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| 8.24 MB | Adobe PDF |
Advisor(s)
Abstract(s)
Decision-making in multi-objective problems is a complex problem with
several approaches existing in the literature. Many such approaches typically combine simulation and optimization methods to achieve a robust tool capable of providing useful information to decision-makers. While simulation helps decision-makers
to test alternative scenarios and allows uncertainty to be considered, optimization
enables them to find the best alternatives for specific conditions. This paper proposes an alternative approach consisting of three levels, wherein we use simulation
to model complex scenarios, simulation-optimization to identify the scenarios of the
Pareto-front, and Data Envelopment Analysis to identify the most efficient solutions,
including those not belonging to the Pareto-front, thereby exploiting the benefits of
efficiency analysis and simulation-optimization. This can be useful when decisionmakers decide to consider scenarios that, while not optimum, are efficient for their
decision-making profile. To test our approach, we applied it to a supply chain design
problem. Our results show how our approach can be used to analyze a given system
from three different perspectives and that some of the solutions, while not optimum,
are efficient. In traditional approaches, such scenarios could be overlooked, despite
their efficiency for specific decision-making profiles.
Description
Keywords
Simulation Multi-objective simulation-optimization Data envelopment analysis Supply chain design Decision-making Efficiency