An optimization model for the design of supply chains considering disruptions from pandemic uncertainty and infection trends

Abstract

Supply chain design has been an extensively researched area of study especially given how companies all around the world seek to optimize system performance. The past years have highlighted the importance of creating lean supply chains in order to reduce costs and improve efficiency. However, the uncertainties brought by the COVID-19 pandemic have challenged global supply chains with constraints on labor and capacity. Decision makers are now focusing on creating a balance between agile and lean systems because of these disruptions. A mixed integer nonlinear programming (MINLP) model for the design of supply chains under a pandemic scenario has been developed in this regard. The model considers uncertainties brought about by the imposition of government restrictions on the operations of supply chains. Specifically, the model integrates forecasts on the likely degree of restrictions that may be imposed on a given network. It then examines the interdependency between infection trends and uncertainties on customer demand, operational capacity, and facility connections. Scenarios involving different infection trend types were used to test model behavior and variable relationships. The numerical experiments yielded results showing that supply chains need to anticipate and hedge against increasing infection trends. Failure to do so could lead to incurring as much as 18% in additional total system costs. The experiments further demonstrated that additional facilities with low setup costs but higher operating costs could be selected as insurance for possible declines in operating capacity or abrupt spikes in demand.