Solving a bi-objective facility location problem in the presence of uncertainty
Sprache des Vortragstitels:
Englisch
Original Tagungtitel:
29th European Conference on Operational Research 2018
Sprache des Tagungstitel:
Englisch
Original Kurzfassung:
To cope with uncertainty in optimization problems, many different approaches have been presented in the literature. The most widely used ones are stochastic optimization, including concepts such as expected value, chance constraints or risk measures, and robust optimization.
However, the intersection of uncertainty and multi-objective optimization has not received as much attention. In this paper, we consider a bi-objective facility location model for designing last mile networks in a disaster relief setting, where the demand of beneficiaries is considered to be uncertain. We use two different approaches for dealing
with uncertainty: (i) scenario based stochastic programming (optimizing
the expected value) and (ii) the concept of minmax robustness (optimizing the worst case value). Both approaches are then integrated into two criterion space search methods, namely the well-known epsilon-constraint method and the more recently introduced balanced box method. Finally, we evaluate and compare the different approaches by applying them to data sets derived from a real world case study, in order to gain insights into structural differences of the obtained Pareto frontiers and the underlying solutions.
Sprache der Kurzfassung:
Englisch
Englischer Vortragstitel:
Solving a bi-objective facility location problem in the presence of uncertainty