Please use this identifier to cite or link to this item:
https://doi.org/10.1016/j.ejor.2020.03.016
DC Field | Value | |
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dc.title | A Novel Decomposition-Based Method for Solving General-Product Structure Assemble-to-Order Systems | |
dc.contributor.author | Mohsen Elhafsi | |
dc.contributor.author | Jianxin Fang | |
dc.contributor.author | Essia Hamouda | |
dc.date.accessioned | 2020-05-04T01:19:51Z | |
dc.date.available | 2020-05-04T01:19:51Z | |
dc.date.issued | 2020-03-13 | |
dc.identifier.citation | Mohsen Elhafsi, Jianxin Fang, Essia Hamouda (2020-03-13). A Novel Decomposition-Based Method for Solving General-Product Structure Assemble-to-Order Systems. ScholarBank@NUS Repository. https://doi.org/10.1016/j.ejor.2020.03.016 | |
dc.identifier.uri | https://scholarbank.nus.edu.sg/handle/10635/167586 | |
dc.description.abstract | Assemble-to-order (ATO) strategies are common to many industries. Despite their popularity, ATO systems remain challenging to analyze. We consider a general-product structure ATO problem modeled as an infinite horizon Markov decision process. As the optimal policy of such a system is computationally intractable, we develop a heuristic policy that is based on a decomposition of the original system, into a series of two-component ATO subsystems. We show that our decomposition heuristic policy (DHP) possesses many properties similar to those encountered in special- product structure ATO systems. Extensive numerical experiments show that the DHP is very efficient. In particular, we show that the DHP requires less than 10−5 the time required to obtain the optimal policy, with an average percentage cost gap less than 4% for systems with up to 5 components and 6 products. We also show that the DHP outperforms the state aggregation heuristic of Nadar et al. (2018), in terms of cost and computational effort. We further develop an information relaxation-based lower bound on the performance of the optimal policy. We show that such a bound is very efficient with an average percentage gap not exceeding 0.5% for systems with up to 5 components and 6 products. Using this lower bound, we further show that the average suboptimality gap of the DHP is within 9% for two special- product structure ATO systems, with up to 9 components and 10 products. Using a sophisticated computing platform, we believe the DHP can handle systems with a large number of components and products. | |
dc.description.uri | https://www.sciencedirect.com/science/article/abs/pii/S0377221720302307 | |
dc.language.iso | en | |
dc.publisher | European Journal of Operational Research | |
dc.rights | CC0 1.0 Universal | |
dc.rights.uri | http://creativecommons.org/publicdomain/zero/1.0/ | |
dc.subject | Production; Inventory control; Assemble-to-order; Markov decision process; approximate policy; information relaxation lower bound | |
dc.type | Article | |
dc.contributor.department | INDUSTRIAL SYSTEMS ENGINEERING AND MANAGEMENT | |
dc.description.doi | 10.1016/j.ejor.2020.03.016 | |
dc.published.state | Published | |
dc.grant.id | NRFRSS2016- 004 | |
dc.grant.id | R-266-000-096-133 | |
dc.grant.id | R-266-000-096-731 | |
dc.grant.id | R-266-000-100-646 | |
dc.grant.id | MOE2017-T2-2-153 | |
dc.grant.fundingagency | NRF Singapore | |
dc.grant.fundingagency | MOE-AcRF Tier 1, Tier 2 | |
Appears in Collections: | Staff Publications Elements |
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EJOR-D-19-01805.pdf | 1.01 MB | Adobe PDF | CLOSED | Post-print |
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