Planning migrant labor for green sugarcane harvest: A stochastic logistics model with dynamic yield prediction

Amaruchkul, Kannapha

doi:10.1016/j.cie.2020.107016

Cited by 6 publications

(4 citation statements)

References 22 publications

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“…Ref. [24] built a model to maximize return on harvest minus costs, considering migrant seasonal labor planning for sugarcane harvesting and yield uncertainty. The objective of this research was to determine the optimal flows of migrant workers from multiple sources to multiple fronts to maximize the expected total contribution subject to worker availability at each origin through a stochastic logistic model.…”

Section: Related Workmentioning

confidence: 99%

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A Two-Stage Stochastic Linear Programming Model for Tactical Planning in the Soybean Supply Chain

2023

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Background: The soybean market is representative of the world. Brazil is the largest producer and exporter of this crop and has low production costs but high logistical costs, which are influenced mainly by transport costs. Added to these characteristics, the disputed grain supply, the possibility of crop failure, and the randomness of some parameters that influence the soybean supply chain make decisions even more challenging. Methods: To mathematically model this problem, we carried out an analysis of the scientific production related to grain supply chain and the models used to address the problem, as well as a document analysis and a case study. Results: This paper proposes a new two-stage stochastic linear programming model with fixed recourse for tactical planning in the soybean supply chain from the perspective of the shipper under take or pay contracts over a one-year time horizon. The first-stage variables are the grain purchasing decisions and the volumes of rail and road transportation hired in advance. The model addresses 243 scenarios derived from four uncertainty sources: the purchase and sale prices of raw agricultural products on the spot market, the probability of crop failure, and the external demand. Conclusions: The model is successfully applied to a soybean trade firm in Brazil with expected gain of US$4,299,720 when using the stochastic model instead of the deterministic model. The stochastic model protected the firm from take or pay fines and crop failures, contracting a smaller volume of rail transport than what the company does.

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Section: Related Workmentioning

confidence: 99%

“…The following expressions provide for entry input (23), output (24), and storage capacities in warehouses (25). Equation (26) shows that the existing volume in each warehouse during the first period is equal to the initial stock.…”

Section: First Stagementioning

confidence: 99%

A Two-Stage Stochastic Linear Programming Model for Tactical Planning in the Soybean Supply Chain

2023

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“…Labor costs are a significant expense in agricultural production, specifically in the harvesting process [42]. Harvesting often involves migrant seasonal workers and requires recruitment processes that take time and require planning [43]. Furthermore, it is necessary to maintain a balance between ensuring a sufficient number of workers to harvest the ripe products (not missing harvestable produce due to a lack of workers) and ensuring that workers are fully utilized to reduce operational costs.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimization Model for Selective Harvest Planning Performed by Humans and Robots

2022

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This paper addresses the formulation of an individual fruit harvest decision as a nonlinear programming problem to maximize profit, while considering selective harvesting based on fruit maturity. A model for the operational level decision was developed and includes four features: time window constraints, resource limitations, yield perishability, and uncertainty. The model implementation was demonstrated through numerical studies that compared decisions for different types of worker and analyzed different robotic harvester capabilities for a case study of sweet pepper harvesting. The results show the influence of the maturity classification capabilities of the robot on its output, as well as the improvement in cycle times needed to reach the economic feasibility of a robotic harvester.

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“…Although causes, effects and policy solutions are indicated by these reports, root causes are not addressed. For instance, mechanical harvesting with financial assistance from the government is recommended, but problems about modes of engagement and biophysical barriers to mechanization are unaddressed [ 20 ]. Ineffectiveness of policies solving the burning problem of agricultural residues is indicated [ 13 ].…”

Section: Introductionmentioning

confidence: 99%

Reframing the wicked problem of pre-harvest burning: A case study of Thailand's sugarcane

Chaya

2024

Heliyon

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Planning migrant labor for green sugarcane harvest: A stochastic logistics model with dynamic yield prediction

Cited by 6 publications

References 22 publications

A Two-Stage Stochastic Linear Programming Model for Tactical Planning in the Soybean Supply Chain

A Two-Stage Stochastic Linear Programming Model for Tactical Planning in the Soybean Supply Chain

Optimization Model for Selective Harvest Planning Performed by Humans and Robots

Reframing the wicked problem of pre-harvest burning: A case study of Thailand's sugarcane

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