Simulation-Based Decision Support For Agricultural Supply Chain Performance Improvement

Persistent Link:
http://hdl.handle.net/10150/581318
Title:
Simulation-Based Decision Support For Agricultural Supply Chain Performance Improvement
Author:
Meng, Chao
Issue Date:
2015
Publisher:
The University of Arizona.
Rights:
Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
Abstract:
Grafted vegetable seedlings have been proven to possess higher seed/non-seed diseases resistance and yields compared with non-grafted ones. Owing to the seasonality of vegetable planting and labor intensiveness of grafted seedling production (e.g., grafting operation), U.S. vegetable seedling supply chains suffer from high grafted seedling cost. To make grafted seedlings affordable for vegetable growers, low-cost production systems and cost-efficient grafting capacity must be achieved via optimal design of a grafting operation system and supply chain collaboration, respectively. Toward this end, a two-level simulation-based framework is proposed in this work for improving the overall performance of the grafted seedling supply chain by supporting both the grafted seedling production system design and supply chain collaboration decisions. The considered supply chain consists of a single grafted seedling producer that produces grafted seedlings and multiple vegetable growers that seasonally purchase grafted seedlings and produce vegetables to meet price-sensitive demand from the downstream market. More specifically, the low level of the proposed framework focuses on the grafted seedling production system design by integrating discrete event simulation (DES) together with a fuzzy analytic hierarchy process (AHP) for multiple criteria (i.e. production cost, capital investment, production throughput time, resource utilization, and product quality). A Unified Modeling Language (UML)-based simulation modeling and generation approach is developed to automatically generate simulation models of various production system design alternatives. UML information models are developed to provide the system structural information for simulation model generation, production information for simulation execution, and output requirement information for defining simulation outputs. The performance of the production system design alternatives for the aforementioned criteria is evaluated via the generated simulation models, and the corresponding simulation results together with decision makers' judgments on the criteria are used to select the best system design via AHP. A best alternative search (BAS) procedure is proposed for the adopted AHP approach to search for the best system design against ranking impreciseness caused by simulation randomness. At the high level, the proposed framework focuses on the optimal supply chain decisions for early order commitment (EOC) to reduce the amortized production capacity cost. EOC is a supply chain collaboration mechanism, where the grafted seedling producer encourages the vegetable growers to commit their orders earlier than their regular ordering times by providing certain benefits (e.g., price discount). Based on the optimal design of a grafted seedling production system and the corresponding production cost obtained at the low level, we first derive analytical solutions for the grafted seedling producer's optimal capacity, vegetable grower's optimal order quantity, and ordering time under a basic supply chain structure (i.e., single-seedling producer and single-vegetable grower). We then introduce capacity competition by extending the basic structure to a multi-vegetable grower structure. The existence of the N-person game equilibrium and the corresponding relationships between the grafted seedling producer's profit and the vegetable growers' early order decisions are provided. In addition, a capacity reservation mechanism is proposed for the seedling producer to motivate the vegetable growers to release order information in advance. To identify the convergence of the vegetable growers' ordering times, a Cellular Automata simulation model is developed, where each vegetable grower is modeled as a Pavlovian or greedy agent making an ordering time decision so as to receive the higher profit over iterations. The proposed framework is demonstrated for grafted seedling supply chains in North America. The experiment results reveal the benefits of the proposed framework in reducing the grafted seedling cost, as well as in increasing the entire supply chain's profit.
Type:
text; Electronic Dissertation
Keywords:
Systems & Industrial Engineering
Degree Name:
Ph.D.
Degree Level:
doctoral
Degree Program:
Graduate College; Systems & Industrial Engineering
Degree Grantor:
University of Arizona
Advisor:
Son, Young-Jun

