Glass container production scheduling through hybrid multi-population based evolutionary algorithm

Authors:
Claudio Fabiano Motta Toledo;MáRcio Da Silva Arantes;Renato Resende Ribeiro De Oliveira;Bernardo Almada-Lobo
Affiliations:
Instituto de Ciências Matemáticas e de Computação, Universidade de São Paulo, 13566-590 São Carlos, SP, Brazil;Instituto de Ciências Matemáticas e de Computação, Universidade de São Paulo, 13566-590 São Carlos, SP, Brazil;Departamento de Ciência da Computação, Universidade Federal de Lavras, 37200-000, C.P. 3037, Lavras, MG, Brazil;INESC-TEC, Faculdade de Engenharia, Universidade do Porto, 4200-465 Porto, Portugal
Venue:
Applied Soft Computing
Year:
2013

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Abstract

Driven by a real-world application in the capital-intensive glass container industry, this paper provides the design of a new hybrid evolutionary algorithm to tackle the short-term production planning and scheduling problem. The challenge consists of sizing and scheduling the lots in the most cost-effective manner on a set of parallel molding machines that are fed by a furnace that melts the glass. The solution procedure combines a multi-population hierarchically structured genetic algorithm (GA) with a simulated annealing (SA), and a tailor-made heuristic named cavity heuristic (CH). The SA is applied to intensify the search for solutions in the neighborhood of the best individuals found by the GA, while the CH determines quickly values for a relevant decision variable of the problem: the processing speed of each machine. The results indicate the superior performance of the proposed approach against a state-of-the-art commercial solver, and compared to a non-hybridized multi-population GA.