Meta-heuristic algorithms for solving the sustainable agro-food grain supply chain network design problem

Purpose – Due to unceasing declination in environment, sustainable agro-food supply chains have become a topic of concern to business, government organizations and customers. The purpose of this study is to examine a problem associated with sustainable network design in context of Indian agro-food grain supply chain. Design/methodology/approach – A mixed integer nonlinear programming (MINLP) model is suggested to apprehend the major complications related with two-echelon food grain supply chain along with sustainability aspects (carbon emissions). Genetic algorithm (GA) and quantum-based genetic algorithm (Q-GA), two meta-heuristic algorithms and LINGO 18 (traditional approach) are employed to establish the vehicle allocation and selection of orders set. Findings – The model minimizes the total transportation cost and carbon emission tax in gathering food grains from farmers to the hubs and later to the selected demand points (warehouses). The simulated data are adopted to test and validate the suggested model. The computational experiments concede that the performance of LINGO is superior than meta-heuristic algorithms (GA and Q-GA) in terms of solution obtained, but there is trade-off with respect to computational time. Research limitations/implications – In literature, inadequate study has been perceived on defining environmental sustainable issues connected with agro-food supply chain from farmer to final distribution centers. A MINLP model has been formulated as practical scenario for central part of India that captures all the major complexities to make the system more efficient. This study is regulated to agro-food Indian industries. Originality/value – The suggested network design problem is an innovative approach to design distribution systems from farmers to the hubs and later to the selected warehouses. This study considerably assists the organizations to design their distribution network more efficiently. Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Ashish Dwivedi [verfasserIn] Ajay Jha [verfasserIn] Dhirendra Prajapati [verfasserIn] Nenavath Sreenu [verfasserIn] Saurabh Pratap [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	agro-supply chain sustainability mixed integer nonlinear programming meta-heuristics

Übergeordnetes Werk:	In: Modern Supply Chain Research and Applications - Emerald Publishing, 2021, 2(2020), 3, Seite 161-177
Übergeordnetes Werk:	volume:2 ; year:2020 ; number:3 ; pages:161-177