Full metadata record

DC FieldValue Language
dc.language.isoen_USen
dc.titleSimulation-Based Decision Support For Agricultural Supply Chain Performance Improvementen_US
dc.creatorMeng, Chaoen
dc.contributor.authorMeng, Chaoen
dc.date.issued2015en
dc.publisherThe University of Arizona.en
dc.rightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.en
dc.description.abstractGrafted vegetable seedlings have been proven to possess higher seed/non-seed diseases resistance and yields compared with non-grafted ones. Owing to the seasonality of vegetable planting and labor intensiveness of grafted seedling production (e.g., grafting operation), U.S. vegetable seedling supply chains suffer from high grafted seedling cost. To make grafted seedlings affordable for vegetable growers, low-cost production systems and cost-efficient grafting capacity must be achieved via optimal design of a grafting operation system and supply chain collaboration, respectively. Toward this end, a two-level simulation-based framework is proposed in this work for improving the overall performance of the grafted seedling supply chain by supporting both the grafted seedling production system design and supply chain collaboration decisions. The considered supply chain consists of a single grafted seedling producer that produces grafted seedlings and multiple vegetable growers that seasonally purchase grafted seedlings and produce vegetables to meet price-sensitive demand from the downstream market. More specifically, the low level of the proposed framework focuses on the grafted seedling production system design by integrating discrete event simulation (DES) together with a fuzzy analytic hierarchy process (AHP) for multiple criteria (i.e. production cost, capital investment, production throughput time, resource utilization, and product quality). A Unified Modeling Language (UML)-based simulation modeling and generation approach is developed to automatically generate simulation models of various production system design alternatives. UML information models are developed to provide the system structural information for simulation model generation, production information for simulation execution, and output requirement information for defining simulation outputs. The performance of the production system design alternatives for the aforementioned criteria is evaluated via the generated simulation models, and the corresponding simulation results together with decision makers' judgments on the criteria are used to select the best system design via AHP. A best alternative search (BAS) procedure is proposed for the adopted AHP approach to search for the best system design against ranking impreciseness caused by simulation randomness. At the high level, the proposed framework focuses on the optimal supply chain decisions for early order commitment (EOC) to reduce the amortized production capacity cost. EOC is a supply chain collaboration mechanism, where the grafted seedling producer encourages the vegetable growers to commit their orders earlier than their regular ordering times by providing certain benefits (e.g., price discount). Based on the optimal design of a grafted seedling production system and the corresponding production cost obtained at the low level, we first derive analytical solutions for the grafted seedling producer's optimal capacity, vegetable grower's optimal order quantity, and ordering time under a basic supply chain structure (i.e., single-seedling producer and single-vegetable grower). We then introduce capacity competition by extending the basic structure to a multi-vegetable grower structure. The existence of the N-person game equilibrium and the corresponding relationships between the grafted seedling producer's profit and the vegetable growers' early order decisions are provided. In addition, a capacity reservation mechanism is proposed for the seedling producer to motivate the vegetable growers to release order information in advance. To identify the convergence of the vegetable growers' ordering times, a Cellular Automata simulation model is developed, where each vegetable grower is modeled as a Pavlovian or greedy agent making an ordering time decision so as to receive the higher profit over iterations. The proposed framework is demonstrated for grafted seedling supply chains in North America. The experiment results reveal the benefits of the proposed framework in reducing the grafted seedling cost, as well as in increasing the entire supply chain's profit.en
dc.typetexten
dc.typeElectronic Dissertationen
dc.subjectSystems & Industrial Engineeringen
thesis.degree.namePh.D.en
thesis.degree.leveldoctoralen
thesis.degree.disciplineGraduate Collegeen
thesis.degree.disciplineSystems & Industrial Engineeringen
thesis.degree.grantorUniversity of Arizonaen
dc.contributor.advisorSon, Young-Junen
dc.contributor.committeememberSon, Young-Junen
dc.contributor.committeememberKubota, Chierien
dc.contributor.committeememberLiu, Jianen
dc.contributor.committeememberTronstad, Russellen
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