Designing a food supply chain strategy during COVID-19 pandemic using an integrated Agent-Based Modelling and Robust Optimization

Towards facing uncertainties in biofuel supply chain networks: a systematic literature review

Biofuel supply chains (BSCs) face diverse uncertainties that pose serious challenges. This has led to an expanding body of research focused on studying these challenges. Hence, there is a growing need for a comprehensive review that summarizes the current studies, identifies their limitations, and provides essential advancements to support scholars in the field. To overcome these limitations, this research aims to provide insights into managing uncertainties in BSCs. The review utilizes the Systematic Reviews and Meta-Analyses (PRISMA) method, identifying 205 papers for analysis. This study encompasses three key tasks: first, it analyses the general information of the shortlisted papers. Second, it discusses existing methodologies and their limitations in addressing uncertainties. Lastly, it identifies critical research gaps and potential future directions. One notable gap involves the underutilization of machine learning techniques, which show potential for risk identification, resilient planning, demand prediction, and parameter estimations in BSCs but have received limited attention. Another area for investigation is the potential of agent-based simulation, which can contribute to analysing resilient policies, evaluating resilience, predicting parameters, and assessing the impact of emerging technologies on BSC resilience in the twenty-first century. Additionally, the study identifies the omission of various realistic assumptions, such as backward flow, lateral transshipments, and ripple effects in BSC. This study highlights the complexity of managing uncertainties in BSCs and emphasizes the need for further research and attention. It contributes to policymakers' understanding of uncertain sources and suitable approaches while inspiring researchers to address limitations and generate breakthrough ideas in managing BSC uncertainties.

Robust design of a logistics system using FePIA procedure and analysis of trade-offs between CO2 emissions and net present value

The problems of flexible planning of the design of logistics systems for the collection of food products such as raw milk can result in a decrease in the performance of critical indicators for their performance. This paper proposes a new efficient methodology for robustly designing a first-mile logistics system for storing and refrigerating milk as a perishable product considering decisions related to open facilities and the flow of products, including sustainability indices. The proposed approach is modeled as a bi-objective problem by considering the minimization of greenhouse gas emissions (CO2) produced by milk transportation canteens and the maximization of the system configuration's net present value (NPV). We have analyzed and determined the most robust configuration for the first time and explained the robustness-NPV and robustness-CO2 relationships. The proposed mathematical model is solved by the Epsilon constraints method, and the robustness is calculated considering an extension of the FePIA methodology for multiobjective problems. A novel contribution is a balance in the possible future values generated by the company related to its cash flows and the generation of CO2 emissions when using a motorized transport frequently used in the shipment of raw milk considering a new important aspect such as the volume of product transported and the slope of the path between the production farm and the storage cooling tanks.

Effects of COVID-19 outbreak in pricing and collaboration of a health-social dual-channel supply chain

The coronavirus pandemic has affected most aspects of product supply and consumer behaviors and led to transformations in the supply chain. The COVID-19 pandemic and the requirements to reduce its prevalence have led many people to shop online and encouraged many manufacturers to sell their products online. In this study, a manufacturer, who intends to possess an online sales channel, and a retailer, who has an in-person sales channel, are considered. Then, pricing strategies and collaboration mechanisms between them in the health-social dual-channel supply chain are investigated. This study is developed in three models, including centralized, decentralized, and collaborated under Stackelberg game, whereas the optimal price of products in each channel, level of implementation of health and safety protocols in retailers, advertising level, and status of online shopping performance are obtained for improving customer trust. Moreover, the demand is represented as a function of selling prices of products in online and in-person shops, compliance level of health protocols, level of online shopping performance, and advertising in health during the COVID-19 pandemic. Although the centralized model provides more profit for the manufacturer, the collaborated model provides the highest profit for the retailer. Thus, since the supply chain profit of centralized and collaborated models is close, the collaboration model is the best option for members in this situation. Sensitivity analysis is finally performed to evaluate the impact of key parameters, and then according to obtained results, some management insights are suggested for the dual-channel supply chain during the COVID-19 pandemic.

Optimization of food-energy-water-waste nexus in a sustainable food supply chain under the COVID-19 pandemic: a case study in Iran

Abstract

During the coronavirus epidemic, food supply chains have been affected by disruptions of this epidemic. Having an overview and considering food-related factors such as energy, waste, and water make the design of the supply chain more effective. In this study, a multi-objective model for a sustainable food supply chain is presented based on the prevalence of coronavirus by considering the food-energy-water-waste nexus. There are dual-channel sales including online and in-person channels, which their demand functions depend on products price in every channel, as well as the prevalence of coronavirus in high-risk or low-risk areas. Distribution centers, located in high-risk regions, require the disinfection of products to minimize coronavirus spread. Indeed, the proposed mathematical model has three objective functions that the first objective maximizes the profit of the food supply under COVID-19 conditions. The other two objectives minimize not only the environmental impact of transportation, but also the delivery time. In order to solve this multi-objective model, the epsilon constraint method, as an efficient technique, is employed. To validate the proposed model, the model is finally implemented in a two-channel supply chain in Iran. The results show that the food supply chain has a close relationship with the energy-water-waste nexus COVID-19 and can also affect customers' behavior. Moreover, the model shows when the prevalence of COVID-19 increases, people tend to buy from online shops, affecting product prices that can change by nearly 50%.

