The time-window strategy in the online order batching problem

Gespeichert in:

Autor*in:	Gil-Borrás, Sergio [verfasserIn] Pardo, Eduardo G. [verfasserIn] Jiménez, Ernesto [verfasserIn] Sörensen, Kenneth [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2024

Schlagwörter:	fixed time window Online order batching problem order picking time window variable time window warehousing

Übergeordnetes Werk:	Enthalten in: International journal of production research - London [u.a.] : Taylor & Francis, 1996, 62(2024), 12, Seite 4446-4469
Übergeordnetes Werk:	volume:62 ; year:2024 ; number:12 ; pages:4446-4469

Links:	Link aufrufen Link aufrufen

DOI / URN:	10.1080/00207543.2023.2263884

Katalog-ID:	189123465X

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982			\|2 26 \|1 00 \|x DE-206 \|b When an order arrives at a warehouse it is usually assigned to a batch and a decision is made on how long to wait before assigning the batch to a picker and starting the picking tour. If the idle time of the pickers is minimised, the batch is immediately assigned, and the picking starts. Alternatively, if a time window is introduced, other orders may arrive, and more efficient batches may be formed. The method to decide how long to wait (the time-window strategy) is therefore important but, surprisingly, almost completely overlooked in the literature. In this paper, we demonstrate that this lack of attention is unwarranted, and that the time-window method significantly influences the overall warehouse performance. In the context of the online order batching problem (OOBP), we first demonstrate that the effects of different time-window strategies are independent of the methods used to solve the other subproblems of the OOBP (batching and routing). Second, we propose two new time-window strategies, compare them to existing methods, and prove that our methods outperform those in the literature under various scenarios. Finally, we show how time-window methods influence different objective functions of the OOBP when varying numbers of orders and pickers.

