Use of MIP for planning temporary immigrant farm labor force

Wishon, Christopher; Villalobos, J. René; Mason, Nicholas; Flores, Héctor; Luján, Graciela del Carmen Sandoval

doi:10.1016/j.ijpe.2015.09.004

Cited by 17 publications

(18 citation statements)

References 27 publications

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“…The use of quantitative methods to address planning for a FPSC is well founded within the current literature (Ahumada and Villalobos, 2009, Zhang and Wilhelm, 2011, Shukla and Jharkharia, 2013, Wishon et al, 2015. The objective of this research is to help a SC manager explore the trade-offs of different disruption management strategies.…”

Section: Methodsmentioning

confidence: 99%

Modeling disruption in a fresh produce supply chain

MacKenzie

Apte

2017

IJLM

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Purpose-The purpose of this paper is to quantify elements that make fresh produce supply chains vulnerable to disruptions and to quantify the benefits of different disruption management strategies.Design/methodology/approach-This paper develops a mathematical model of a disruption in a fresh produce supply chain and analyzes the relationships among variables. Findings-The model determines the optimal safety stock as a function of the perishability of the produce, the length of time it takes to find the contamination, the level of demand during the disruption, and the amount of produce that can be rerouted. Applying the model to the 2006 E. coli spinach contamination reveals that the drop in customer demand for fresh spinach plays the largest role in Dole losing sales.Research limitations-The model includes several parameters that may be difficult to estimate.Future models can incorporate uncertainty that is inherent in supply chain disruptions. 2Practical implications-The model in this paper can help a supply chain manager explore the trade-offs of different disruption management strategies. For example, a supply chain manager can determine the value of holding additional safety stock versus trying to improve traceability in the supply chain.Originality/value-This paper quantifies and models insights delivered in the qualitative analyses of fresh produce supply chain disruptions. The theoretical contributions include an analysis of the interaction among safety stock, levels of demand, communication, and traceability parameters in order to help supply chain managers evaluate different strategies to mitigate the effects of contaminated produce.3

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Section: Methodsmentioning

confidence: 99%

Modeling disruption in a fresh produce supply chain

MacKenzie

Apte

2017

IJLM

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“…For the data used in this case study, we have used a dataset from Wishon et al (2015). The information used was representative of a typical farm from the agricultural region of Yuma, AZ, in the Southwestern United States and the study considered four distinct crops (broccoli, iceberg lettuce, romaine lettuce and cauliflower).…”

Section: Definition Of Parametersmentioning

confidence: 99%

“…However, the variety of farmers and farmers' parameters was a feature which was not captured in the original study of Wishon et al (2015), which only modeled a "typical farm". Unfortunately, this creates an issue since the mechanism has to be tested under the setting of multiple, heterogeneous farmers; yet, efficiently capturing such information is a non-trivial task and may require an extensive amount of resources.…”

Section: Definition Of Parametersmentioning

confidence: 99%

Coordination of perishable crop production using auction mechanisms

Mason

Villalobos

2015

Agricultural Systems

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“…In addition to the model described above, we will provide examples for two expansions to this model; one expansion for making tactical decisions under uncertainty (Ahumada et al 2012) and one for making labor allocation decisions for the production of four crops, without distribution decisions (Wishon et al 2012). The use of these models will allow us to better illustrate the specifics expected from these models when applied to real-world cases.…”

Section: Planning the Planting And Harvesting (Grower Perspective)mentioning

confidence: 99%

“…The best planting schedule is determined by using the model obtained in Wishon et al (2012), for creating a planting and hiring schedule (Table 2.2). However, for this model, we assume that there is a given demand for each product that must be met and that the availability of workers is constant throughout the season.…”

Section: Planning the Planting And Harvesting (Grower Perspective)mentioning

confidence: 99%