Integrated Multi-Project Scheduling and Hierarchical Workforce Allocation in the ETO Assembly Process

The engineer-to-order (ETO) production strategy plays an important role in today’s manufacturing industry. This paper studies integrated multi-project scheduling and hierarchical workforce allocation in the assembly process of ETO products. The multi-project scheduling problem involves the...
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Autor*in:	Chun Jiang [verfasserIn] Xiaofeng Hu [verfasserIn] Juntong Xi [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2019

Schlagwörter:	engineer-to-order multi-project scheduling hierarchical workforce worker allocation particle swarm optimization Tabu search

Übergeordnetes Werk:	In: Applied Sciences - MDPI AG, 2012, 9(2019), 5, p 885
Übergeordnetes Werk:	volume:9 ; year:2019 ; number:5, p 885

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/app9050885

Katalog-ID:	DOAJ008737932

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spelling	10.3390/app9050885 doi (DE-627)DOAJ008737932 (DE-599)DOAJ6be03beb24874cc3891ec798a384e105 DE-627 ger DE-627 rakwb eng TA1-2040 QH301-705.5 QC1-999 QD1-999 Chun Jiang verfasserin aut Integrated Multi-Project Scheduling and Hierarchical Workforce Allocation in the ETO Assembly Process 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The engineer-to-order (ETO) production strategy plays an important role in today’s manufacturing industry. This paper studies integrated multi-project scheduling and hierarchical workforce allocation in the assembly process of ETO products. The multi-project scheduling problem involves the scheduling of tasks of different projects under many constraints, and the workforce allocation problem involves assigning hierarchical workers to each task. These two problems are interrelated. The task duration depends on the number of hierarchical workers assigned to the task. We developed a mathematical model to represent the problem. In order to solve this issue with the minimization of the makespan as the objective, we propose a hybrid algorithm combining particle swarm optimization (PSO) and Tabu search (TS). The improved PSO is designed as the global search process and the Tabu search is introduced to improve the local searching ability. The proposed algorithm is tested on different scales of benchmark instances and a case that uses industrial data from a collaborating steam turbine company. The results show that the solution quality of the hybrid algorithm outperforms the other three algorithms proposed in the literature and the experienced project manager. engineer-to-order multi-project scheduling hierarchical workforce worker allocation particle swarm optimization Tabu search Technology T Engineering (General). Civil engineering (General) Biology (General) Physics Chemistry Xiaofeng Hu verfasserin aut Juntong Xi verfasserin aut In Applied Sciences MDPI AG, 2012 9(2019), 5, p 885 (DE-627)737287640 (DE-600)2704225-X 20763417 nnns volume:9 year:2019 number:5, p 885 https://doi.org/10.3390/app9050885 kostenfrei https://doaj.org/article/6be03beb24874cc3891ec798a384e105 kostenfrei http://www.mdpi.com/2076-3417/9/5/885 kostenfrei https://doaj.org/toc/2076-3417 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 9 2019 5, p 885
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allfieldsGer	10.3390/app9050885 doi (DE-627)DOAJ008737932 (DE-599)DOAJ6be03beb24874cc3891ec798a384e105 DE-627 ger DE-627 rakwb eng TA1-2040 QH301-705.5 QC1-999 QD1-999 Chun Jiang verfasserin aut Integrated Multi-Project Scheduling and Hierarchical Workforce Allocation in the ETO Assembly Process 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The engineer-to-order (ETO) production strategy plays an important role in today’s manufacturing industry. This paper studies integrated multi-project scheduling and hierarchical workforce allocation in the assembly process of ETO products. The multi-project scheduling problem involves the scheduling of tasks of different projects under many constraints, and the workforce allocation problem involves assigning hierarchical workers to each task. These two problems are interrelated. The task duration depends on the number of hierarchical workers assigned to the task. We developed a mathematical model to represent the problem. In order to solve this issue with the minimization of the makespan as the objective, we propose a hybrid algorithm combining particle swarm optimization (PSO) and Tabu search (TS). The improved PSO is designed as the global search process and the Tabu search is introduced to improve the local searching ability. The proposed algorithm is tested on different scales of benchmark instances and a case that uses industrial data from a collaborating steam turbine company. The results show that the solution quality of the hybrid algorithm outperforms the other three algorithms proposed in the literature and the experienced project manager. engineer-to-order multi-project scheduling hierarchical workforce worker allocation particle swarm optimization Tabu search Technology T Engineering (General). Civil engineering (General) Biology (General) Physics Chemistry Xiaofeng Hu verfasserin aut Juntong Xi verfasserin aut In Applied Sciences MDPI AG, 2012 9(2019), 5, p 885 (DE-627)737287640 (DE-600)2704225-X 20763417 nnns volume:9 year:2019 number:5, p 885 https://doi.org/10.3390/app9050885 kostenfrei https://doaj.org/article/6be03beb24874cc3891ec798a384e105 kostenfrei http://www.mdpi.com/2076-3417/9/5/885 kostenfrei https://doaj.org/toc/2076-3417 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 9 2019 5, p 885
allfieldsSound	10.3390/app9050885 doi (DE-627)DOAJ008737932 (DE-599)DOAJ6be03beb24874cc3891ec798a384e105 DE-627 ger DE-627 rakwb eng TA1-2040 QH301-705.5 QC1-999 QD1-999 Chun Jiang verfasserin aut Integrated Multi-Project Scheduling and Hierarchical Workforce Allocation in the ETO Assembly Process 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The engineer-to-order (ETO) production strategy plays an important role in today’s manufacturing industry. This paper studies integrated multi-project scheduling and hierarchical workforce allocation in the assembly process of ETO products. The multi-project scheduling problem involves the scheduling of tasks of different projects under many constraints, and the workforce allocation problem involves assigning hierarchical workers to each task. These two problems are interrelated. The task duration depends on the number of hierarchical workers assigned to the task. We developed a mathematical model to represent the problem. In order to solve this issue with the minimization of the makespan as the objective, we propose a hybrid algorithm combining