Modeling and optimization of the palm oil (Elaeis guineensis) supply chain in Colombia

The aim of this research is to develop a quantitative tool that supports decision-makers in the strategic planning of supply chains (SC). The problem to be solved consists in determining the optimal configuration of the palm oil SC, including decisions associated to the number, location and capacity of all the facilities of the SC in a given country; its expansion policy in the planning horizon, means of transportation, production rates, material flow, waste management, and its potential environmental impact. Bearing this in mind, two mathematical models are presented to address this problem. The first one is a mixed integer linear programming (MILP) model applied to the oil palm industry in Colombia that aims to maximize the net present value of its SC in a specific planning horizon. On the other hand, the second model solves a multi-objective optimization (MOO) MILP problem. It combines the first model with the Life Cycle Assessment (LCA) methodology to optimize the palm oil SC in Colombia. The MOO model aims at maximizing the economic benefit of this SC and simultaneously minimizing its environmental impact (measured in “eco-points”). The MOO problem was solved using the epsilon constraint method. Pareto optimal solutions provide valuable information for the optimal design and configuration of the palm oil SC, in particular the compensations or trade-offs resulting from economic profit, and its environmental impact. The solutions obtained through this model show a more rational distribution of productive units, including the establishment of renewable power plants.

• Autores: Peña González, Darwin Dacier
• Tipo de recurso: Doctoral thesis
• Fecha de publicación: 2021
• Institución: Universidad del Norte
• Repositorio: Repositorio Uninorte
• Idioma: eng
• OAI Identifier: oai:manglar.uninorte.edu.co:10584/11201
• Acceso en línea: http://hdl.handle.net/10584/11201
• Palabra clave: Aceite de palma - Colombia
• Rights: openAccess
• License: https://creativecommons.org/licenses/by/4.0/