Predictive quality-aware control for scheduling of potato starch production

Xiang, Lin; Negenborn, Rudy R.; Lodewijks, Gabriël

doi:10.1016/j.compag.2018.04.020

Cited by 6 publications

(5 citation statements)

References 28 publications

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“…Optimization of cold supply chains, focused on fresh foods in general, has received attention from the scientific community in recent years (e.g., [12], [7], [13], [14], [15], [16], [17]). Quality degradation of food in combination with minimizing costs is considered in [18].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Constraint (15) makes it possible to connect the demand and supply of nodes i with decision variable u mi j . Constraint (16) shows in which ways demand d i (k) can be fulfilled.…”

Section: Logistic Aspectsmentioning

confidence: 99%

“…The term g i specifies the demand can be fulfilled while the term g C i denotes the number of units of meat within the demand that can not be fulfilled. Constraint (17) links the supply, which can be determined using (15), to the demand.…”

Section: Logistic Aspectsmentioning

confidence: 99%

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Quality-Aware Control for Optimizing Meat Supply Chains

Sprong

Xiang

Maestre

et al. 2019

2019 18th European Control Conference (ECC)

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Controlling food quality and reducing waste is one of the most challenging tasks in the food industry, as it is facing high rates of wastage, leading to negative environmental impact. This research focuses on improving the scheduling and control of the supply chain of Irish lamb meat using real-time quality and temperature information. Temperature controlled reefers and sensor technologies can be used to monitor and set the temperature during transport and storage. In order to minimize waste, while at the same time optimizing cooling and transport costs, a mathematical model is proposed. The model consists of two aspects. The quality aspect considers the shelf life of Irish lamb which is related to temperature. The logistic aspect considers supply chain scheduling. With this model, a strategy is proposed to determine the movements of meat, as well as the temperature setting of cooling equipment. Results of simulation experiments indicate a sustainable approach can reduce or even eliminate waste and decrease operational costs when real-time monitoring and control is used.

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Section: Literature Reviewmentioning

confidence: 99%

“…Constraint (15) makes it possible to connect the demand and supply of nodes i with decision variable u mi j . Constraint (16) shows in which ways demand d i (k) can be fulfilled.…”

Section: Logistic Aspectsmentioning

confidence: 99%

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Quality-Aware Control for Optimizing Meat Supply Chains

Sprong

Xiang

Maestre

et al. 2019

2019 18th European Control Conference (ECC)

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“…Planning for harvest is crucial for determining capacity for product transportation and storage activities (Khalilzadeh and Wang, 2021). Real-time techniques have been established about when and how much product will be harvested, stored, and processed in order to preserve the post-harvest product quality for perishable food goods (Lin et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Precipitation forecast with logistics regression methods for harvest optimization

SAMASTI¹,

Küçükdeniz

2023

International Journal of Agriculture Environment and Food Sciences

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This paper proposes a model that forecasts the weather and then, based on that forecast, uses an income-oriented linear programming method to optimize the harvesting process. Data representing a total yearly output capacity of 472,878 tons from 214 different field locations were used to test the model for sugar beet production. Prior to optimization, long-term one-year weather rainfall forecasting was done using 10 years of actual weather data for the field locations. Weather precipitation was forecasted using logistic regression with an accuracy of 84.16%. The outcome of the weather precipitation prediction model was a parameter in the optimization model. The weather forecast for precipitation led to the 120-day harvest planning being optimized. Comparative analysis was done on the outcomes of the developed model and the current scenario. Comparing the current situation to the proposed one, revenue would have increased by 16.7%. Given that it incorporates weather forecasts into the harvest optimization process, the methodology presented in this paper is more practical than other harvest optimization models.

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“…This kind of real‐time decision making can help by gaining more returns on the stock as well as save food losses due to wastage of food after it's expiry date. This kind of predictive approach in supply chain management has been studied by different researchers (B. Fu & Labuza, 1993; Koutsoumanis & Nychas, 2000; Lin, Negenborn, & Lodewijks, 2018; Rong, Akkerman, & Grunow, 2011). For example, A study by Lin et al (2018) aims to minimize the starch loss by deciding on whether to harvest new potatoes or to store them or to send existing ones for starch production based on the real time quality of potatoes.…”

Section: Introductionmentioning

confidence: 99%

Temperature and humidity based models for the prediction of transpiration rate in potatoes during storage

Kedia

Kausley

Rai

2020

J Food Process Engineering

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Transpiration of potatoes causes shrinkage, loss of skin firmness, and reduction in sellable weight leading to reduced returns on the stock. The knowledge about the transpiration rate of potatoes stored or being transported at different environmental conditions is necessary for predicting the weight of stock after a given period of time. In this study, models incorporating the effect of two most influential yet easily measurable parameters; temperature and humidity on transpiration rate were developed using well‐known Arrhenius equation. Since standard Arrhenius equation considers the effect of only temperature, we have employed Modified Arrhenius equations which incorporate the effect of both temperature and humidity. The experimental weight versus time data generated by storing the potatoes at different environmental conditions was used to develop these models with Root Mean Square Error of 0.13–0.32 g/kg day. These models predicted the weight of potatoes stored at different environmental conditions belonging to different potato varieties with Mean Absolute Percentage Error of 0.13–5.5. The study also presents a detailed analysis of transpiration models already available in the literature and compares them with the current model. The models developed in this study will help in making real‐time decisions to avoid food wastage in the supply chain. Practical applications It is well established that weight loss occuring due to transpiration leads to degradation of potato quality. During the movement in the supply chain, potatoes may experience varying environmental conditions which influence their rate of transpiration. The knowledge about the transpiration rate of potatoes stored or being transported is necessary for predicting the weight of stock after a given period. This can further aid in decisions regarding the sale and repurposing of the stock to minimize the wastage of potatoes. In addition to this, the knowledge about the transpiration rate is important for designing Modified Atmosphere Packaging (MAP) where the water vapor transmission rate of the packaging material is influenced by the transpiration rate.

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Predictive quality-aware control for scheduling of potato starch production

Cited by 6 publications

References 28 publications

Quality-Aware Control for Optimizing Meat Supply Chains

Quality-Aware Control for Optimizing Meat Supply Chains

Precipitation forecast with logistics regression methods for harvest optimization

Temperature and humidity based models for the prediction of transpiration rate in potatoes during storage

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