Multi-objective optimization under parametric uncertainty: A Pareto ellipsoids-based algorithm

Mores, Wannes; Nimmegeers, Philippe; Hashem, Ihab; Bhonsale, Satyajeet; Impe, Jan Van

doi:10.1016/j.compchemeng.2022.108099

Cited by 8 publications

(4 citation statements)

References 27 publications

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“…The third baseline, proposed in [31], [32], is closely related to our work. Using a divide-and-conquer approach it divides the set of weights into regular simplexes and places samples on its vertices.…”

Section: Discussionmentioning

confidence: 97%

“…Closely related to our work, the work presented in [31], [32] considers MOO under uncertain parameters, and pose a Paretoapproximation problem where samples are required to be statistically significantly different, focusing on applications in chemical engineering. Similar to our work, this approach iteratively places new samples on the Pareto-front using a divideand-conquer (DC) approach to find new weights.…”

Section: B Related Workmentioning

confidence: 99%

“…Both algorithms stop dividing when solutions are no longer significantly different. However, a key difference is that [31], [32] chooses new Pareto-samples by uniformly placing weights (similar to a breadth-first-search). In our work, we place new weights such that we greedily minimize dispersion, i.e., the distance in the objective space.…”

Section: B Related Workmentioning

confidence: 99%

“…The set of Pareto-optimal solutions is then approximated by exploring different scalarization weights [5], [8], [28]- [30]. However, finding useful weights is often challenging [8], [9], [31], [32].…”

Section: B Related Workmentioning

confidence: 99%

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Online Multi-Robot Task Assignment with Stochastic Blockages

Wilde

Alonso–Mora

2022

2022 IEEE 61st Conference on Decision and Control (CDC)

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We study the problem of finding statistically distinct plans for stochastic planning and task assignment problems such as online multi-robot pickup and delivery (MRPD) when facing multiple competing objectives. In many real-world settings robot fleets do not only need to fulfil delivery requests, but also have to consider auxiliary objectives such as energy efficiency or avoiding human-centered work spaces. We pose MRPD as a multiobjective optimization problem where the goal is to find MRPD policies that yield different trade-offs between given objectives. There are two main challenges: 1) MRPD is computationally hard, which limits the number of trade-offs that can reasonably be computed, and 2) due to the random task arrivals, one needs to consider statistical variance of the objective values in addition to the average. We present an adaptive sampling algorithm that finds a set of policies which i) are approximately optimal, ii) approximate the set of all optimal solutions, and iii) are statistically distinguishable. We prove completeness and adapt a state-of-the-art MRPD solver to the multi-objective setting for three example objectives. In a series of simulation experiments we demonstrate the advantages of the proposed method compared to baseline approaches and show its robustness in a sensitivity analysis. The approach is general and could be adapted to other multi-objective task assignment and planning problems under uncertainty.

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Section: Discussionmentioning

confidence: 97%

Section: B Related Workmentioning

confidence: 99%