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Evaluation of multi-objective optimization approaches for solving green supply chain design problems
ارزیابی روشهای بهینه سازی چند منظوره برای حل مشکلات طراحی زنجیره تامین سبز-2017 This paper evaluates the applicability of different multi-objective optimization methods for envir
onmentally conscious supply chain design. We analyze a case study with three objectives: costs, CO2 and
fine dust (also known as PM – Particulate Matters) emissions. We approximate the Pareto front using the
weighted sum and epsilon constraint scalarization methods with pre-defined or adaptively selected
parameters, two popular evolutionary algorithms, SPEA2 and NSGA-II, with different selection strategies,
and their interactive counterparts that incorporate Decision Makers (DMs) indirect preferences into the
search process. Within this case study, the CO2 emissions could be lowered significantly by accepting a
marginal increase of costs over their global minimum. NSGA-II and SPEA2 enabled faster estimation of
the Pareto front, but produced significantly worse solutions than the exact optimization methods. The
interactive methods outperformed their a posteriori counterparts, and could discover solutions corre
sponding better to the DM preferences. In addition, by adjusting appropriately the elicitation interval and
starting generation of the elicitation, the number of pairwise comparisons needed by the interactive
evolutionary methods to construct a satisfactory solution could be decreased.
Keywords: Supply chain management | Green logistics | Multi-objective programming | Indirect preference information| Evolutionary algorithms |Interactive evolutionary multi-objective | optimization |
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