zbMATH — the first resource for mathematics

Using a multiple-GA method to solve the batch picking problem: Considering travel distance and order due time. (English) Zbl 1154.90381
Summary: Warehousing involves all activities related to the movement of goods such as receiving, storage, order picking, accumulation, sorting and shipping within warehouses or distribution centres. Among these activities, order picking is the most costly process because its operations are labour-intensive and repetitive. In this paper, we propose a batch picking model that considers not only travel cost but also an earliness and tardiness penalty to fulfil the current complex and quick-response oriented environment. This model is solved using a multiple-GA method for generating optimal batch picking plans. The core of the multiple-GA method consists of the GA\(_{\_\text{BATCH}}\) and GA\(_{\_\text{TSP}}\) algorithms. The GA\(_{\_\text{BATCH}}\) algorithm finds the optimal batch picking plan by minimizing the sum of the travel cost and earliness and tardiness penalty. The GA\(_{\_\text{TSP}}\) algorithm searches for the most effective travel path for a batch by minimizing the travel distance. To exhibit the benefits of the proposed model a set of simulations and a sensitivity analysis are conducted using a number of datasets with different order characteristics and warehouse environments. The results from these experiments show that the proposed method outperforms benchmark models.

90B30 Production models
Full Text: DOI
[1] DOI: 10.1016/j.eswa.2004.12.006 · doi:10.1016/j.eswa.2004.12.006
[2] DOI: 10.1016/j.omega.2004.05.003 · doi:10.1016/j.omega.2004.05.003
[3] DOI: 10.1080/15458830.1996.11770701 · doi:10.1080/15458830.1996.11770701
[4] DOI: 10.1080/00207549308956753 · doi:10.1080/00207549308956753
[5] DOI: 10.1023/A:1011049113445 · doi:10.1023/A:1011049113445
[6] Gen M, Genetic Algorithms and Engineering Optimisation (2000)
[7] DOI: 10.1016/0377-2217(92)90235-2 · doi:10.1016/0377-2217(92)90235-2
[8] DOI: 10.1080/00207540600558015 · Zbl 1094.90505 · doi:10.1080/00207540600558015
[9] Holland J, Adaptation in Natural and Artificial Systems (1975)
[10] DOI: 10.1016/j.compind.2004.06.001 · doi:10.1016/j.compind.2004.06.001
[11] DOI: 10.1080/00207540500151325 · Zbl 1082.90054 · doi:10.1080/00207540500151325
[12] DOI: 10.1016/0167-188X(88)90014-6 · doi:10.1016/0167-188X(88)90014-6
[13] DOI: 10.1080/002075499191094 · Zbl 0948.90508 · doi:10.1080/002075499191094
[14] DOI: 10.1016/0305-0483(95)00038-0 · doi:10.1016/0305-0483(95)00038-0
[15] DOI: 10.1108/09600039010005133 · doi:10.1108/09600039010005133
[16] DOI: 10.1080/00207540110028128 · Zbl 1060.90519 · doi:10.1080/00207540110028128
[17] DOI: 10.1080/00207549608904926 · Zbl 0926.90007 · doi:10.1080/00207549608904926
[18] DOI: 10.1080/00207540410001733896 · Zbl 1068.90011 · doi:10.1080/00207540410001733896
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. It attempts to reflect the references listed in the original paper as accurately as possible without claiming the completeness or perfect precision of the matching.