International Journal of Management and Applied Science (IJMAS)
current issues
Volume-4,Issue-10  ( Oct, 2018 )
Past issues
  1. Volume-4,Issue-10  ( Oct, 2018 )
  2. Volume-4,Issue-9  ( Sep, 2018 )
  3. Volume-4,Issue-8  ( Aug, 2018 )
  4. Volume-4,Issue-7  ( Jul, 2018 )
  5. Volume-4,Issue-6  ( Jun, 2018 )
  6. Volume-4,Issue-5  ( May, 2018 )
  7. Volume-4,Issue-4  ( Apr, 2018 )
  8. Volume-4,Issue-3  ( Mar, 2018 )
  9. Volume-4,Issue-2  ( Feb, 2018 )
  10. Volume-4,Issue-1  ( Jan, 2018 )

Statistics report
Jan. 2019
Submitted Papers : 80
Accepted Papers : 10
Rejected Papers : 70
Acc. Perc : 12%
Issue Published : 56
Paper Published : 3637
No. of Authors : 7479
  Journal Paper




Paper Title :
Order Batching in a Picker-to-Part Warehousing System of a Supply Chain

Author :Jason Chao-Hsien Pan, Ming-Hung Wu

Article Citation :Jason Chao-Hsien Pan ,Ming-Hung Wu , (2018 ) " Order Batching in a Picker-to-Part Warehousing System of a Supply Chain " , International Journal of Management and Applied Science (IJMAS) , pp. 7-10, Volume-4,Issue-9

Abstract : Order batch policy determines the way orders are combined into batches so that the travel distance of orders fulfilled can be reduced. Since the order batching problem (OBP) is NP-hard in the strong sense, meta-heuristic approaches have been proposed and genetic algorithm (GA) is the most commonly applied method for picker-to-part warehousing systems. However, for the grouping problem, group genetic algorithm (GGA) is more suitable than the classic GA since the encodings of GA used are not adapted to the cost function to be optimized. This paper develops a meta-heuristic method based on GGA for OBP and defines an indicator by incorporating the similarity of the picking positions of orders into routing characteristics for each of the three routing policies of return, traversal and largest gap. The results of the numerical experiment indicate that the throughput of the proposed heuristic method is statistically better than the existing ones for a picker-to-part warehousing system. Keywords - Supply Chain Management,Picker-to-Part Warehouse System, .Order Batching, Group Genetic Algorithm,

Type : Research paper

Indexed : Google Scholar


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