Racing Strategy for the Dynamic-Customer Location-Allocation Problem

Ankrah, Reginald; Lacroix, Benjamin; McCall, John; Hardwick, Andrew; Conway, Anthony; Owusu, Gilbert

doi:10.1109/cec48606.2020.9185918

Cited by 2 publications

(2 citation statements)

References 12 publications

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“…While progress has been made in developing algorithms, much work remains to be done before the field can fully address the challenges of real-world problems. Currently, the performance of algorithms is typically evaluated using artificial benchmark problems and some existing ROOT methods have been applied to real-world-based benchmark problems, including nonlinear dynamic stochastic optimization problems for stochastic energy management [102,103] and dynamic customer location-allocation [104,105]. One of the main challenges in solving real-world problems is that dynamic handling components are not yet capable of handling the complexities of these problems, such as irregular changes over time, multiple types of environmental changes, local and hardto-detect environmental changes, and continuously changing environments.…”

Section: Challenges and Progress In Solving Real-world Root Problemsmentioning

confidence: 99%

Robust Optimization Over Time: A Critical Review

Yazdani,

Omidvar,

Yazdani

et al. 2024

IEEE Trans. Evol. Computat.

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Robust optimization over time (ROOT) is the combination of robust optimization and dynamic optimization. In ROOT, frequent changes to deployed solutions are undesirable, which can be due to the high cost of switching between deployed solutions, limitations on the resources required to deploy new solutions, and/or the system's inability to tolerate frequent changes in the deployed solutions. ROOT is dedicated to the study and development of algorithms capable of dealing with the implications of deploying or maintaining solutions over longer time horizons involving multiple environmental changes. This paper presents an in-depth review of the research on ROOT. The overarching aim of this survey is to help researchers gain a broad perspective on the current state of the field, what has been achieved so far, and the existing challenges and pitfalls. This survey also aims to improve accessibility and clarity by standardizing terminology and unifying mathematical notions used across the field, providing explicit mathematical formulations of definitions, and improving many existing mathematical descriptions. Moreover, we classify ROOT problems based on two ROOT-specific criteria: the requirements for changing or keeping deployed solutions and the number of deployed solutions. This classification helps researchers gain a better understanding of the characteristics and requirements of ROOT problems, which is crucial to systematic algorithm design and benchmarking. Additionally, we classify ROOT methods based on the approach they use for finding robust solutions and provide a comprehensive

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Section: Challenges and Progress In Solving Real-world Root Problemsmentioning

confidence: 99%

Robust Optimization Over Time: A Critical Review

Yazdani,

Omidvar,

Yazdani

et al. 2024

IEEE Trans. Evol. Computat.

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“…Address more real-world (inspired) problems. Except for just a few works like [25][26][27][28][29][30], the rest were focused on artificial problems. Note that addressing a larger number of real-world problems is a way to increase the number of contributions that exploit the opportunities indicated above.…”

Section: Research Opportunitiesmentioning

confidence: 99%

Robust Optimization over Time Problems—Characterization and Literature Review

Novoa-Hernández,

Puris,

Pelta

2023

Electronics

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Robust optimization over time (ROOT) is a relatively recent topic in the field of dynamic evolutionary optimization (EDO). The goal of ROOT problems is to find the optimal solution for several environments at the same time. Although significant contributions to ROOT have been published in the past, it is not clear to what extent progress has been made in terms of the type of problem addressed. In particular, we believe that there is confusion regarding what it actually means to solve a ROOT problem. To overcome these limitations, the objective of this paper is twofold. On the one hand, to provide a characterization framework of ROOT problems in terms of their most relevant features, and on the other hand, to organize existing contributions according to it. As a result, from an initial set of 186 studies, the characterization framework was applied to 35 of them, allowing to identification of some important gaps and proposing new research opportunities. We have also experimentally addressed the effect of available information on ROOT problems, concluding that there is indeed a significant impact on the performance of the algorithm and that the proposed classification is appropriate to characterize the complexity of ROOT problems. To help identify further research opportunities, we have implemented an interactive dashboard with the results of the review conducted, which is available online.

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Racing Strategy for the Dynamic-Customer Location-Allocation Problem

Cited by 2 publications

References 12 publications

Robust Optimization Over Time: A Critical Review

Robust Optimization Over Time: A Critical Review

Robust Optimization over Time Problems—Characterization and Literature Review

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