constraining the feasible region of the problem; this is however case-dependent and
the impact of these constraints may be more pronounced for other DWPPs.
As a side complementary experiment of the project, a detailed discussion
regarding the pros and cons of expressing environmental impacts in MOO utilizing
the midpoint categories versus endpoint score has been conducted in [21].
Finally, the algorithms developed and the experience gained in this project,
could be applied (with due adaptations) to other case studies. In particular, a very
appealing and timely research area would be the optimization of supply chains,
under resiliency constraints and risk-based decision making.
Acknowledgements The authors acknowledge the funding from Luxembourg National Research
Fund (FNR) in the framework of the OASIS project (CR13/SR/5871061).
References
1. A. Azapagic, R. Clift, Life cycle assessment and multiobjective optimisation, Journal of
Cleaner Production, Vol. 7, 1999, pp. 135–143.
2. I.E. Grossman, G Guillén-Gosálbez, Scope for the Application of Mathematical Programming
Techniques in the Synthesis and Planning of Sustainable Processes, Computers & Chemical
Engineering, Vol. 34, 2010, pp. 1365–1376.
3. G. Guillén-Gosálbez, J.A. Caballero, L. Jiménez, Application of Life Cycle Assessment to the
Structural Optimization of Process Flowsheets, Industrial & Engineering Chemistry
Research, Vol. 47, 2008, pp. 777–789.
4. F. You, L. Tao, D.J. Graziano, S.W. Snyder, Optimal design of sustainable cellulosic biofuel
supply chains: Multiobjective optimization coupled with life cycle assessment and input–
output analysis, AIChE Journal, Vol. 58, 2012, pp. 1157–1180.
5. C. Pieragostini, M.C. Mussati, P Aguirre, On process optimization considering LCA
methodology, Journal of Environmental Management, Vol. 96, 2012, pp. 43–54.
6. F. Capitanescu, S. Rege, A. Marvuglia, E. Benetto, A. Ahmadi, T. Navarrete-Gutierrez, L.
Barna, Cost versus life cycle assessment-based optimization of drinking water production
plants, Journal of Environmental Management, Vol. 177, 2016, pp. 278–287.
7. A. Ahmadi, L. Barna, F. Capitanescu, A. Marvuglia, E. Benetto, An archive-based
multi-objective evolutionary algorithm with adaptive search space partitioning to deal with
expensive optimisation problems: application to process eco-design, Computers & Chemical
Engineering, Vol. 87, 2016, pp. 95–110.
8. F. Capitanescu, A. Ahmadi, E. Benetto, A. Marvuglia, L. Barna, Some efficient approaches
for multi-objective constrained optimization of computationally expensive black-box model
problems, Computers & Chemical Engineering, Vol. 82, No. 2, 2015, pp. 228–239.
9. F. Capitanescu, A. Marvuglia, E. Benetto, A. Ahmadi, L. Barna, Assessing the uses of
NLP-based surrogate models for solving expensive multi-objective optimization problems:
application to potable water chains, Enviroinfo Conference, Copenhagen (Denmark), 2015.
10. F. Capitanescu, A. Marvuglia, E. Benetto, A. Ahmadi, L. Barna, Linear programming-based
directed local search for expensive multi-objective optimization problems: application to
drinking water production plants, European Journal of Operational Research, Vol. 262,
2017, pp. 322–334.
11. M.J. Goedkoop, R. Heijungs, M. Huijbregts, A. De Schryver, J. Struijs, R. Van Zelm, ReCiPe
2008—A life cycle impact assessment method which comprises harmonised category
indicators at the midpoint and the endpoint level, First edition Report I: Characterisation, 6
January 2009.
30
F. Capitanescu et al.
the impact of these constraints may be more pronounced for other DWPPs.
As a side complementary experiment of the project, a detailed discussion
regarding the pros and cons of expressing environmental impacts in MOO utilizing
the midpoint categories versus endpoint score has been conducted in [21].
Finally, the algorithms developed and the experience gained in this project,
could be applied (with due adaptations) to other case studies. In particular, a very
appealing and timely research area would be the optimization of supply chains,
under resiliency constraints and risk-based decision making.
Acknowledgements The authors acknowledge the funding from Luxembourg National Research
Fund (FNR) in the framework of the OASIS project (CR13/SR/5871061).
References
1. A. Azapagic, R. Clift, Life cycle assessment and multiobjective optimisation, Journal of
Cleaner Production, Vol. 7, 1999, pp. 135–143.
2. I.E. Grossman, G Guillén-Gosálbez, Scope for the Application of Mathematical Programming
Techniques in the Synthesis and Planning of Sustainable Processes, Computers & Chemical
Engineering, Vol. 34, 2010, pp. 1365–1376.
3. G. Guillén-Gosálbez, J.A. Caballero, L. Jiménez, Application of Life Cycle Assessment to the
Structural Optimization of Process Flowsheets, Industrial & Engineering Chemistry
Research, Vol. 47, 2008, pp. 777–789.
4. F. You, L. Tao, D.J. Graziano, S.W. Snyder, Optimal design of sustainable cellulosic biofuel
supply chains: Multiobjective optimization coupled with life cycle assessment and input–
output analysis, AIChE Journal, Vol. 58, 2012, pp. 1157–1180.
5. C. Pieragostini, M.C. Mussati, P Aguirre, On process optimization considering LCA
methodology, Journal of Environmental Management, Vol. 96, 2012, pp. 43–54.
6. F. Capitanescu, S. Rege, A. Marvuglia, E. Benetto, A. Ahmadi, T. Navarrete-Gutierrez, L.
Barna, Cost versus life cycle assessment-based optimization of drinking water production
plants, Journal of Environmental Management, Vol. 177, 2016, pp. 278–287.
7. A. Ahmadi, L. Barna, F. Capitanescu, A. Marvuglia, E. Benetto, An archive-based
multi-objective evolutionary algorithm with adaptive search space partitioning to deal with
expensive optimisation problems: application to process eco-design, Computers & Chemical
Engineering, Vol. 87, 2016, pp. 95–110.
8. F. Capitanescu, A. Ahmadi, E. Benetto, A. Marvuglia, L. Barna, Some efficient approaches
for multi-objective constrained optimization of computationally expensive black-box model
problems, Computers & Chemical Engineering, Vol. 82, No. 2, 2015, pp. 228–239.
9. F. Capitanescu, A. Marvuglia, E. Benetto, A. Ahmadi, L. Barna, Assessing the uses of
NLP-based surrogate models for solving expensive multi-objective optimization problems:
application to potable water chains, Enviroinfo Conference, Copenhagen (Denmark), 2015.
10. F. Capitanescu, A. Marvuglia, E. Benetto, A. Ahmadi, L. Barna, Linear programming-based
directed local search for expensive multi-objective optimization problems: application to
drinking water production plants, European Journal of Operational Research, Vol. 262,
2017, pp. 322–334.
11. M.J. Goedkoop, R. Heijungs, M. Huijbregts, A. De Schryver, J. Struijs, R. Van Zelm, ReCiPe
2008—A life cycle impact assessment method which comprises harmonised category
indicators at the midpoint and the endpoint level, First edition Report I: Characterisation, 6
January 2009.
30
F. Capitanescu et al.