Assessing the performance of multiobjective genetic algorithms for optimization of a batch process scheduling problem

SHAW, KJ, NORTCLIFFE, Anne, THOMPSON, M, LOVE, J, FLEMING, PJ and FONSECA, CM (1999). Assessing the performance of multiobjective genetic algorithms for optimization of a batch process scheduling problem. In: Evolutionary Computation, 1999. CEC 99. Proceedings of the 1999 Congress on. IEEE.

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Abstract

Scheduling optimization problems provide much potential for innovative solutions by genetic algorithms. The complexities, constraints and practicalities of the scheduling process motivate the development of genetic algorithm (GA) techniques to allow innovative and flexible scheduling solutions. Multiobjective genetic algorithms (MOGAs) extend the standard evolutionary-based genetic algorithm optimization technique to allow individual treatment of several objectives simultaneously. This allows the user to attempt to optimize several conflicting objectives, and to explore the trade-offs, conflicts and constraints inherent in this process. The area of MOGA performance assessment and comparison is a relatively new field, as much research concentrates on applications rather than the theory. However, the theoretical exploration of MOGA performance can have tangible effects on the development of highly practical applications, such as the process plant scheduling system under development in this work. By assessing and comparing the strengths, variations and limitations of the developing MOGA using a quantitative method, a highly efficient MOGA can develop to suit the application. The user can also gain insight into behaviour the application itself. In this work, four MOGAs are implemented to solve a process scheduling optimization problem; using two and five objectives, and two schedule building rules.

Item Type:	Book Section
Uncontrolled Keywords:	process plant scheduling system, performance assessment, multiobjective genetic algorithms, batch process scheduling problem, scheduling optimization problem, complexity, constraints, evolutionary-based genetic algorithm optimization technique, conflicting objectives, schedule building rules
Research Institute, Centre or Group - Does NOT include content added after October 2018:	Materials and Engineering Research Institute > Engineering Research
Departments - Does NOT include content added after October 2018:	Faculty of Science, Technology and Arts > Department of Engineering and Mathematics
Identification Number:	https://doi.org/10.1109/CEC.1999.781905
Depositing User:	Anne Nortcliffe
Date Deposited:	19 Jan 2018 15:43
Last Modified:	18 Mar 2021 16:20
URI:	https://shura.shu.ac.uk/id/eprint/14433

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