Systematic Literature Review on Robust Optimization in Solving Sustainable Development Goals (SDGs) Problems during the COVID-19 Pandemic

Handling uncertainty is important in decision making, especially for SDGs problems. Robust Optimization (RO) is an applied optimization method that can be employed to handle optimization under uncertain data. With SDGs problems, many uncertain data have been considered in decision making. With RO, the data uncertainties are assumed to lay within a compact, convex continuous set. There are three special sets that can be used to represent the data, i.e., box, ellipsoidal, or polyhedral uncertainty sets. These special sets lead the SDGs problems to a computationally tractable optimization model, such that the global optimal solution is attained. However, literature reviews on the application of RO in SDGs decision-making is sparse, especially during the COVID-19 pandemic period. This paper examines the following topics: (1) the purposes of studies of RO and SDGs during the COVID-19 pandemic, (2) the state-of-the-art in RO-SDGs to determine the research objectives, and (3) the SDGs type of problems that have been modeled using RO. A systematic literature review is conducted in this paper, wherein discussion is based on a PRISMA (Preferred Reporting Items for Systematic Review and Meta-Analyses) flowchart. To this end, the database reference searching conducted on the Scopus, Science Direct, and SAGE databases, is completed using the help RStudio software. The analysis was carried out on two datasets, assisted by the output visualization using RStudio software with the "bibliometrix” package, and using the ‘biblioshiny()' command to create a link to the "shiny web interface”. In this paper, the research gap on application of RO to SDGs problems is analyzed in order to identify the research objectives, methods, and specific RO-SDGs problems. As a result, the application of RO to SDGs problems is rare;this finding provides a motivation to conduct a further study of RO and SDGs during the COVID-19 pandemic. An expansion is presented using the key phrase "Operations Research and Optimization Modeling”, or "OROM”. SDGs in Indonesia may be referenced as an example of the capacity building available through RO/OROM. © 2023 by the authors.

Improving the capacity of local food network through local food hubs' development

The COVID-19 pandemic and humanitarian challenges such as war and conflict, climate changes, and economic crises bring unexpected humanitarian challenges, such as food insecurity. Especially for developing countries, this is a double crisis, food poverty and COVID-19 pandemic. This situation needs an alternative food network (AFN), which can improve the local food sources to fulfill the food demand. The local food hubs' (LFHs) development as an AFN that considers local producers and customers could serve as an alternative strategy to reduce the uncertainty of food availability. However, the challenge in developing LFH in developing countries is the problem of logistics efficiency. Therefore, this study discusses the development of food logistics efficiency by determining the optimum location of LFH and the fulfillment of food supply for each optimum location of LFH in rural areas. Robust optimization is applied to the Multi-Objective Many-to-Many Location-Routing Problem (MOMMLRP) model to handle the uncertain demand and production capacity. This study focused on West Java Province, Indonesia, as areas that represent developing countries. Based on our result, eight sub-districts have been determined as an optimum location to develop LFHs. This study also elaborates on the food network in the worst situation that needs regional food hubs' (RFHs) support. The RFH support can be used when the LFH faces an unexpected problem that cannot fulfill their demand.

Contribution of robust optimization on handling agricultural processed products supply chain problem during covid-19 pandemic

This research aims to show how decision sciences can make a significant contribution on handling the supply chain problem during Covid-19 Pandemic. The paper discusses how robust optimization handles uncertain demand in agricultural processed products supply chain problems within two scenarios during the pandemic situation, i.e., the large-scale social distancing and partial social distancing. The study assumes that demand and production capacity are uncertain during a pandemic situation. Robust counterpart methodology is employed to obtain the robust optimal solution. To this end, the uncertain data is assumed to lie within a polyhedral uncertainty set. The result shows that the robust counterpart model is a computationally tractable through linear programming problem. Numerical experiment is presented for the Bandung area with a case on sugar and cooking oil that is the most influential agricultural processed products besides the main staple food of the Indonesian people, rice. © 2022 Growing Science Ltd.