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1108/MSCRA-04-2020-0007

Katalog-ID:	DOAJ084528354

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allfieldsGer	10.1108/MSCRA-04-2020-0007 doi (DE-627)DOAJ084528354 (DE-599)DOAJe45f2ad7aa8c410e9e02f1ff9c89d428 DE-627 ger DE-627 rakwb eng T1-995 Ashish Dwivedi verfasserin aut Meta-heuristic algorithms for solving the sustainable agro-food grain supply chain network design problem 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Purpose – Due to unceasing declination in environment, sustainable agro-food supply chains have become a topic of concern to business, government organizations and customers. The purpose of this study is to examine a problem associated with sustainable network design in context of Indian agro-food grain supply chain. Design/methodology/approach – A mixed integer nonlinear programming (MINLP) model is suggested to apprehend the major complications related with two-echelon food grain supply chain along with sustainability aspects (carbon emissions). Genetic algorithm (GA) and quantum-based genetic algorithm (Q-GA), two meta-heuristic algorithms and LINGO 18 (traditional approach) are employed to establish the vehicle allocation and selection of orders set. Findings – The model minimizes the total transportation cost and carbon emission tax in gathering food grains from farmers to the hubs and later to the selected demand points (warehouses). The simulated data are adopted to test and validate the suggested model. The computational experiments concede that the performance of LINGO is superior than meta-heuristic algorithms (GA and Q-GA) in terms of solution obtained, but there is trade-off with respect to computational time. Research limitations/implications – In literature, inadequate study has been perceived on defining environmental sustainable issues connected with agro-food supply chain from farmer to final distribution centers. A MINLP model has been formulated as practical scenario for central part of India that captures all the major complexities to make the system more efficient. This study is regulated to agro-food Indian industries. Originality/value – The suggested network design problem is an innovative approach to design distribution systems from farmers to the hubs and later to the selected warehouses. This study considerably assists the organizations to design their distribution network more efficiently. agro-supply chain sustainability mixed integer nonlinear programming meta-heuristics Technology (General) Ajay Jha verfasserin aut Dhirendra Prajapati verfasserin aut Nenavath Sreenu verfasserin aut Saurabh Pratap verfasserin aut In Modern Supply Chain Research and Applications Emerald Publishing, 2021 2(2020), 3, Seite 161-177 (DE-627)1684915694 26313871 nnns volume:2 year:2020 number:3 pages:161-177 https://doi.org/10.1108/MSCRA-04-2020-0007 kostenfrei https://doaj.org/article/e45f2ad7aa8c410e9e02f1ff9c89d428 kostenfrei https://www.emerald.com/insight/content/doi/10.1108/MSCRA-04-2020-0007/full/pdf?title=meta-heuristic-algorithms-for-solving-the-sustainable-agro-food-grain-supply-chain-network-design-problem kostenfrei https://doaj.org/toc/2631-3871 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_21 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 2 2020 3 161-177
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title	Meta-heuristic algorithms for solving the sustainable agro-food grain supply chain network design problem
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author_browse	Ashish Dwivedi Ajay Jha Dhirendra Prajapati Nenavath Sreenu Saurabh Pratap
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title_sort	meta-heuristic algorithms for solving the sustainable agro-food grain supply chain network design problem
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title_auth	Meta-heuristic algorithms for solving the sustainable agro-food grain supply chain network design problem
abstract	Purpose – Due to unceasing declination in environment, sustainable agro-food supply chains have become a topic of concern to business, government organizations and customers. The purpose of this study is to examine a problem associated with sustainable network design in context of Indian agro-food grain supply chain. Design/methodology/approach – A mixed integer nonlinear programming (MINLP) model is suggested to apprehend the major complications related with two-echelon food grain supply chain along with sustainability aspects (carbon emissions). Genetic algorithm (GA) and quantum-based genetic algorithm (Q-GA), two meta-heuristic algorithms and LINGO 18 (traditional approach) are employed to establish the vehicle allocation and selection of orders set. Findings – The model minimizes the total transportation cost and carbon emission tax in gathering food grains from farmers to the hubs and later to the selected demand points (warehouses). The simulated data are adopted to test and validate the suggested model. The computational experiments concede that the performance of LINGO is superior than meta-heuristic algorithms (GA and Q-GA) in terms of solution obtained, but there is trade-off with respect to computational time. Research limitations/implications – In literature, inadequate study has been perceived on defining environmental sustainable issues connected with agro-food supply chain from farmer to final distribution centers. A MINLP model has been formulated as practical scenario for central part of India that captures all the major complexities to make the system more efficient. This study is regulated to agro-food Indian industries. Originality/value – The suggested network design problem is an innovative approach to design distribution systems from farmers to the hubs and later to the selected warehouses. This study considerably assists the organizations to design their distribution network more efficiently.
abstractGer	Purpose – Due to unceasing declination in environment, sustainable agro-food supply chains have become a topic of concern to business, government organizations and customers. The purpose of this study is to examine a problem associated with sustainable network design in context of Indian agro-food grain supply chain. Design/methodology/approach – A mixed integer nonlinear programming (MINLP) model is suggested to apprehend the major complications related with two-echelon food grain supply chain along with sustainability aspects (carbon emissions). Genetic algorithm (GA) and quantum-based genetic algorithm (Q-GA), two meta-heuristic algorithms and LINGO 18 (traditional approach) are employed to establish the vehicle allocation and selection of orders set. Findings – The model minimizes the total transportation cost and carbon emission tax in gathering food grains from farmers to the hubs and later to the selected demand points (warehouses). The simulated data are adopted to test and validate the suggested model. The computational experiments concede that the performance of LINGO is superior than meta-heuristic algorithms (GA and Q-GA) in terms of solution obtained, but there is trade-off with respect to computational time. Research limitations/implications – In literature, inadequate study has been perceived on defining environmental sustainable issues connected with agro-food supply chain from farmer to final distribution centers. A MINLP model has been formulated as practical scenario for central part of India that captures all the major complexities to make the system more efficient. This study is regulated to agro-food Indian industries. Originality/value – The suggested network design problem is an innovative approach to design distribution systems from farmers to the hubs and later to the selected warehouses. This study considerably assists the organizations to design their distribution network more efficiently.
abstract_unstemmed	Purpose – Due to unceasing declination in environment, sustainable agro-food supply chains have become a topic of concern to business, government organizations and customers. The purpose of this study is to examine a problem associated with sustainable network design in context of Indian agro-food grain supply chain. Design/methodology/approach – A mixed integer nonlinear programming (MINLP) model is suggested to apprehend the major complications related with two-echelon food grain supply chain along with sustainability aspects (carbon emissions). Genetic algorithm (GA) and quantum-based genetic algorithm (Q-GA), two meta-heuristic algorithms and LINGO 18 (traditional approach) are employed to establish the vehicle allocation and selection of orders set. Findings – The model minimizes the total transportation cost and carbon emission tax in gathering food grains from farmers to the hubs and later to the selected demand points (warehouses). The simulated data are adopted to test and validate the suggested model. The computational experiments concede that the performance of LINGO is superior than meta-heuristic algorithms (GA and Q-GA) in terms of solution obtained, but there is trade-off with respect to computational time. Research limitations/implications – In literature, inadequate study has been perceived on defining environmental sustainable issues connected with agro-food supply chain from farmer to final distribution centers. A MINLP model has been formulated as practical scenario for central part of India that captures all the major complexities to make the system more efficient. This study is regulated to agro-food Indian industries. Originality/value – The suggested network design problem is an innovative approach to design distribution systems from farmers to the hubs and later to the selected warehouses. This study considerably assists the organizations to design their distribution network more efficiently.
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title_short	Meta-heuristic algorithms for solving the sustainable agro-food grain supply chain network design problem
url	https://doi.org/10.1108/MSCRA-04-2020-0007 https://doaj.org/article/e45f2ad7aa8c410e9e02f1ff9c89d428 https://www.emerald.com/insight/content/doi/10.1108/MSCRA-04-2020-0007/full/pdf?title=meta-heuristic-algorithms-for-solving-the-sustainable-agro-food-grain-supply-chain-network-design-problem https://doaj.org/toc/2631-3871
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