Graphical abstract

Exploring the Role of Industry 4.0 and Simulation as a Solution to the COVID-19 Outbreak: a Literature Review

The COVID-19 pandemic was an unexpected and disruptive event that significantly affected the performance of manufacturing systems and supply chains in various sectors. In this paper, a literature review is provided, which investigates the role that Industry 4.0 technologies and simulation tools have played in addressing the effects of the pandemic crisis. Specifically, a bibliometric analysis provides an overview of the most influential technologies through a study of the most used keywords. While a document analysis, conducted on critical papers that concern real case studies, shows that so far simulation provided support in four main areas: energy consumption, healthcare supply chain & contact tracing, food supply chain, and in general supply chain management. The main outcome of this research work is that Industry 4.0 technologies and simulation models were particularly important during the pandemic crisis and their properties deserve to be deeply exploited in the near future.

An agent-based modeling framework for the design of a dynamic closed-loop supply chain network

The supply chain is a dynamic and uncertain system consisting of material, information, and fund flows between different organizations, from the acquisition of the raw materials to the delivery of the finished products to the end customers. Closed-loop supply chains do not end with the delivery of the finished products to the end customers, the process continues until economic value is obtained from the returned products or they are disposed properly in landfills. Incorporating reverse flows in supply chains increases the uncertainty and complexity, as well as complicating the management of supply chains that are already composed of different actors and have a dynamic structure. Since agent-based modeling and simulation is a more efficient method of handling the dynamic and complex nature of supply chains than the traditional analytical methods, in this study agent-based modeling methodology has been used to model a generic closed-loop supply chain network design problem with the aims of integrating customer behavior into the network, coping with the dynamism, and obtaining a more realistic structure by eliminating the required assumptions for solving the model with analytical methods. The actors in the CLSC network have been defined as agents with goals, properties and behaviors. In the proposed model dynamic customer arrivals, the changing aspects of customers' purchasing preferences for new and refurbished products and the time, quantity and quality uncertainties of returns have been handled via the proposed agent-based architecture. To observe the behavior of the supply chain in several conditions various scenarios have been developed according to different parameter settings for the supplier capacities, the rate of customers being affected by advertising, the market incentive threshold values, and the environmental awareness of customers. From the scenarios, it has been concluded that the system should be fed in the right amounts for the new and refurbished products to increase the effectiveness of factors such as advertising, incentives, and environmental awareness for achieving the desired sales amounts and cost targets.

Using simulation modelling and systems science to help contain COVID-19: A systematic review

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.

A Novel Dynamic Approach for Risk Analysis and Simulation Using Multi-Agents Model

Static risk analysis techniques (SRATs) use event graphs and risk analysis assessment models. Those techniques are not time-based techniques and hence are inadequate to model dynamic stochastic systems. This paper proposes a novel dynamic approach to model such stochastic systems using Dynamic Fault Trees (DFT). The proposed model is called Generic Dynamic Agent-Based Model (GDABM) for risk analysis. GDABM is built on top of the well-known Agent-Based Modeling and Simulation (ABMS) technique. GDABM can model the dynamic system agents in both nominal (failure-free) and degraded (failure) modes. GDABM shows the propagation of failure between system elements and provides complete information about the system’s configurations. In this paper, a complete detailed case study is provided to show the GDABM capabilities to model and study the risk analysis for such dynamic systems. In the case study, the GDABM models the risk analysis for a chemical reactor/operator and performs a complete risk analysis for the entire system. The GDABM managed to simulate the dynamic behavior of the system’s components successfully using Repast Simphony 2.0. Detailed agent behavioral modes and failure modes are provided with various scenarios, including different time stamps. The proposed GDABM is compared to a reference model. The reference model is referred to as the ABM model. GDABM has given very promising results. A comparison study was performed on three performance measures. The performance measures used are (1) Accuracy, (2) response time, and (3) execution time. GDABM has outperformed the reference model by 15% in terms of accuracy and by 27% in terms of response time. GDABM incurs a slightly higher execution time (13%) when compared to the ABM reference model. It can be concluded that GDABM can deliver accepted performance in terms of accuracy and response time without incurring much processing overhead.

Plate Waste Forecasting Using the Monte Carlo Method for Effective Decision Making in Latvian Schools

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.

A Game-Theoretic Approach for CSR Emergency Medical Supply Chain during COVID-19 Crisis

The COVID-19 pandemic has caused high fluctuations in the demand for medical supplies. Therefore, emergency medical supplies enterprises have faced challenges in decision making and need to consider more corporate social responsibility (CSR) in production. At the same time, the government needs to take considerable measures to support emergency medical supplies enterprises. As such, our paper researches the decision and coordination problems for emergency medical supply chain considering CSR with the government, manufacturer, and retailer. The manufacturer produces emergency medical supplies. It has additional production technological innovation efforts to improve supply efficiency and assume CSR. The retailer faces uncertain demands and is responsible for undertaking CSR to meet the demands. The government must implement a certain degree of subsidies to ease the impact of the pandemic on emergency medical supply chain enterprises. Meanwhile, our paper further explores the obligations of the economy, society, and efficiency of enterprises under the COVID-19 pandemic and the decision making of enterprises for the implementation of CSR. Based on the principle of maximizing social welfare, we discuss decentralized decision making (without government and with government) and centralized decision making, respectively. On this basis, our paper not only designs a wholesale price–cost sharing joint contract coordination mechanism but also proves that a joint contract can achieve supply chain coordination under certain conditions. Through the analysis, we observe: (1) Government subsidies can improve the enthusiasm of supply chain members to undertake CSR; (2) With the improvement of the retailer’s CSR level, the profits of supply chain members and overall performance have improved to a certain degree; (3) To improve supply efficiency and assume social responsibility, the manufacturer implements technological innovation investment. However, it will impose some burden on the manufacturer. Government subsidies allow the manufacturer to balance between social responsibility and its profit.

Supporting data for the integrated Agent-Based Modelling and Robust Optimization on food supply network design in COVID-19 pandemic

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" [1]. 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.