A stochastic planning framework for the discovery of complementary, agricultural systems

Flores, Héctor; Villalobos, J. René

doi:10.1016/j.ejor.2019.07.053

Cited by 26 publications

(22 citation statements)

References 21 publications

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“…Ref. [87] developed a two-stage stochastic programming model for a local agri-fresh supply chain based on the study of [85]. In the first stage, the selection of agricultural technologies and planting decisions are made.…”

Section: Decision Variablesmentioning

confidence: 99%

“…Refs. [85,87] took into account planting and harvesting windows that affect vegetable yields because of different weather conditions. Different from other studies, [33] proposed an uncertain harvesting time window.…”

Section: Time Windowmentioning

confidence: 99%

“…Refs. [84,87] considered shelf-life restrictions. Instead of including quality changes, [69] incorporated different selling prices according to the remaining shelf life.…”

Section: Perishabilitymentioning

confidence: 99%

“…[78,88] considered investment budget for cultivation, on the other hand, [85,87] considered a budget for farming technology selection. In addition, [87] allowed a limited amount of farming operations. Harvesting related papers used multiple different harvesting resources.…”

Section: Resource Limitationmentioning

confidence: 99%

“…Ref. [87] considered the variability of rainfall, temperature, and market prices in their study. Market prices were also discussed by [56,78].…”

Section: Uncertaintymentioning

confidence: 99%

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Optimization Models for Harvest and Production Planning in Agri-Food Supply Chain: A Systematic Review

Taşkıner

Bilgen

2021

Logistics

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This paper provides a comprehensive review of the research done on optimization models that focus on harvest and production planning for food crops. Optimization models have been used extensively in providing insights to decision-makers on issues related to harvest and production planning in agri-food supply chains. First, we conduct an extensive literature review on previous survey articles to distinguish our research from others. Based on the previous reviews, a new classification scheme is developed to classify articles systematically. Harvest and production planning problems in agri-food supply chains are analyzed through three sections: problem scope, model characteristics, and modeling approach. Neglected problem topics and several promising research directions are presented to stimulate research interest on agri-food supply chains specifically planning of harvest and production.

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Section: Decision Variablesmentioning

confidence: 99%

Section: Time Windowmentioning

confidence: 99%

“…Refs. [84,87] considered shelf-life restrictions. Instead of including quality changes, [69] incorporated different selling prices according to the remaining shelf life.…”

Section: Perishabilitymentioning

confidence: 99%

Section: Resource Limitationmentioning

confidence: 99%

“…Ref. [87] considered the variability of rainfall, temperature, and market prices in their study. Market prices were also discussed by [56,78].…”

Section: Uncertaintymentioning

confidence: 99%

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Optimization Models for Harvest and Production Planning in Agri-Food Supply Chain: A Systematic Review

Taşkıner

Bilgen

2021

Logistics

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Artificial intelligence ‐ driven sustainable development: Examining organizational, technical, and processing approaches to achieving global goals

Kulkov,

Kulkova,

Rohrbeck

et al. 2023

Sustainable Development

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This study presents a comprehensive literature review using a systematic approach to explore the role of artificial intelligence (AI) in promoting sustainable development in line with the United Nations Sustainable Development Goals (SDGs). The systematic review approach was applied to collect and analyze topics, and the literature search was conducted in two stages, encompassing 57 articles that met the research requirements. Our analysis reveals that AI's contribution to sustainability is concentrated within three key areas: organizational, technical, and processing aspects. The organizational aspect focuses on the integration of AI in companies and industries, addressing barriers to implementation and the relationship between companies, partners, and customers. The technical aspect highlights the development of AI algorithms that can address global challenges and contribute to the growth of stability and development in society. The processing aspect emphasizes the internal transformation of companies, their business models, and strategies in response to AI integration. Our proposed conceptual model outlines the essential elements organizations must consider when incorporating AI into their sustainability efforts, such as strategic alignment, infrastructure development, change management, and continuous improvement. By addressing these critical aspects, organizations can harness the potential of AI to drive positive social, environmental, and economic outcomes, ultimately contributing to the achievement of the SDGs. The model serves as a comprehensive framework for organizations seeking to leverage AI for sustainable development, but it should be adapted to individual contexts to ensure its relevance and effectiveness.

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Impact of COVID-19 on Global Supply Chain Management

Manimuthu

Kumar²,

Srinivasa³

2021

Management and Industrial Engineering

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A stochastic planning framework for the discovery of complementary, agricultural systems

Cited by 26 publications

References 21 publications

Optimization Models for Harvest and Production Planning in Agri-Food Supply Chain: A Systematic Review

Optimization Models for Harvest and Production Planning in Agri-Food Supply Chain: A Systematic Review

Artificial intelligence ‐ driven sustainable development: Examining organizational, technical, and processing approaches to achieving global goals

Impact of COVID-19 on Global Supply Chain Management

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