“…This article supports our original research article entitled “Designing a Food Supply Chain Strategy during COVID-19 Pandemic using an Integrated Agent-Based Modelling and Robust Optimization” [1] . While our original research article provides a high-level framework for integrating ABM and RO to address uncertain food supply chain problem with unlimited data availability and its’ result, this article explains how to combine such methods with related data used.…”
Section: Data Descriptionsupporting
confidence: 78%
“… Fig. 2 Polyhedral uncertainty set for uncertain daily rice production volume in Bekasi Regency [1] . …”
Section: Experimental Design Materials and Methodsmentioning
confidence: 99%
“…When data availability is limited, ABM is one of the simulation methods that could be used to feed RO with the required data. ABM is chosen in our original research article as it has the unique ability to represent a system based on the actors and their behaviours, please see our original research article for high-level explanation of ABM & RO integration [1] .…”
This article presents the data as a support for “Designing a Food Supply Chain Strategy during COVID-19 Pandemic using an Integrated Agent-Based Modelling and Robust Optimization” Achmad et al. An integration framework of Agent-Based Modelling (ABM) and Robust Optimization (RO) is proposed to address the food supply network development involving normal and pandemic condition issue regarding the actual food production data availability. In this article, the data associated with the integrated ABM simulation and RO are discussed. Particularly, this article provides the output rice production capacity data from the ABM simulation. This article also discusses how the output data from ABM simulation are processed to construct the polyhedral uncertainty set, which will later used by RO. By showing the output data from the ABM simulation and explaining how it is processed to be used in RO, other researchers and investigators could integrate their own ABM simulation model with RO to address their respective problems considering any uncertainty. Furthermore, the additional data needed for the optimization model are also included, which are mainly retrieved from the reports of government agencies.
“…This article supports our original research article entitled “Designing a Food Supply Chain Strategy during COVID-19 Pandemic using an Integrated Agent-Based Modelling and Robust Optimization” [1] . While our original research article provides a high-level framework for integrating ABM and RO to address uncertain food supply chain problem with unlimited data availability and its’ result, this article explains how to combine such methods with related data used.…”
Section: Data Descriptionsupporting
confidence: 78%
“… Fig. 2 Polyhedral uncertainty set for uncertain daily rice production volume in Bekasi Regency [1] . …”
Section: Experimental Design Materials and Methodsmentioning
confidence: 99%
“…When data availability is limited, ABM is one of the simulation methods that could be used to feed RO with the required data. ABM is chosen in our original research article as it has the unique ability to represent a system based on the actors and their behaviours, please see our original research article for high-level explanation of ABM & RO integration [1] .…”
This article presents the data as a support for “Designing a Food Supply Chain Strategy during COVID-19 Pandemic using an Integrated Agent-Based Modelling and Robust Optimization” Achmad et al. An integration framework of Agent-Based Modelling (ABM) and Robust Optimization (RO) is proposed to address the food supply network development involving normal and pandemic condition issue regarding the actual food production data availability. In this article, the data associated with the integrated ABM simulation and RO are discussed. Particularly, this article provides the output rice production capacity data from the ABM simulation. This article also discusses how the output data from ABM simulation are processed to construct the polyhedral uncertainty set, which will later used by RO. By showing the output data from the ABM simulation and explaining how it is processed to be used in RO, other researchers and investigators could integrate their own ABM simulation model with RO to address their respective problems considering any uncertainty. Furthermore, the additional data needed for the optimization model are also included, which are mainly retrieved from the reports of government agencies.
“…Apart from research on COVID‐19 spread dynamics and evaluation of implementing different interventions, 67 relevant papers investigated the impacts of the pandemic and related NPIs on various sectors. Ten papers investigated the disruptions and uncertainties to the supply chain caused by the COVID‐19 pandemic (Achmad et al, 2021 ; Burgos & Ivanov, 2021 ; Choudhary et al, 2021 ; Duan et al, 2021 ; Ghadge et al, 2021 ; Moosavi & Hosseini, 2021 ; Nguyen, 2021 ; Sinha et al, 2020 ) and the post‐pandemic recovery strategies (Ivanov, 2021 ; Rahman et al, 2021 ). Twenty papers explored the other sectors at the national and regional levels, including industrial network (Song et al, 2020 ), tourism (Gu et al, 2021 ; Luo et al, 2021 ), national security (Prikazchikov et al, 2021 ), food–energy–water (Calder et al, 2021 ), economy (Chen et al, 2021 ; Fosco & Zurita, 2021 ; Inoue et al, 2021 ; Inoue & Todo, 2020 ; Sharma et al, 2021 ), financial (Spelta et al, 2021 ), social activity (de Brito Jr et al, 2021 ; Schmidt & Albert, 2021 ; Weibrecht et al, 2021 ), healthcare (Schlüter et al, 2021 ), employment (Marreros et al, 2021 ) and transport and land‐use (Habib & Anik, 2021 ).…”
This study systematically reviews applications of three simulation approaches, that is, system dynamics model (SDM), agent‐based model (ABM) and discrete event simulation (DES), and their hybrids in COVID‐19 research and identifies theoretical and application innovations in public health. Among the 372 eligible papers, 72 focused on COVID‐19 transmission dynamics, 204 evaluated both pharmaceutical and non‐pharmaceutical interventions, 29 focused on the prediction of the pandemic and 67 investigated the impacts of COVID‐19. ABM was used in 275 papers, followed by 54 SDM papers, 32 DES papers and 11 hybrid model papers. Evaluation and design of intervention scenarios are the most widely addressed area accounting for 55% of the four main categories, that is, the transmission of COVID‐19, prediction of the pandemic, evaluation and design of intervention scenarios and societal impact assessment. The complexities in impact evaluation and intervention design demand hybrid simulation models that can simultaneously capture micro and macro aspects of the socio‐economic systems involved.
“…Nowadays, many research studies focus on the COVID-19 pandemic in relation to food supply chains. For example, Achmad et al [ 16 ] studied robust food supply chain strategies by determining the optimum food hub location and food network to maintain food security, which is robust against disruptions and uncertainties during the COVID-19 pandemic.…”
Food waste is a global problem, which becomes apparent at various stages of the food supply chain. The present research study focuses on the optimization of food consumption in schools and effective food management through data-driven decision making within the trends: zero food waste and digital transformation. The paper presents a plate waste forecasting system based on mathematical modeling and simulation using the Monte Carlo method, which showed an RMSE equal to ±3% and a MAPE of 10.15%. The solution based on the simulator provides a possibility to better understand the relationship between the parameters investigated through data visualization and apply this knowledge to train managers to make decisions that are more effective. The developed system has multi-disciplinary application: forecasting, education and decision making targeted to reduce food waste and improve public health and food management in schools.
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