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

Abstract

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.

Benders Decomposition Method on Adjustable Robust Counterpart Optimization Model for Internet Shopping Online Problem

“…Optimization is an action to obtain the best results in a certain situation [6]. The process of determining conditions that yield a function's maximum or least value is known as optimization, and an optimization model can be created from optimization problems [7]. More than one method is used in solving optimization problems [8].…”