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

Hector Flores, Jesus Villalobos

Research output: Contribution to journalArticle

Abstract

One of the greatest 21st century challenges is meeting the need to feed a growing world population which is expected to increase by about 35% by 2050. To meet this challenge, it is necessary to make major improvements on current food production and distribution systems capabilities, as well as to adapt these systems to expected trends such as climate change. Changing climate patterns may present opportunities for unidentified, geographical regions with adequate climate patterns to produce high-value agricultural products in a profitable and sustainable manner. This paper focuses on the design and planning aspects of a discovery process to unearth agri-food supply chains capable of generating attractive return on investments. A stochastic optimization framework is used to develop planting and harvesting schedules for a set of identified regions with complementary weather characteristics. To address the high-level of variability in the problem context, a two-stage stochastic decomposition method is used to consider a larger number of scenarios. As part of the solution process, a modeling scheme is developed that learns past interactions between entering discretized, weather scenarios and optimal first-stage solutions. In this context, machine learning and dimensionality reduction techniques are used to iteratively estimate each region's probability of belonging to first-stage solutions based on previous solution-scenario results. The implementation of the stochastic framework is shown through a case study applied to multiple locations within the US southwest states of Arizona and New Mexico.

Original languageEnglish (US)
Pages (from-to)707-729
Number of pages23
JournalEuropean Journal of Operational Research
Volume280
Issue number2
DOIs
StatePublished - Jan 16 2020

Fingerprint

Planning
Climate
Weather
Scenarios
Stochastic Decomposition
Food Chain
Stochastic Optimization
Distribution System
Stochastic Methods
Climate Change
Harvesting
Dimensionality Reduction
Production Systems
Food supply
Decomposition Method
Geographical regions
Agricultural products
Supply Chain
Machine Learning
Schedule

Keywords

  • Decision support systems
  • OR in agriculture
  • Stochastic decomposition algorithm
  • Supply chain management
  • Two-stage stochastic model

ASJC Scopus subject areas

  • Computer Science(all)
  • Modeling and Simulation
  • Management Science and Operations Research
  • Information Systems and Management

Cite this

A stochastic planning framework for the discovery of complementary, agricultural systems. / Flores, Hector; Villalobos, Jesus.

In: European Journal of Operational Research, Vol. 280, No. 2, 16.01.2020, p. 707-729.

Research output: Contribution to journalArticle

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