particle swarm optimization (PSO) and Tabu search (TS). The improved PSO is designed as the global search process and the Tabu search is introduced to improve the local searching ability. The proposed algorithm is tested on different scales of benchmark instances and a case that uses industrial data from a collaborating steam turbine company. The results show that the solution quality of the hybrid algorithm outperforms the other three algorithms proposed in the literature and the experienced project manager. engineer-to-order multi-project scheduling hierarchical workforce worker allocation particle swarm optimization Tabu search Technology T Engineering (General). Civil engineering (General) Biology (General) Physics Chemistry Xiaofeng Hu verfasserin aut Juntong Xi verfasserin aut In Applied Sciences MDPI AG, 2012 9(2019), 5, p 885 (DE-627)737287640 (DE-600)2704225-X 20763417 nnns volume:9 year:2019 number:5, p 885 https://doi.org/10.3390/app9050885 kostenfrei https://doaj.org/article/6be03beb24874cc3891ec798a384e105 kostenfrei http://www.mdpi.com/2076-3417/9/5/885 kostenfrei https://doaj.org/toc/2076-3417 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 9 2019 5, p 885
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callnumber-first	T - Technology
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spellingShingle	Chun Jiang misc TA1-2040 misc QH301-705.5 misc QC1-999 misc QD1-999 misc engineer-to-order misc multi-project scheduling misc hierarchical workforce misc worker allocation misc particle swarm optimization misc Tabu search misc Technology misc T misc Engineering (General). Civil engineering (General) misc Biology (General) misc Physics misc Chemistry Integrated Multi-Project Scheduling and Hierarchical Workforce Allocation in the ETO Assembly Process
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topic	misc TA1-2040 misc QH301-705.5 misc QC1-999 misc QD1-999 misc engineer-to-order misc multi-project scheduling misc hierarchical workforce misc worker allocation misc particle swarm optimization misc Tabu search misc Technology misc T misc Engineering (General). Civil engineering (General) misc Biology (General) misc Physics misc Chemistry
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title_auth	Integrated Multi-Project Scheduling and Hierarchical Workforce Allocation in the ETO Assembly Process
abstract	The engineer-to-order (ETO) production strategy plays an important role in today’s manufacturing industry. This paper studies integrated multi-project scheduling and hierarchical workforce allocation in the assembly process of ETO products. The multi-project scheduling problem involves the scheduling of tasks of different projects under many constraints, and the workforce allocation problem involves assigning hierarchical workers to each task. These two problems are interrelated. The task duration depends on the number of hierarchical workers assigned to the task. We developed a mathematical model to represent the problem. In order to solve this issue with the minimization of the makespan as the objective, we propose a hybrid algorithm combining particle swarm optimization (PSO) and Tabu search (TS). The improved PSO is designed as the global search process and the Tabu search is introduced to improve the local searching ability. The proposed algorithm is tested on different scales of benchmark instances and a case that uses industrial data from a collaborating steam turbine company. The results show that the solution quality of the hybrid algorithm outperforms the other three algorithms proposed in the literature and the experienced project manager.
abstractGer	The engineer-to-order (ETO) production strategy plays an important role in today’s manufacturing industry. This paper studies integrated multi-project scheduling and hierarchical workforce allocation in the assembly process of ETO products. The multi-project scheduling problem involves the scheduling of tasks of different projects under many constraints, and the workforce allocation problem involves assigning hierarchical workers to each task. These two problems are interrelated. The task duration depends on the number of hierarchical workers assigned to the task. We developed a mathematical model to represent the problem. In order to solve this issue with the minimization of the makespan as the objective, we propose a hybrid algorithm combining particle swarm optimization (PSO) and Tabu search (TS). The improved PSO is designed as the global search process and the Tabu search is introduced to improve the local searching ability. The proposed algorithm is tested on different scales of benchmark instances and a case that uses industrial data from a collaborating steam turbine company. The results show that the solution quality of the hybrid algorithm outperforms the other three algorithms proposed in the literature and the experienced project manager.
abstract_unstemmed	The engineer-to-order (ETO) production strategy plays an important role in today’s manufacturing industry. This paper studies integrated multi-project scheduling and hierarchical workforce allocation in the assembly process of ETO products. The multi-project scheduling problem involves the scheduling of tasks of different projects under many constraints, and the workforce allocation problem involves assigning hierarchical workers to each task. These two problems are interrelated. The task duration depends on the number of hierarchical workers assigned to the task. We developed a mathematical model to represent the problem. In order to solve this issue with the minimization of the makespan as the objective, we propose a hybrid algorithm combining particle swarm optimization (PSO) and Tabu search (TS). The improved PSO is designed as the global search process and the Tabu search is introduced to improve the local searching ability. The proposed algorithm is tested on different scales of benchmark instances and a case that uses industrial data from a collaborating steam turbine company. The results show that the solution quality of the hybrid algorithm outperforms the other three algorithms proposed in the literature and the experienced project manager.
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title_short	Integrated Multi-Project Scheduling and Hierarchical Workforce Allocation in the ETO Assembly Process
url	https://doi.org/10.3390/app9050885 https://doaj.org/article/6be03beb24874cc3891ec798a384e105 http://www.mdpi.com/2076-3417/9/5/885 https://doaj.org/toc/2076-3417
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Integrated Multi-Project Scheduling and Hierarchical Workforce Allocation in the ETO Assembly Process

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