ϵ-OA for the solution of bi-objective generalized disjunctive programming problems in the synthesis of nonlinear process networks - Fattahi, Ali; Turkay, Metin (Computers & chemical engineering, 2015)
•A novel algorithm (ϵ-OA) for generating efficient solutions for bi-objective GDPs is presented.•Theoretical characterization of the ϵ-OA is provided.•The ϵ-OA is illustrated on two well-known benchmark problems.•Comparative analysis of the performance illustrates the superiority of the novel ϵ-OA algorithm over straightforward implementations. There has been an increasing interest in multicriteria optimization (MCO) of nonlinear process network problems in recent years. Several mathematical models have been developed and solved using MCO methodologies including ϵ-constraint, weighted sum, and minimum distance. In this paper, we investigate the bi-objective nonlinear network synthesis problem and propose an effective algorithm, ϵ-OA, based on augmented ϵ-constraint and logic-based outer approximation (OA). We provide theoretical characterization of the proposed algorithm and show that the solutions generated are efficient. We illustrate the effectiveness of our novel algorithm on two benchmark problems. The ϵ-OA is compared to the straightforward use of OA with augmented ϵ-constraint algorithm (ϵ-con+OA), the augmented ϵ-constraint without OA (ϵ-MINLP), and the traditional ϵ-constraint (T-ϵ-con). Based on the results, our novel algorithm is very effective in solving the bi-objective generalized disjunctive programming problems in the synthesis of process networks.