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Henn S, Koch S, Doerner KF, Strauss C, Wäscher G (2010). Metaheuristics for the Order Batching Problem in Manual Order Picking Systems. BuR - Business Research, Vol. 3, Iss. 1, pp. 82-105, URN: urn:nbn:de:0009-20-25082
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%0 Journal Article %T Metaheuristics for the Order Batching Problem in Manual Order Picking Systems %A Henn, Sebastian %A Koch, Sören %A Doerner, F. Karl %A Strauss, Christine %A Wäscher, Gerhard %J BuR - Business Research %D 2010 %V 3 %N 1 %@ 1866-8658 %F henn2010 %X In manual order picking systems, order pickers walk or drive through a distribution warehouse in order to collect items which are requested by (internal or external) customers. In order to perform these operations efficiently, it is usually required that customer orders are combined into (more substantial) picking orders of limited size. The Order Batching Problem considered in this paper deals with the question of how a given set of customer orders should be combined such that the total length of all tours is minimized which are necessary to collect all items. The authors introduce two metaheuristic approaches for the solution of this problem: the first one is based on Iterated Local Search; the second on Ant Colony Optimization. In a series of extensive numerical experiments, the newly developed approaches are benchmarked against classic solution methods. It is demonstrated that the proposed methods are not only superior to existing methods but provide solutions which may allow distribution warehouses to be operated significantly more efficiently. %L 330 %K warehouse management %K order picking %K order batching %K iterated local search %K ant colony optimization %U http://nbn-resolving.de/urn:nbn:de:0009-20-25082 %P 82-105
Bibtex
@Article{henn2010,
author = "Henn, Sebastian
and Koch, S{\"o}ren
and Doerner, F. Karl
and Strauss, Christine
and W{\"a}scher, Gerhard",
title = "Metaheuristics for the Order Batching Problem in Manual Order Picking Systems",
journal = "BuR - Business Research",
year = "2010",
volume = "3",
number = "1",
pages = "82--105",
keywords = "warehouse management",
keywords = "order picking",
keywords = "order batching",
keywords = "iterated local search",
keywords = "ant colony optimization",
abstract = "In manual order picking systems, order pickers walk or drive through a distribution warehouse in order to collect items which are requested by (internal or external) customers. In order to perform these operations efficiently, it is usually required that customer orders are combined into (more substantial) picking orders of limited size. The Order Batching Problem considered in this paper deals with the question of how a given set of customer orders should be combined such that the total length of all tours is minimized which are necessary to collect all items. The authors introduce two metaheuristic approaches for the solution of this problem: the first one is based on Iterated Local Search; the second on Ant Colony Optimization. In a series of extensive numerical experiments, the newly developed approaches are benchmarked against classic solution methods. It is demonstrated that the proposed methods are not only superior to existing methods but provide solutions which may allow distribution warehouses to be operated significantly more efficiently.",
issn = "1866-8658",
url = "http://nbn-resolving.de/urn:nbn:de:0009-20-25082"
}
RIS
TY - JOUR AU - Henn, Sebastian AU - Koch, Sören AU - Doerner, F. Karl AU - Strauss, Christine AU - Wäscher, Gerhard PY - 2010// TI - Metaheuristics for the Order Batching Problem in Manual Order Picking Systems JO - BuR - Business Research SP - 82 EP - 105 VL - 3 IS - 1 KW - warehouse management KW - order picking KW - order batching KW - iterated local search KW - ant colony optimization N2 - In manual order picking systems, order pickers walk or drive through a distribution warehouse in order to collect items which are requested by (internal or external) customers. In order to perform these operations efficiently, it is usually required that customer orders are combined into (more substantial) picking orders of limited size. The Order Batching Problem considered in this paper deals with the question of how a given set of customer orders should be combined such that the total length of all tours is minimized which are necessary to collect all items. The authors introduce two metaheuristic approaches for the solution of this problem: the first one is based on Iterated Local Search; the second on Ant Colony Optimization. In a series of extensive numerical experiments, the newly developed approaches are benchmarked against classic solution methods. It is demonstrated that the proposed methods are not only superior to existing methods but provide solutions which may allow distribution warehouses to be operated significantly more efficiently. SN - 1866-8658 UR - http://nbn-resolving.de/urn:nbn:de:0009-20-25082 ID - henn2010 ER -
Wordbib
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ISI
PT Journal AU Henn, S Koch, S Doerner, FK Strauss, C Wäscher, G TI Metaheuristics for the Order Batching Problem in Manual Order Picking Systems SO BuR - Business Research PY 2010 BP 82 EP 105 VL 3 IS 1 DE warehouse management; order picking; order batching; iterated local search; ant colony optimization AB In manual order picking systems, order pickers walk or drive through a distribution warehouse in order to collect items which are requested by (internal or external) customers. In order to perform these operations efficiently, it is usually required that customer orders are combined into (more substantial) picking orders of limited size. The Order Batching Problem considered in this paper deals with the question of how a given set of customer orders should be combined such that the total length of all tours is minimized which are necessary to collect all items. The authors introduce two metaheuristic approaches for the solution of this problem: the first one is based on Iterated Local Search; the second on Ant Colony Optimization. In a series of extensive numerical experiments, the newly developed approaches are benchmarked against classic solution methods. It is demonstrated that the proposed methods are not only superior to existing methods but provide solutions which may allow distribution warehouses to be operated significantly more efficiently. ER
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<abstract>In manual order picking systems, order pickers walk or drive through a distribution warehouse in order to collect items which are requested by (internal or external) customers. In order to perform these operations efficiently, it is usually required that customer orders are combined into (more substantial) picking orders of limited size. The Order Batching Problem considered in this paper deals with the question of how a given set of customer orders should be combined such that the total length of all tours is minimized which are necessary to collect all items. The authors introduce two metaheuristic approaches for the solution of this problem: the first one is based on Iterated Local Search; the second on Ant Colony Optimization. In a series of extensive numerical experiments, the newly developed approaches are benchmarked against classic solution methods. It is demonstrated that the proposed methods are not only superior to existing methods but provide solutions which may allow distribution warehouses to be operated significantly more efficiently.</abstract>
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Full Metadata
| Bibliographic Citation | BuR - Business Research, Vol. 3, Iss. 1, pp. 82-105 |
|---|---|
| Title | Metaheuristics for the Order Batching Problem in Manual Order Picking Systems (eng) |
| Author | Sebastian Henn, Sören Koch, Karl F. Doerner, Christine Strauss, Gerhard Wäscher |
| Language | ---Sprache--- |
| Abstract | In manual order picking systems, order pickers walk or drive through a distribution warehouse in order to collect items which are requested by (internal or external) customers. In order to perform these operations efficiently, it is usually required that customer orders are combined into (more substantial) picking orders of limited size. The Order Batching Problem considered in this paper deals with the question of how a given set of customer orders should be combined such that the total length of all tours is minimized which are necessary to collect all items. The authors introduce two metaheuristic approaches for the solution of this problem: the first one is based on Iterated Local Search; the second on Ant Colony Optimization. In a series of extensive numerical experiments, the newly developed approaches are benchmarked against classic solution methods. It is demonstrated that the proposed methods are not only superior to existing methods but provide solutions which may allow distribution warehouses to be operated significantly more efficiently. |
| Subject | warehouse management, order picking, order batching, iterated local search, ant colony optimization |
| DDC | 330 |
| Rights | authorcontract |
| URN: | urn:nbn:de:0009-20-25082 |