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allfields	10.1080/00207543.2023.2263884 doi (DE-627)189123465X (DE-599)KXP189123465X DE-627 ger DE-627 rda eng Gil-Borrás, Sergio verfasserin aut The time-window strategy in the online order batching problem Sergio Gil-Borrás, Eduardo G. Pardo, Ernesto Jiménez and Kenneth Sörensen 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier fixed time window (dpeaa)DE-206 Online order batching problem (dpeaa)DE-206 order picking (dpeaa)DE-206 time window (dpeaa)DE-206 variable time window (dpeaa)DE-206 warehousing (dpeaa)DE-206 Pardo, Eduardo G. verfasserin aut Jiménez, Ernesto verfasserin aut Sörensen, Kenneth verfasserin (DE-588)1064677991 (DE-627)814187730 (DE-576)424031582 aut Enthalten in International journal of production research London [u.a.] : Taylor & Francis, 1996 62(2024), 12, Seite 4446-4469 Online-Ressource (DE-627)301516731 (DE-600)1485085-0 (DE-576)094115516 1366-588X nnns volume:62 year:2024 number:12 pages:4446-4469 https://www.tandfonline.com/doi/pdf/10.1080/00207543.2023.2263884 Verlag lizenzpflichtig https://doi.org/10.1080/00207543.2023.2263884 Resolving-System lizenzpflichtig GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_63 GBV_ILN_70 GBV_ILN_100 GBV_ILN_224 GBV_ILN_285 GBV_ILN_370 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 62 2024 12 4446-4469 26 01 0206 4538705349 x1z 13-06-24 26 00 DE-206 When an order arrives at a warehouse it is usually assigned to a batch and a decision is made on how long to wait before assigning the batch to a picker and starting the picking tour. If the idle time of the pickers is minimised, the batch is immediately assigned, and the picking starts. Alternatively, if a time window is introduced, other orders may arrive, and more efficient batches may be formed. The method to decide how long to wait (the time-window strategy) is therefore important but, surprisingly, almost completely overlooked in the literature. In this paper, we demonstrate that this lack of attention is unwarranted, and that the time-window method significantly influences the overall warehouse performance. In the context of the online order batching problem (OOBP), we first demonstrate that the effects of different time-window strategies are independent of the methods used to solve the other subproblems of the OOBP (batching and routing). Second, we propose two new time-window strategies, compare them to existing methods, and prove that our methods outperform those in the literature under various scenarios. Finally, we show how time-window methods influence different objective functions of the OOBP when varying numbers of orders and pickers.
spelling	10.1080/00207543.2023.2263884 doi (DE-627)189123465X (DE-599)KXP189123465X DE-627 ger DE-627 rda eng Gil-Borrás, Sergio verfasserin aut The time-window strategy in the online order batching problem Sergio Gil-Borrás, Eduardo G. Pardo, Ernesto Jiménez and Kenneth Sörensen 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier fixed time window (dpeaa)DE-206 Online order batching problem (dpeaa)DE-206 order picking (dpeaa)DE-206 time window (dpeaa)DE-206 variable time window (dpeaa)DE-206 warehousing (dpeaa)DE-206 Pardo, Eduardo G. verfasserin aut Jiménez, Ernesto verfasserin aut Sörensen, Kenneth verfasserin (DE-588)1064677991 (DE-627)814187730 (DE-576)424031582 aut Enthalten in International journal of production research London [u.a.] : Taylor & Francis, 1996 62(2024), 12, Seite 4446-4469 Online-Ressource (DE-627)301516731 (DE-600)1485085-0 (DE-576)094115516 1366-588X nnns volume:62 year:2024 number:12 pages:4446-4469 https://www.tandfonline.com/doi/pdf/10.1080/00207543.2023.2263884 Verlag lizenzpflichtig https://doi.org/10.1080/00207543.2023.2263884 Resolving-System lizenzpflichtig GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_63 GBV_ILN_70 GBV_ILN_100 GBV_ILN_224 GBV_ILN_285 GBV_ILN_370 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 62 2024 12 4446-4469 26 01 0206 4538705349 x1z 13-06-24 26 00 DE-206 When an order arrives at a warehouse it is usually assigned to a batch and a decision is made on how long to wait before assigning the batch to a picker and starting the picking tour. If the idle time of the pickers is minimised, the batch is immediately assigned, and the picking starts. Alternatively, if a time window is introduced, other orders may arrive, and more efficient batches may be formed. The method to decide how long to wait (the time-window strategy) is therefore important but, surprisingly, almost completely overlooked in the literature. In this paper, we demonstrate that this lack of attention is unwarranted, and that the time-window method significantly influences the overall warehouse performance. In the context of the online order batching problem (OOBP), we first demonstrate that the effects of different time-window strategies are independent of the methods used to solve the other subproblems of the OOBP (batching and routing). Second, we propose two new time-window strategies, compare them to existing methods, and prove that our methods outperform those in the literature under various scenarios. Finally, we show how time-window methods influence different objective functions of the OOBP when varying numbers of orders and pickers.
allfields_unstemmed	10.1080/00207543.2023.2263884 doi (DE-627)189123465X (DE-599)KXP189123465X DE-627 ger DE-627 rda eng Gil-Borrás, Sergio verfasserin aut The time-window strategy in the online order batching problem Sergio Gil-Borrás, Eduardo G. Pardo, Ernesto Jiménez and Kenneth Sörensen 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier fixed time window (dpeaa)DE-206 Online order batching problem (dpeaa)DE-206 order picking (dpeaa)DE-206 time window (dpeaa)DE-206 variable time window (dpeaa)DE-206 warehousing (dpeaa)DE-206 Pardo, Eduardo G. verfasserin aut Jiménez, Ernesto verfasserin aut Sörensen, Kenneth verfasserin (DE-588)1064677991 (DE-627)814187730 (DE-576)424031582 aut Enthalten in International journal of production research London [u.a.] : Taylor & Francis, 1996 62(2024), 12, Seite 4446-4469 Online-Ressource (DE-627)301516731 (DE-600)1485085-0 (DE-576)094115516 1366-588X nnns volume:62 year:2024 number:12 pages:4446-4469 https://www.tandfonline.com/doi/pdf/10.1080/00207543.2023.2263884 Verlag lizenzpflichtig