Stochastic Optimization of Agrochemical Supply Chains with Risk Management

• Published in: Computer Aided Chemical Engineering Vol. 52; pp. 3337 - 3343
• Main Authors: Naraghi, Saba Ghasemi, Jiang, Zheyu
• Format: Book Chapter
• Language: English
• Published: 2023
• Subjects: Agrochemical supply chain; Conditional Value-at-Risk (CVaR); mixed-integer nonlinear programming; perspective cuts; Value-at-Risk (VaR); Value-at-Risk (VaR); Agrochemical supply chain; mixed-integer nonlinear programming; Conditional Value-at-Risk (CVaR); perspective cuts
• ISBN: 9780443152740; 0443152748
• ISSN: 1570-7946
• DOI: 10.1016/B978-0-443-15274-0.50532-1

The global agrochemical market is highly consolidated, with large multinational companies accounting for a major share of the market. Thus, even for a single agrochemical product, its supply chain typically involves many possible paths connecting the raw material sources of active ingredients to final customers. In addition to structural complexity, agrochemical supply chains are also subject to seasonality and various unique uncertainties, thereby demanding high system resilience and implementation of risk management strategies in the face of these uncertainties and disruptions. In this study, we formulate and optimize the supply chain of an agrochemical active ingredient by formulating a stochastic mixed-integer nonlinear programming (MINLP) model. This MINLP formulation is scenario-based with demand uncertainty addressed by Value-at-Risk (VaR) and Conditional Value-at-Risk (CVaR). For the first time, we propose to reformulate these nonlinear CVaR constraints using perspective reformulation techniques. We show that these perspective cuts give a tight approximation of the original MINLP model. Through an illustrative case study, we compare the results and performance of the original MINLP and the reformulated MILP.