Enhancing Association Rule Mining for Solving the Storage Location Assignment Problem

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dc.contributor.authorBohlin, Jonas
dc.contributor.authorGabrielii, Tobias
dc.contributor.departmentChalmers tekniska högskola / Institutionen för matematiska vetenskapersv
dc.contributor.examinerStrömberg, Ann-Brith
dc.contributor.supervisorGrover, Divya
dc.date.accessioned2022-08-29T13:17:35Z
dc.date.available2022-08-29T13:17:35Z
dc.date.issued2022sv
dc.date.submitted2020
dc.description.abstractIn the era of online retailing, reducing the picking time of orders is of great importance. One way of driving the picking time down is to optimise the locations of articles within the warehouse, a problem which is referred to as the Storage Location Assignment Problem (SLAP). The SLAP is an NP-hard problem, and it is therefore desirable to find a relaxation of the problem. In this thesis a rule based approach to the problem is proposed, focusing on association rule mining and rules created from a neural network utilising distance metric learning. These rules are then used by a greedy and a genetic algorithm, to optimise the article placements. The data used to find rules and evaluate the algorithms come from an online retailer of electronic spare parts. When evaluating the genetic algorithm on this dataset, it performs worse than the baseline of storing the most frequently purchased articles closest to the picking depot. However, the greedy algorithm outperforms this baseline by up to 11%, showing that there is a lot of promise for this rule based approach.sv
dc.identifier.coursecodeMVEX03sv
dc.identifier.urihttps://hdl.handle.net/20.500.12380/305465
dc.language.isoengsv
dc.setspec.uppsokPhysicsChemistryMaths
dc.subjectStorage Location Assignment Problem, Association Rule Mining, Distance Metric Learning, Genetic Algorithm, Warehouse Management.sv
dc.titleEnhancing Association Rule Mining for Solving the Storage Location Assignment Problemsv
dc.type.degreeExamensarbete för masterexamensv
dc.type.uppsokH
local.programmeEngineering mathematics and computational science (MPENM), MSc
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