https://doi.org/10.1080/00207543.2023.2263884 Resolving-System lizenzpflichtig GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_63 GBV_ILN_70 GBV_ILN_100 GBV_ILN_224 GBV_ILN_285 GBV_ILN_370 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 62 2024 12 4446-4469 26 01 0206 4538705349 x1z 13-06-24 26 00 DE-206 When an order arrives at a warehouse it is usually assigned to a batch and a decision is made on how long to wait before assigning the batch to a picker and starting the picking tour. If the idle time of the pickers is minimised, the batch is immediately assigned, and the picking starts. Alternatively, if a time window is introduced, other orders may arrive, and more efficient batches may be formed. The method to decide how long to wait (the time-window strategy) is therefore important but, surprisingly, almost completely overlooked in the literature. In this paper, we demonstrate that this lack of attention is unwarranted, and that the time-window method significantly influences the overall warehouse performance. In the context of the online order batching problem (OOBP), we first demonstrate that the effects of different time-window strategies are independent of the methods used to solve the other subproblems of the OOBP (batching and routing). Second, we propose two new time-window strategies, compare them to existing methods, and prove that our methods outperform those in the literature under various scenarios. Finally, we show how time-window methods influence different objective functions of the OOBP when varying numbers of orders and pickers.
allfieldsGer	10.1080/00207543.2023.2263884 doi (DE-627)189123465X (DE-599)KXP189123465X DE-627 ger DE-627 rda eng Gil-Borrás, Sergio verfasserin aut The time-window strategy in the online order batching problem Sergio Gil-Borrás, Eduardo G. Pardo, Ernesto Jiménez and Kenneth Sörensen 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier fixed time window (dpeaa)DE-206 Online order batching problem (dpeaa)DE-206 order picking (dpeaa)DE-206 time window (dpeaa)DE-206 variable time window (dpeaa)DE-206 warehousing (dpeaa)DE-206 Pardo, Eduardo G. verfasserin aut Jiménez, Ernesto verfasserin aut Sörensen, Kenneth verfasserin (DE-588)1064677991 (DE-627)814187730 (DE-576)424031582 aut Enthalten in International journal of production research London [u.a.] : Taylor & Francis, 1996 62(2024), 12, Seite 4446-4469 Online-Ressource (DE-627)301516731 (DE-600)1485085-0 (DE-576)094115516 1366-588X nnns volume:62 year:2024 number:12 pages:4446-4469 https://www.tandfonline.com/doi/pdf/10.1080/00207543.2023.2263884 Verlag lizenzpflichtig https://doi.org/10.1080/00207543.2023.2263884 Resolving-System lizenzpflichtig GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_63 GBV_ILN_70 GBV_ILN_100 GBV_ILN_224 GBV_ILN_285 GBV_ILN_370 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 62 2024 12 4446-4469 26 01 0206 4538705349 x1z 13-06-24 26 00 DE-206 When an order arrives at a warehouse it is usually assigned to a batch and a decision is made on how long to wait before assigning the batch to a picker and starting the picking tour. If the idle time of the pickers is minimised, the batch is immediately assigned, and the picking starts. Alternatively, if a time window is introduced, other orders may arrive, and more efficient batches may be formed. The method to decide how long to wait (the time-window strategy) is therefore important but, surprisingly, almost completely overlooked in the literature. In this paper, we demonstrate that this lack of attention is unwarranted, and that the time-window method significantly influences the overall warehouse performance. In the context of the online order batching problem (OOBP), we first demonstrate that the effects of different time-window strategies are independent of the methods used to solve the other subproblems of the OOBP (batching and routing). Second, we propose two new time-window strategies, compare them to existing methods, and prove that our methods outperform those in the literature under various scenarios. Finally, we show how time-window methods influence different objective functions of the OOBP when varying numbers of orders and pickers.
allfieldsSound	10.1080/00207543.2023.2263884 doi (DE-627)189123465X (DE-599)KXP189123465X DE-627 ger DE-627 rda eng Gil-Borrás, Sergio verfasserin aut The time-window strategy in the online order batching problem Sergio Gil-Borrás, Eduardo G. Pardo, Ernesto Jiménez and Kenneth Sörensen 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier fixed time window (dpeaa)DE-206 Online order batching problem (dpeaa)DE-206 order picking (dpeaa)DE-206 time window (dpeaa)DE-206 variable time window (dpeaa)DE-206 warehousing (dpeaa)DE-206 Pardo, Eduardo G. verfasserin aut Jiménez, Ernesto verfasserin aut Sörensen, Kenneth verfasserin (DE-588)1064677991 (DE-627)814187730 (DE-576)424031582 aut Enthalten in International journal of production research London [u.a.] : Taylor & Francis, 1996 62(2024), 12, Seite 4446-4469 Online-Ressource (DE-627)301516731 (DE-600)1485085-0 (DE-576)094115516 1366-588X nnns volume:62 year:2024 number:12 pages:4446-4469 https://www.tandfonline.com/doi/pdf/10.1080/00207543.2023.2263884 Verlag lizenzpflichtig https://doi.org/10.1080/00207543.2023.2263884 Resolving-System lizenzpflichtig GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_63 GBV_ILN_70 GBV_ILN_100 GBV_ILN_224 GBV_ILN_285 GBV_ILN_370 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 62 2024 12 4446-4469 26 01 0206 4538705349 x1z 13-06-24 26 00 DE-206 When an order arrives at a warehouse it is usually assigned to a batch and a decision is made on how long to wait before assigning the batch to a picker and starting the picking tour. If the idle time of the pickers is minimised, the batch is immediately assigned, and the picking starts. Alternatively, if a time window is introduced, other orders may arrive, and more efficient batches may be formed. The method to decide how long to wait (the time-window strategy) is therefore important but, surprisingly, almost completely overlooked in the literature. In this paper, we demonstrate that this lack of attention is unwarranted, and that the time-window method significantly influences the overall warehouse performance. In the context of the online order batching problem (OOBP), we first demonstrate that the effects of different time-window strategies are independent of the methods used to solve the other subproblems of the OOBP (batching and routing). Second, we propose two new time-window strategies, compare them to existing methods, and prove that our methods outperform those in the literature under various scenarios. Finally, we show how time-window methods influence different objective functions of the OOBP when varying numbers of orders and pickers.
