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A bi-objective mathematical model for integrated planning of sugarcane harvesting and transport operations
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Orientador(es)
Resumo(s)
Motivated by a real situation arising in the Brazilian sugarcane industry, this paper addresses the integrated planning of harvesting and transport operations over a multi-period planning horizon. The aim is to develop a tactical plan for the deployment of harvesting and transport equipment that sets the periods for the execution of the harvesting operations on the sugarcane fields and the type of harvesting machines and road haulage vehicles to be operated. These decisions are made subject to multiple constraints related to the projected crop yield, resource availability, demand for sugarcane at the mills and further technical requirements specific to the harvesting operations. The tactical plan to be determined minimizes the total cost incurred by the equipment used and the total time required to harvest all the fields. We propose a bi-objective mixed-integer non-linear programming model for this new problem. A computational study is conducted for test instances capturing the characteristics of a Brazilian milling company. Pareto-optimal solutions are identified by the Progressive Bounded Constraint method that is extended to the problem at hand. A comparative analysis highlights the trade-offs between economic performance and harvesting efficiency, thereby supporting the decision maker in making a more informed choice of the preferred tactical plan. Useful managerial insights are also provided into the profile of the harvesting and transport resources that should be used under different weather conditions and work schedules.
Descrição
Palavras-chave
Multi-Objective Optimization Mixed-Integer Programming Integrated Sugarcane Harvesting and Transport Planning
Contexto Educativo
Citação
Filho, Angelo Aliano; Teresa Melo e Margarida Vaz Pato .(2021). "A bi-objective mathematical model for integrated planning of sugarcane harvesting and transport operations", Computers and Operations Research, vol. 134: article 105419. 2021
Editora
Elsevier