operations is presented.</description><subject>Ant colony optimization</subject><subject>Artificial neural networks</subject><subject>Column generation</subject><subject>Genetic algorithm</subject><subject>Genetic algorithms</subject><subject>Heuristic</subject><subject>Heuristic methods</subject><subject>Integer programming</subject><subject>Mixed integer</subject><subject>Neural networks</subject><subject>Order batching</subject><subject>Order picking</subject><subject>Simulated annealing</subject><issn>0957-4174</issn><issn>1873-6793</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><recordid>eNp9UMmOEzEQtRBIhMAPcLLElQ5e2ktLXEYjNmkkLnC2HC9JJWm7sbsZDV_BJ-OezJlTPdVbqvQQekvJjhIqP5x2od7bHSOsLSgXUj5DG6oV76Qa-HO0IYNQXU9V_xK9qvVECFWEqA36e4OPD_sCHtsyQwQH9oJTWMrjmO9zOb_Hh9AgOGwvh1xgPo7YJo9dvixjeiSLnSEnfGw-qKsy5oJHSDDCH0gHPNpzqJNNGFLDaWnhufhQ8ATuvAry9JRRX6MX0V5qePM0t-jn508_br92d9-_fLu9uescZ3ruou-19yzKQTEuHdMu-r1wWvWRyL1uRN8rzbkcfBwEEUoIQoNgXGtJgt7zLXp3zZ1K_rWEOptTXkpqJw3rRS_ooJp6i9hV5UqutYRopgKjLQ-GErM2b05mbd6szZtr88308WoK7f_fEIqpDkJywUMJbjY-w__s_wCXq48h</recordid><startdate>20201130</startdate><enddate>20201130</enddate><creator>Ardjmand, Ehsan</creator><creator>Ghalehkhondabi, Iman</creator><creator>Young II, William A.</creator><creator>Sadeghi, Azadeh</creator><creator>Weckman, Gary R.</creator><creator>Shakeri, Heman</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><orcidid>https://orcid.org/0000-0002-2831-7048</orcidid><orcidid>https://orcid.org/0000-0002-0166-2159</orcidid><orcidid>https://orcid.org/0000-0001-6296-2940</orcidid></search><sort><creationdate>20201130</creationdate><title>A hybrid artificial neural network, genetic algorithm and column generation heuristic for minimizing makespan in manual order picking operations</title><author>Ardjmand, Ehsan ; Ghalehkhondabi, Iman ; Young II, William A. ; Sadeghi, Azadeh ; Weckman, Gary R. ; Shakeri, Heman</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c328t-fd48dd2f697236c28cfdb5c874f06b82f644783369df950575501e5238860e8b3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Ant colony optimization</topic><topic>Artificial neural networks</topic><topic>Column generation</topic><topic>Genetic algorithm</topic><topic>Genetic algorithms</topic><topic>Heuristic</topic><topic>Heuristic methods</topic><topic>Integer programming</topic><topic>Mixed integer</topic><topic>Neural networks</topic><topic>Order batching</topic><topic>Order picking</topic><topic>Simulated annealing</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Ardjmand, Ehsan</creatorcontrib><creatorcontrib>Ghalehkhondabi, Iman</creatorcontrib><creatorcontrib>Young II, William A.</creatorcontrib><creatorcontrib>Sadeghi, Azadeh</creatorcontrib><creatorcontrib>Weckman, Gary R.</creatorcontrib><creatorcontrib>Shakeri, Heman</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>Ardjmand, Ehsan</au><au>Ghalehkhondabi, Iman</au><au>Young II, William A.</au><au>Sadeghi, Azadeh</au><au>Weckman, Gary R.</au><au>Shakeri, Heman</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A hybrid artificial neural network, genetic algorithm and column generation heuristic for minimizing makespan in manual order picking operations</atitle><jtitle>Expert systems with applications</jtitle><date>2020-11-30</date><risdate>2020</risdate><volume>159</volume><spage>113566</spage><pages>113566-</pages><artnum>113566</artnum><issn>0957-4174</issn><eissn>1873-6793</eissn><abstract>•A soft computing-based column generation heuristic for order picking is proposed.•The proposed algorithm is compared against PSA-ACO and an exact method.•Based on numerical experiments some managerial insights are proposed.
At an operational level, order picking is the main activity in fulfillment centers. Motivated by and through collaboration with a third party logistic company, this study presents a novel hybrid column generation (CG), genetic algorithm (GA), and artificial neural network (ANN) heuristic for minimizing makespan in manual order picking operations. The results of column generation heuristic is compared against a mixed integer programming model solved by Gurobi, and a parallel simulated annealing and ant colony optimization (PSA-ACO) previously proposed in the literature. Through numerical experiments, the superiority of CG heuristic compared to other methods is shown, and some managerial insights regarding the relationship between makespan optimization, workload balance, picking capacity, and number of pickers in order picking operations is presented.</abstract><cop>New York</cop><pub>Elsevier Ltd</pub><doi>10.1016/j.eswa.2020.113566</doi><orcidid>https://orcid.org/0000-0002-2831-7048</orcidid><orcidid>https://orcid.org/0000-0002-0166-2159</orcidid><orcidid>https://orcid.org/0000-0001-6296-2940</orcidid></addata></record> |
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| ispartof | Expert systems with applications, 2020-11, Vol.159, p.113566, Article 113566 |
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| language | eng |
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| source | Elsevier ScienceDirect Journals |
| subjects | Ant colony optimization Artificial neural networks Column generation Genetic algorithm Genetic algorithms Heuristic Heuristic methods Integer programming Mixed integer Neural networks Order batching Order picking Simulated annealing |
| title | A hybrid artificial neural network, genetic algorithm and column generation heuristic for minimizing makespan in manual order picking operations |
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