language	English
source	Enthalten in International journal of production research 62(2024), 12, Seite 4446-4469 volume:62 year:2024 number:12 pages:4446-4469
sourceStr	Enthalten in International journal of production research 62(2024), 12, Seite 4446-4469 volume:62 year:2024 number:12 pages:4446-4469
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institution	findex.gbv.de
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topic_facet	fixed time window Online order batching problem order picking time window variable time window warehousing
sw_local_iln_str_mv	26: DE-206:
isfreeaccess_bool	false
container_title	International journal of production research
authorswithroles_txt_mv	Gil-Borrás, Sergio @@aut@@ Pardo, Eduardo G. @@aut@@ Jiménez, Ernesto @@aut@@ Sörensen, Kenneth @@aut@@
publishDateDaySort_date	2024-01-01T00:00:00Z
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author	Gil-Borrás, Sergio
spellingShingle	Gil-Borrás, Sergio misc fixed time window misc Online order batching problem misc order picking misc time window misc variable time window misc warehousing The time-window strategy in the online order batching problem
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topic_title	26 00 DE-206 When an order arrives at a warehouse it is usually assigned to a batch and a decision is made on how long to wait before assigning the batch to a picker and starting the picking tour. If the idle time of the pickers is minimised, the batch is immediately assigned, and the picking starts. Alternatively, if a time window is introduced, other orders may arrive, and more efficient batches may be formed. The method to decide how long to wait (the time-window strategy) is therefore important but, surprisingly, almost completely overlooked in the literature. In this paper, we demonstrate that this lack of attention is unwarranted, and that the time-window method significantly influences the overall warehouse performance. In the context of the online order batching problem (OOBP), we first demonstrate that the effects of different time-window strategies are independent of the methods used to solve the other subproblems of the OOBP (batching and routing). Second, we propose two new time-window strategies, compare them to existing methods, and prove that our methods outperform those in the literature under various scenarios. Finally, we show how time-window methods influence different objective functions of the OOBP when varying numbers of orders and pickers The time-window strategy in the online order batching problem Sergio Gil-Borrás, Eduardo G. Pardo, Ernesto Jiménez and Kenneth Sörensen fixed time window (dpeaa)DE-206 Online order batching problem (dpeaa)DE-206 order picking (dpeaa)DE-206 time window (dpeaa)DE-206 variable time window (dpeaa)DE-206 warehousing (dpeaa)DE-206
topic	misc fixed time window misc Online order batching problem misc order picking misc time window misc variable time window misc warehousing
topic_unstemmed	misc fixed time window misc Online order batching problem misc order picking misc time window misc variable time window misc warehousing
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title_full	The time-window strategy in the online order batching problem Sergio Gil-Borrás, Eduardo G. Pardo, Ernesto Jiménez and Kenneth Sörensen
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title_short	The time-window strategy in the online order batching problem
url	https://www.tandfonline.com/doi/pdf/10.1080/00207543.2023.2263884 https://doi.org/10.1080/00207543.2023.2263884
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ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4326</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4393</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">62</subfield><subfield code="j">2024</subfield><subfield code="e">12</subfield><subfield code="h">4446-4469</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">26</subfield><subfield code="1">01</subfield><subfield code="x">0206</subfield><subfield code="b">4538705349</subfield><subfield code="y">x1z</subfield><subfield code="z">13-06-24</subfield></datafield><datafield tag="982" ind1=" " ind2=" "><subfield code="2">26</subfield><subfield code="1">00</subfield><subfield code="x">DE-206</subfield><subfield code="b">When an order arrives at a warehouse it is usually assigned to a batch and a decision is made on how long to wait before assigning the batch to a picker and starting the picking tour. If the idle time of the pickers is minimised, the batch is immediately assigned, and the picking starts. Alternatively, if a time window is introduced, other orders may arrive, and more efficient batches may be formed. The method to decide how long to wait (the time-window strategy) is therefore important but, surprisingly, almost completely overlooked in the literature. In this paper, we demonstrate that this lack of attention is unwarranted, and that the time-window method significantly influences the overall warehouse performance. In the context of the online order batching problem (OOBP), we first demonstrate that the effects of different time-window strategies are independent of the methods used to solve the other subproblems of the OOBP (batching and routing). Second, we propose two new time-window strategies, compare them to existing methods, and prove that our methods outperform those in the literature under various scenarios. Finally, we show how time-window methods influence different objective functions of the OOBP when varying numbers of orders and pickers.</subfield></datafield></record></collection>
score	7.398567

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The time-window strategy in the online order batching problem

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