A Demand-Driven Model for Reallocating Workers in Assembly Lines
This paper introduces the <italic>demand-driven assembly line rebalancing problem</italic> (DDALRP) and proposes a non-linear, multi-objective, combinatorial optimization model to solve it. A DDALRP arises whenever the production output of the assembly line (AL) must be continuously read...
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9843988/ |
| _version_ | 1811310663330430976 |
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| author | Randall Mauricio Perez-Wheelock Wei Ou Pisal Yenradee Van-Nam Huynh |
| author_facet | Randall Mauricio Perez-Wheelock Wei Ou Pisal Yenradee Van-Nam Huynh |
| author_sort | Randall Mauricio Perez-Wheelock |
| collection | DOAJ |
| description | This paper introduces the <italic>demand-driven assembly line rebalancing problem</italic> (DDALRP) and proposes a non-linear, multi-objective, combinatorial optimization model to solve it. A DDALRP arises whenever the production output of the assembly line (AL) must be continuously readjusted along a planning horizon in order to satisfy as much as possible a given demand forecast; thus, dealing not with a one-time rebalance, but with a multi-period rebalance, fact that exponentially increases the complexity and combinatorial nature of the problem. Adapting or regulating the production output of the AL to a particular demand forecast or production plan is a relatively new idea in the assembly line balancing (ALB) / rebalancing (ALR) literature; and the novelty of this work is the rebalancing mechanism employed to solve the problem: we address the problem by reallocating workers to stations, taking into consideration their learning and forgetting (L&F) curves. Our proposed model was solved by implementing a genetic algorithm (GA) in 162 cases (three problem instances under 54 scenarios each), which produced useful insights about the dynamics of worker reallocation under different situations: optimistic, most-likely, pessimistic L&F coefficients; experienced and inexperienced workers; and different demand scenarios. |
| first_indexed | 2024-04-13T10:04:06Z |
| format | Article |
| id | doaj.art-425361d3b8564640a22c5e569081d059 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-13T10:04:06Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-425361d3b8564640a22c5e569081d0592022-12-22T02:51:10ZengIEEEIEEE Access2169-35362022-01-0110803008032010.1109/ACCESS.2022.31946589843988A Demand-Driven Model for Reallocating Workers in Assembly LinesRandall Mauricio Perez-Wheelock0https://orcid.org/0000-0001-9111-7965Wei Ou1Pisal Yenradee2https://orcid.org/0000-0001-8220-520XVan-Nam Huynh3https://orcid.org/0000-0002-3860-7815Graduate School of Advanced Science and Technology, Japan Advanced Institute of Science and Technology, Nomi, JapanInternational Business School, Zhejiang Gongshang University, Hangzhou, ChinaSchool of Manufacturing Systems and Mechanical Engineering, Sirindhorn International Institute of Technology, Thammasat University, Rangsit, ThailandGraduate School of Advanced Science and Technology, Japan Advanced Institute of Science and Technology, Nomi, JapanThis paper introduces the <italic>demand-driven assembly line rebalancing problem</italic> (DDALRP) and proposes a non-linear, multi-objective, combinatorial optimization model to solve it. A DDALRP arises whenever the production output of the assembly line (AL) must be continuously readjusted along a planning horizon in order to satisfy as much as possible a given demand forecast; thus, dealing not with a one-time rebalance, but with a multi-period rebalance, fact that exponentially increases the complexity and combinatorial nature of the problem. Adapting or regulating the production output of the AL to a particular demand forecast or production plan is a relatively new idea in the assembly line balancing (ALB) / rebalancing (ALR) literature; and the novelty of this work is the rebalancing mechanism employed to solve the problem: we address the problem by reallocating workers to stations, taking into consideration their learning and forgetting (L&F) curves. Our proposed model was solved by implementing a genetic algorithm (GA) in 162 cases (three problem instances under 54 scenarios each), which produced useful insights about the dynamics of worker reallocation under different situations: optimistic, most-likely, pessimistic L&F coefficients; experienced and inexperienced workers; and different demand scenarios.https://ieeexplore.ieee.org/document/9843988/Assembly linedemand forecastlearning & forgetting curvesmulti-period rebalancingworker reallocation |
| spellingShingle | Randall Mauricio Perez-Wheelock Wei Ou Pisal Yenradee Van-Nam Huynh A Demand-Driven Model for Reallocating Workers in Assembly Lines IEEE Access Assembly line demand forecast learning & forgetting curves multi-period rebalancing worker reallocation |
| title | A Demand-Driven Model for Reallocating Workers in Assembly Lines |
| title_full | A Demand-Driven Model for Reallocating Workers in Assembly Lines |
| title_fullStr | A Demand-Driven Model for Reallocating Workers in Assembly Lines |
| title_full_unstemmed | A Demand-Driven Model for Reallocating Workers in Assembly Lines |
| title_short | A Demand-Driven Model for Reallocating Workers in Assembly Lines |
| title_sort | demand driven model for reallocating workers in assembly lines |
| topic | Assembly line demand forecast learning & forgetting curves multi-period rebalancing worker reallocation |
| url | https://ieeexplore.ieee.org/document/9843988/ |
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