Process industry scheduling optimization using genetic algorithm and mathematical programming
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This article addresses the problem of scheduling in oil refineries. The problem consists of a multi-product plant scheduling, with two serial machine stages—a mixer and a set of tanks—which have resource constraints and operate on a continuous flow basis. Two models were developed: the first using mixed-integer linear programming (MILP) and the second using genetic algorithms (GA). Their main objective was to meet the whole forecast demand, observing the operating constraints of the refinery and minimizing the number of operational changes. A real-life data-set related to the production of fuel oil and asphalt in a large refinery was used. The MILP and GA models proved to be good solutions for both primary objectives, but the GA model resulted in a smaller number of operational changes. The reason for this is that GA incorporates a multi-criteria approach, which is capable of adaptively updating the weights of the objective throughout the evolutionary process.
KeywordsScheduling Refining MILP Genetic algorithm
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- Ballintijn K. (1993) Optimization in refinery scheduling: Modeling and solution. Optimization in industry. Mathematical Programming and Modeling Techniques in Practice 1: 191–199Google Scholar
- Berrichi, A., Amodeo, L., Yalaoui, F., Châtelet, E., & Mezghiche, M. (2008). Bi-objective optimization algorithms for joint production and maintenance scheduling: Application to the parallel machine problem. Journal of Intelligent Manufacturing. doi: 10.1007/s10845-008-0113-5.
- Churchland P., Sejnowski T. (1996) The Computational Brain. MIT Press, Cambridge, MAGoogle Scholar
- Horn J. (1997) Multicriterion Decision Making. In: Back T., Fogel D. B., Michalewicz Z. (eds) Handbook of evolutionary computation. IOP Publ. Ltd and Oxford University Press, OxfordGoogle Scholar
- Jonathan, M., Zebulum, R., Patheco, M., & Vellasco, M. (2000). Multiobjective optimization techniques: a study of the energy minimization method and its application to the synthesis of ota amplifiers.Google Scholar
- Karuppiah, R., Furman, K. C., & Grossmann, I. E. (2008). Global optimization for scheduling refinery crude oil operations. Computers and Chemical Engineering (in press).Google Scholar
- Sahdev, M. K., Jain, K. K., & Srivastava, P. (2004). Petroleum refinery planning and optmization using linear programming: Cheresources.Google Scholar
- Simão, L., Dias, D., & Pacheco, M. (2007). Refinery scheduling optimization using genetic algorithms and cooperative coevolution.Google Scholar
- Wu N., Zhou M., Chu F. (2005) Short-term scheduling for refinery process: Bridging the gap between theory and applications. International Journal of Intelligent Control and Systems 10(2): 162–174Google Scholar
- Zebulum, R. S., Pacheco, M. A., & Vellasco, M. (1998). Synthesis of CMOS operational amplifiers through genetic algorithms. Proceedings of XI Brazillian symposium on integrated circuit design, pp. 125–128.Google Scholar