Robust Agricultural Supply Chain Management with Various Random and Fuzzy Parameters

Takashi Hasuike; Tomoko Kashima; Shimpei Matsumoto

doi:10.1109/IIAI-AAI.2017.88

Robust Agricultural Supply Chain Management with Various Random and Fuzzy Parameters

Takashi Hasuike, Tomoko Kashima, Shimpei Matsumoto

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

Abstract

This paper proposes a robust model of multiperiod agricultural supply chain to consider both maximizing the total profit and minimizing the environmental load with random and fuzzy parameters. Our proposed model is formulated as a fuzzy and stochastic, multiobjective and multiperiod programming problem, and hence, it is hard to solve the formulated problem directly without setting a specific random distribution and a specific membership function. Therefore, a distribution-free approach based on sample mean and variance derived from received data, which does not assume any specific random distributions and membership functions, is introduced to apply our proposed model to various uncertain conditions. In addition, deterministic equivalent transformations are also introduced to obtain the optimal solution efficiently using the scenario-based approach.

Original language	English
Title of host publication	Proceedings - 2017 6th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2017
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	11-16
Number of pages	6
ISBN (Electronic)	9781538606216
DOIs	https://doi.org/10.1109/IIAI-AAI.2017.88
Publication status	Published - 2017 Nov 15
Event	6th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2017 - Hamamatsu, Shizuoka, Japan Duration: 2017 Jul 9 → …

Other

Other	6th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2017
Country/Territory	Japan
City	Hamamatsu, Shizuoka
Period	17/7/9 → …

Keywords

Agricultural supply chain management
Distribution-free approach
Fuzziness
Mathematical programming problem
Randomness

ASJC Scopus subject areas

Artificial Intelligence
Computer Networks and Communications
Computer Science Applications
Information Systems
Information Systems and Management

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10.1109/IIAI-AAI.2017.88

Cite this

Robust Agricultural Supply Chain Management with Various Random and Fuzzy Parameters. / Hasuike, Takashi; Kashima, Tomoko; Matsumoto, Shimpei.

Proceedings - 2017 6th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2017. Institute of Electrical and Electronics Engineers Inc., 2017. p. 11-16 8113204.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Hasuike, T, Kashima, T & Matsumoto, S 2017, Robust Agricultural Supply Chain Management with Various Random and Fuzzy Parameters. in Proceedings - 2017 6th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2017., 8113204, Institute of Electrical and Electronics Engineers Inc., pp. 11-16, 6th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2017, Hamamatsu, Shizuoka, Japan, 17/7/9. https://doi.org/10.1109/IIAI-AAI.2017.88

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abstract = "This paper proposes a robust model of multiperiod agricultural supply chain to consider both maximizing the total profit and minimizing the environmental load with random and fuzzy parameters. Our proposed model is formulated as a fuzzy and stochastic, multiobjective and multiperiod programming problem, and hence, it is hard to solve the formulated problem directly without setting a specific random distribution and a specific membership function. Therefore, a distribution-free approach based on sample mean and variance derived from received data, which does not assume any specific random distributions and membership functions, is introduced to apply our proposed model to various uncertain conditions. In addition, deterministic equivalent transformations are also introduced to obtain the optimal solution efficiently using the scenario-based approach.",

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AU - Kashima, Tomoko

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N2 - This paper proposes a robust model of multiperiod agricultural supply chain to consider both maximizing the total profit and minimizing the environmental load with random and fuzzy parameters. Our proposed model is formulated as a fuzzy and stochastic, multiobjective and multiperiod programming problem, and hence, it is hard to solve the formulated problem directly without setting a specific random distribution and a specific membership function. Therefore, a distribution-free approach based on sample mean and variance derived from received data, which does not assume any specific random distributions and membership functions, is introduced to apply our proposed model to various uncertain conditions. In addition, deterministic equivalent transformations are also introduced to obtain the optimal solution efficiently using the scenario-based approach.

AB - This paper proposes a robust model of multiperiod agricultural supply chain to consider both maximizing the total profit and minimizing the environmental load with random and fuzzy parameters. Our proposed model is formulated as a fuzzy and stochastic, multiobjective and multiperiod programming problem, and hence, it is hard to solve the formulated problem directly without setting a specific random distribution and a specific membership function. Therefore, a distribution-free approach based on sample mean and variance derived from received data, which does not assume any specific random distributions and membership functions, is introduced to apply our proposed model to various uncertain conditions. In addition, deterministic equivalent transformations are also introduced to obtain the optimal solution efficiently using the scenario-based approach.

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Robust Agricultural Supply Chain Management with Various Random and Fuzzy Parameters

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Keywords

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