Browsing by Subject "Multi-objective optimization"

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Open Access
Planning sustainable routes: Economic, environmental and welfare concerns
(Elsevier BV, 2021-10-09) Dükkancı, O.; Karsu, Özlem; Kara, Bahar Y.
We introduce a problem called the Sustainable Vehicle Routing Problem (SVRP) in which the sustainability notion is considered in terms of economic, environmental and social impacts. Inspired by real-world problems that large cargo companies face for their delivery decisions, we introduce a new facet to the classical vehicle routing problem by considering the welfare of all three stakeholders of the problem: an environmentally conscious company, the drivers, and the indistinguishable customers, as our setting assumes that all customers belong to the same delivery class. Thus, the proposed problem consists of three objective functions. The first one is to minimize the total fuel consumption and emission to represent the companies’ main economic and environmental concerns. The second one is to maximize total welfare of the drivers through a function that encourages equitable payment across drivers while encouraging low total driver cost and the third one is to maximize total welfare of the customers through a function that encourages fairness in terms of delivery times. The last two objectives are measured using slots for tour lengths and delivery times. We implement an efficient solution approach based on the -constraint scalarization to find the nondominated solutions of our triobjective optimization problem and present computational analysis that provide insights on the trade-off between the objectives. Our experiments demonstrate the potential of the suggested framework under the customer anonymity assumption to help decision makers make effective plans that all parties involved would give consent to.
Open Access
Robust optimization of multi-objective multi-armed bandits with contaminated bandit feedback
(2022-06) Bozgan, Kerem
Multi-objective multi-armed bandits (MO-MAB) is an important extension of the standard MAB problem that has found a wide variety of applications ranging from clinical trials to online recommender systems. We consider Pareto set identification problem in the adversarial MO-MAB setting, where at each arm pull, with probability ϵ ∈ (0,1/2), an adversary corrupts the reward samples by replacing the true samples with the samples from an arbitrary distribution of its choosing. Existing MO-MAB methods in the literature are incapable of handling such attacks unless there are strict restrictions on the contamination distributions. As a result, these methods perform poorly in practice where such restrictions on the adversary are not valid in general. To fill this gap in the literature, we propose two different robust, median-based optimization methods that can approximate the Pareto optimal set from contaminated samples. We prove a sample complexity bound of the form O(1/α^2 log(1/δ)) for the proposed methods, where α>0 and δ ∈ (0,1) are accuracy and confidence parameters, respectively, that can be set by the user according to his/her preference. This bound matches, in the worst case, the bounds from [1, Theorem 4] and [2, Theorem 3] that consider the adversary free setting. We compare the proposed methods with a mean-based method from the MO-MAB literature on real-world and synthetic experiments. Numerical results verify our theoretical expectations and show the importance of robust algorithm design in the adversarial setting.