Robust Optimization for Food Supply Chain Management Problems: A Critical Review and its Novelty

Abstract

Optimization problems in real life often have problems with data that cannot be known precisely; constraints on the data are commonly referred as errors. This kind of data is called uncertainty. This uncertainty problem can be solved using Robust Optimization (RO). RO is growing rapidly with the participation of various kinds of research, especially the supply chain (distribution of food or goods between regions). It can be seen that RO is very active in providing support and contribution in various aspects of life by providing optimal results for an objective function and dealing with existing limitations and data uncertainty. This article discusses the background of the problem and the purpose of creating an article, provides an overview of bibliometric map analysis methods and discusses literature and studies. Critical review from OR database articles for supply chain problems are used as a reference, so at the end, it can be determined what novelty is an opportunity for further research.