Plane Separation: A method to solve dynamic multi-objective optimization problems with incorporated preferences

Macias-Escobar, Teodoro; Cruz-Reyes, Laura; Fraire, Héctor; Dorronsoro, Bernabè

doi:10.1016/j.future.2019.10.039

Cited by 13 publications

(4 citation statements)

References 15 publications

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“…The result of multiobjective planning was solved by linear weighted transformation. Through the multiple search algorithm, it is found that the best results are obtained at 7 : 2 : 1 by using plane separation [12]. Based on the allocation of the supply selection status for that week, the supply efficiency is calculated using the total supply forecast table as follows in (10).…”

Section: Results Of Multiobjective Planningmentioning

confidence: 99%

The Optimal Supply Decision Based on Dynamic Multiobjective Optimization and Prediction

Cheng

Zhou

et al. 2022

Journal of Function Spaces

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Different material supply-related decisions intensively affect the efficiency of manufacturers. To obtain a suitable supply-related protocol, this study proposes a supply selection model which considers both manufacturers’ development orientation and material ordering. In contrast to traditional approaches that rely on expert opinions, the proposed approach in this study allows the time series analysis (ARIMA) to forecast the trend in manufacturers’ development during the execution of the plan. Based on the predicted trend, taking the minimum of total material management cost as the objective function, the control function and optimization conditions are constructed to select the appropriate protocol. The dynamic prediction protocol is obtained by considering the variation in production and material costs by an evolutionary algorithm. The model enables users to determine material supply protocol in continuous time and autonomously adapt to changes in the manufacturers’ production goals within a lower convergence time.

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Section: Results Of Multiobjective Planningmentioning

confidence: 99%

The Optimal Supply Decision Based on Dynamic Multiobjective Optimization and Prediction

Cheng

Zhou

et al. 2022

Journal of Function Spaces

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“…Macias-Escobar et al [94] propose the plane separation (PS) method that can incorporate preferences in the optimization process by splitting the population into multiple planes based on the proximity of the solutions to a region of interest (ROI). PS uses the planes to focus on the search towards the ROI while maintaining the diversity in the solutions set to avoid stagnation in local optima.…”

Section: Endogenous-exogenous Hybrid Dmoeasmentioning

confidence: 99%

Dynamic Multi-Objective Evolutionary Algorithms: An Overview

Guerrero-Peña,

Araújo,

Garrozi

2023

Learn. Nonlin. Mod.

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Evolutionary algorithms have been widely explored and applied in optimization problems. The introduction of multi-objective evolutionary algorithms (MOEAs) has facilitated the adaptation and creation of new methods to handle more complex and realistic optimizations, such as dynamic multi-objective optimization problems (DMOPs). A dynamic MOEA (DMOEA) can be constructed by changing the MOEA structure and variation operators used to solve DMOPs. Furthermore, DMOEAs can implement change-detection strategies and mechanisms to handle the dynamics of the environment. DMOEAs are often designed to solve unconstrained DMOPs. However, several studies have been conducted to solve dynamic constrained MOPs and more recently, to solve dynamic many-objective optimization problems. There are many real-world DMOPs; however, few DMOEAs have been evaluated with respect to those problems. Evaluation of algorithm performance is an essential aspect of using DMOEAs. Several measures used to validate MOEAs have been adapted to assess DMOEAs and quantitatively compare them. This paper presents a broad review of DMOEAs. The information is summarized and organized according to current research branches. Furthermore, a more general classification scheme for DMOEAs is proposed, based on the method of incorporating diversity to solve DMOPs.

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“…It remains unclear what the maximum capability knee points can provide if the (approximately) true knee points are correctly identiied. In addition, only a few studies [107,115] have investigated preference incorporation in EDMO to ease decision making challenges [55]. This research direction can be further studied.…”

Section: Opportunitiesmentioning

confidence: 99%

Evolutionary Dynamic Multi-objective Optimisation: A Survey

et al. 2022

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Evolutionary dynamic multi-objective optimisation (EDMO) is a relatively young but rapidly growing area of investigation. EDMO employs evolutionary approaches to handle multi-objective optimisation problems that have time-varying changes in objective functions, constraints and/or environmental parameters. Due to the simultaneous presence of dynamics and multi-objectivity in problems, the optimisation difficulty for EDMO has a marked increase compared to that for single-objective or stationary optimisation. After nearly two decades of community effort, EDMO has achieved significant advancements on various topics, including theoretic research and applications. This paper presents a broad survey and taxonomy of existing research on EDMO. Multiple research opportunities are highlighted to further promote the development of the EDMO research field.

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Plane Separation: A method to solve dynamic multi-objective optimization problems with incorporated preferences

Cited by 13 publications

References 15 publications

The Optimal Supply Decision Based on Dynamic Multiobjective Optimization and Prediction

The Optimal Supply Decision Based on Dynamic Multiobjective Optimization and Prediction

Dynamic Multi-Objective Evolutionary Algorithms: An Overview

Evolutionary Dynamic Multi-objective Optimisation: A Survey

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