| Full text | |
| Author(s): |
de Souza Amorim, Flaviana Moreira
;
Arantes, Marcio da Silva
;
Motta Toledo, Claudio Fabiano
;
Frisch, Pierre Eric
;
Almada-Lobo, Bernardo
;
IEEE
Total Authors: 6
|
| Document type: | Journal article |
| Source: | 2018 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC); v. N/A, p. 8-pg., 2018-01-01. |
| Abstract | |
The present paper proposes two hybrid genetic algorithms as decision-making techniques for operational level decisions in the Glass Container Industry (GCI). The proposed methods address a production scenario where one new furnace and the related machines must be added to the current industrial plant. The configurations for each machine connected in a furnace is a decision to be taken, which depends on demand forecasts for glass containers within a time horizon. It is a tactical and operational level decisions that must be efficiently made. A mathematical formulation is first presented to describe precisely the objective and constraints for such problem. The formulation will also allow solving the problem instances by applying an exact method. Next, a hybrid approach combining genetic algorithms with mathematical programming techniques, and a greedy filter heuristic is proposed to solve the same problem instances. The set of instances is generated with data provided by a GCI located in Portugal and Brazil. The results reported indicate that the hybrid genetic algorithms return solutions able to support the operational and tactical decisions. (AU) | |
| FAPESP's process: | 13/07375-0 - CeMEAI - Center for Mathematical Sciences Applied to Industry |
| Grantee: | Francisco Louzada Neto |
| Support Opportunities: | Research Grants - Research, Innovation and Dissemination Centers - RIDC |