Sustainable Agri-Food Supply Chain with Uncertain Demand and Lead Time

Galal, Noha M.; El-Kilany, Khaled S.

doi:10.2507/ijsimm15(3)8.350

Cited by 39 publications

(9 citation statements)

References 21 publications

(34 reference statements)

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“…Rijpkema et al (2014) differentiate between two lead times (corresponding to regular and expedited orders) with static characteristics. Galal & El-Kilany (2016) have considered a level of uncertainty expressed as a stochastic lead time for otherwise unconstrained yield from the supplier.…”

Section: Modelling Uncertaintymentioning

confidence: 99%

Proceedings of SW21 The OR Society Simulation Workshop

2023

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Simulation, and now digital twins, excel in estimating parameters associated with complex and timedependent stochastic processes, e.g., large airport, highway, and warehouse operations. Within such contexts, we consider statistical inference, whereby one seeks to quantify the error in the obtained simulation estimate. Historically, statistical inference in simulation has been considered challenging because parameters needing estimation are often complicated, and simulation output often autocorrelated and non-normal. However, we argue that the remarkably simple idea of batching can be used as an "omnibus" inference device in simulation settings. Batching for inference works in three simple steps: (i) divide simulation output data into overlapping batches; (ii) construct parameter estimates from each batch; and (iii) use the batch estimates, after accounting for dependence, to perform statistical inference. As we describe in this paper, the resulting procedures are usually trivial to implement in software, and are provably correct and efficient. Batching ideas originated in the 1950s, and have enjoyed steady development in the simulation community since the 1970s mostly within the problem of variance estimation. Our thesis is that batching ideas have much wider utility, and that batching should be considered alongside other resampling ideas such as the bootstrap or the jackknife in modern statistics.

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Section: Modelling Uncertaintymentioning

confidence: 99%

Proceedings of SW21 The OR Society Simulation Workshop

2023

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“…The online control of continuous processes also requires dynamic models for unavoidable noise compensation, like the variations in raw materials supply and quality. Galal and El-Kilany (2016) study the effect of changing the order quantity in a two-echelon fresh food supply chain on costs, emissions, and service level using a discrete-event simulation model and including stochastic demand and lead-time, the amount of CO2 emissions along the supply chain.…”

Section: Demand Side Managementmentioning

confidence: 99%

Dynamic simulation driven design and management of production facilities in agricultural/food industry

Bianchi¹,

Catalano²,

Oliveto³

et al. 2021

Acta Hortic.

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An industrial plant in the agro-food sector can be considered a complex system as it is composed of numerous types of machines and it is characterized by a strong variation (seasonality) in the agricultural production. Whenever the dynamic behavior of the plants during operation is considered, system and design complexities increase. Reliable operation of food processing farms is primarily dependent on perfect balance between variable supply and product storage at each given time. To date, the classical modus operandi of food processing management systems is carried out under stationary and average conditions. Moreover, most of the systems installed for agricultural and food industries are sized using average production data. This often results in a mismatch between the actual operation and the expected operation. Consequently, the system is not optimized for the needs of a specific company. Also, the system is not flexible to the evolution that the production process could possibly have in the future. Promising techniques useful to solve the above-described problems could possibly be borrowed from demand side management (DSM) in smart grid systems. Such techniques allow customers to make dynamically informed decisions regarding their energy demand and help the energy providers in reducing the peak load demand and reshape the load profile. DSM is successfully used to improve the energy management system and we conjecture that DSM could be suitably adapted to food processing management. In this paper we describe how DSM could be exploited in the intelligent management of production facilities serving agricultural and food industry. The main objective is, indeed, to present how methods for modelling and implementing the dynamic simulation used for the optimization of the energy management in smart grid systems can be applied to a fruit and vegetables processing plant through a suitable adaptation.

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“…2.1 Agro-food supply chain network design Galal and El-Kilany (2016) studied the results of varying order quantity in agro-supply chain. In the study, a simulation model was established to involve stochastic demand and time.…”

Section: Literature Reviewmentioning

confidence: 99%

Meta-heuristic algorithms for solving the sustainable agro-food grain supply chain network design problem

Dwivedi

Jha

Prajapati

et al. 2020

MSCRA

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PurposeDue to unceasing declination in environment, sustainable agro-food supply chains have become a topic of concern to business, government organizations and customers. The purpose of this study is to examine a problem associated with sustainable network design in context of Indian agro-food grain supply chain.Design/methodology/approachA mixed integer nonlinear programming (MINLP) model is suggested to apprehend the major complications related with two-echelon food grain supply chain along with sustainability aspects (carbon emissions). Genetic algorithm (GA) and quantum-based genetic algorithm (Q-GA), two meta-heuristic algorithms and LINGO 18 (traditional approach) are employed to establish the vehicle allocation and selection of orders set.FindingsThe model minimizes the total transportation cost and carbon emission tax in gathering food grains from farmers to the hubs and later to the selected demand points (warehouses). The simulated data are adopted to test and validate the suggested model. The computational experiments concede that the performance of LINGO is superior than meta-heuristic algorithms (GA and Q-GA) in terms of solution obtained, but there is trade-off with respect to computational time.Research limitations/implicationsIn literature, inadequate study has been perceived on defining environmental sustainable issues connected with agro-food supply chain from farmer to final distribution centers. A MINLP model has been formulated as practical scenario for central part of India that captures all the major complexities to make the system more efficient. This study is regulated to agro-food Indian industries.Originality/valueThe suggested network design problem is an innovative approach to design distribution systems from farmers to the hubs and later to the selected warehouses. This study considerably assists the organizations to design their distribution network more efficiently.

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Sustainable Agri-Food Supply Chain with Uncertain Demand and Lead Time

Cited by 39 publications

References 21 publications

Proceedings of SW21 The OR Society Simulation Workshop

Proceedings of SW21 The OR Society Simulation Workshop

Dynamic simulation driven design and management of production facilities in agricultural/food industry

Meta-heuristic algorithms for solving the sustainable agro-food grain supply chain network design problem

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