A bi-objective genetic approach for the selection of sugarcane varieties to comply with environmental and economic requirements

The selection of sugarcane varieties is an important problem faced by companies in Brazil that exploit sugarcane harvest for energy production. In the light of current concerns regarding the reduction of environmental damage and the efficiency of the production system, research into this problem is called for. In this context the authors begin by outlining the sugarcane variety selection problem in accordance with technical constraints with the purpose of minimizing collection and transport costs and maximizing energy balance obtained from residues of the sugarcane harvest. They then present a previously developed model for the problem within bi-objective binary linear programming and study its computational complexity. Fundamentally, this paper is devoted to the application of a bi-objective genetic heuristic to the question addressed. A computational experiment, performed by resorting to a test set including real and semi-randomly generated instances, is then reported. The results prove the high quality of the heuristic in terms of solution quality, besides computing time. For these reasons, this will be an appropriate tool to help sugarcane company managers to plan their producing activities.

[1]  Dimitri P. Bertsekas,et al.  Nonlinear Programming , 1997 .

[2]  P. Siarry,et al.  Multiobjective Optimization: Principles and Case Studies , 2004 .

[3]  T. L. Saaty,et al.  The computational algorithm for the parametric objective function , 1955 .

[4]  Helenice de Oliveira Florentino,et al.  Multiobjective 0-1 integer programming for the use of sugarcane residual biomass in energy cogeneration , 2011, Int. Trans. Oper. Res..

[5]  Gary B. Lamont,et al.  Multiobjective evolutionary algorithms: classifications, analyses, and new innovations , 1999 .

[6]  O. H. Brownlee,et al.  ACTIVITY ANALYSIS OF PRODUCTION AND ALLOCATION , 1952 .

[7]  Maria João Alves,et al.  MOTGA: A multiobjective Tchebycheff based genetic algorithm for the multidimensional knapsack problem , 2007, Comput. Oper. Res..

[8]  Asoke Kumar Bhunia,et al.  Elitist genetic algorithm for assignment problem with imprecise goal , 2007, Eur. J. Oper. Res..

[9]  Klaudia Frankfurter Computers And Intractability A Guide To The Theory Of Np Completeness , 2016 .

[10]  Serpil Sayin,et al.  Measuring the quality of discrete representations of efficient sets in multiple objective mathematical programming , 2000, Math. Program..

[11]  Paul R. Harper,et al.  A genetic algorithm for the project assignment problem , 2005, Comput. Oper. Res..

[12]  David S. Johnson,et al.  Computers and Intractability: A Guide to the Theory of NP-Completeness , 1978 .

[13]  Günther R. Raidl,et al.  An improved hybrid genetic algorithm for the generalized assignment problem , 2004, SAC '04.

[14]  M. Fisher,et al.  A multiplier adjustment method for the generalized assignment problem , 1986 .

[15]  Jared L. Cohon,et al.  Multiobjective programming and planning , 2004 .

[16]  Gary B. Lamont,et al.  Evolutionary Algorithms for Solving Multi-Objective Problems , 2002, Genetic Algorithms and Evolutionary Computation.

[17]  R. K. Ursem Multi-objective Optimization using Evolutionary Algorithms , 2009 .

[18]  Helenice de Oliveira Florentino,et al.  MULTIOBJECTIVE OPTIMIZATION OF ECONOMIC BALANCES OF SUGARCANE HARVEST BIOMASS , 2008 .

[19]  George Mavrotas,et al.  Solving multiobjective, multiconstraint knapsack problems using mathematical programming and evolutionary algorithms , 2010, Eur. J. Oper. Res..

[20]  Charles Gide,et al.  Cours d'économie politique , 1911 .

[21]  Margarida Moz,et al.  Solving a bi-objective nurse rerostering problem by using a utopic Pareto genetic heuristic , 2008, J. Heuristics.

[22]  Lothar Thiele,et al.  Comparison of Multiobjective Evolutionary Algorithms: Empirical Results , 2000, Evolutionary Computation.

[23]  Rajeev Kumar,et al.  Assessing solution quality of biobjective 0-1 knapsack problem using evolutionary and heuristic algorithms , 2010, Appl. Soft Comput..

[24]  Joseph L. Zinnes,et al.  Theory and Methods of Scaling. , 1958 .

[25]  K. Schittkowski,et al.  NONLINEAR PROGRAMMING , 2022 .

[26]  Maria Márcia Pereira Sartori,et al.  Determination of the optimal quantity of crop residues for energy in sugarcane crop management using linear programming in variety selection and planting strategy , 2001 .

[27]  Xavier Gandibleux,et al.  A survey and annotated bibliography of multiobjective combinatorial optimization , 2000, OR Spectr..

[28]  Tomaz Caetano Cannavam Ripoli,et al.  Biomassa de cana-de-açúcar: colheita, energia e ambiente , 2004 .

[29]  Jason R. Schott Fault Tolerant Design Using Single and Multicriteria Genetic Algorithm Optimization. , 1995 .