Citation
Case study: an effective genetic algorithm for the chemical batch production scheduling. In proceedings of the 5th Multidisciplinary International Conference on Scheduling : Theory and Applications (MISTA 2011), 9-11 August 2011, Phoenix, Arizona, USA, pages 469-482, 2011.
Paper
Abstract
The paper presents a case study – two genetic algorithms for solving the integer programming part of the MILP formulation for a particular chemical batch production scheduling problem. The standard MILP formulation of the batch production scheduling problem is decomposed into two sub problems: 1) unconstrained integer programming problem involving two independent assignment and one permutation vectors as solution representation and having one objective function; 2) constrained continuous linear program representing the original MILP formulation with integer variables fixed in terms of the solution of the first sub problem. The first sub problem is known as NP-hard and is difficult to solve by exact algorithms, the second sub problem is easy to solve in terms of exact methods. Two genetic algorithms are proposed to solve the first sub problem; the linear program acts as an objective function by delivering the solution of the original MILP model using the values of integer variables found by genetic algorithms. It is explained how the complete production schedule and the objective function value (the schedule cost) are deduced. The genetic algorithms combine the approaches for solving both assignment and permutation sequencing problems. The solution representations, algorithms’ convergence and parameters are discussed. The efficiency of both algorithms is proved by the computational results that are compared with the optimal solutions and with another heuristic technique.
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Bibtex
@INPROCEEDINGS{2011-469-482-P, author = {D. Borodin and B. Van Vreckem and W. De Bruyn},
title = {Case study: an effective genetic algorithm for the chemical batch production scheduling},
booktitle = {In proceedings of the 5th Multidisciplinary International Conference on Scheduling : Theory and Applications (MISTA 2011), 9-11 August 2011, Phoenix, Arizona, USA},
year = {2011},
editor = {J. Fowler and G. Kendall and B. McCollum},
pages = {469--482},
note = {Paper},
abstract = {The paper presents a case study – two genetic algorithms for solving the integer programming part of the MILP formulation for a particular chemical batch production scheduling problem. The standard MILP formulation of the batch production scheduling problem is decomposed into two sub problems: 1) unconstrained integer programming problem involving two independent assignment and one permutation vectors as solution representation and having one objective function; 2) constrained continuous linear program representing the original MILP formulation with integer variables fixed in terms of the solution of the first sub problem. The first sub problem is known as NP-hard and is difficult to solve by exact algorithms, the second sub problem is easy to solve in terms of exact methods. Two genetic algorithms are proposed to solve the first sub problem; the linear program acts as an objective function by delivering the solution of the original MILP model using the values of integer variables found by genetic algorithms. It is explained how the complete production schedule and the objective function value (the schedule cost) are deduced. The genetic algorithms combine the approaches for solving both assignment and permutation sequencing problems. The solution representations, algorithms’ convergence and parameters are discussed. The efficiency of both algorithms is proved by the computational results that are compared with the optimal solutions and with another heuristic technique.},
owner = {gxk},
timestamp = {2011.08.15},
webpdf = {2011-469-482-P.pdf} }