Multi-objective optimization of closed-loop supply chains to achieve sustainable development goals in uncertain environments

Khalili-Fard, Alireza; Parsaee, Sarah; Bakhshi, Alireza; Yazdani, Maziar; Aghsami, Amir; Rabbani, Masoud

doi:10.1016/j.engappai.2024.108052

Cited by 14 publications

(2 citation statements)

References 94 publications

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“…The modified epsilon-constraint method called AUGMECON by Mavrotas, G [47] is used to solve the RRM. This method is widely acknowledged for solving multi-objective problems [48,49]. The detailed steps are as follows:…”

Section: Solution Proceduresmentioning

confidence: 99%

Multi-Objective Evcuation Planning Model Considering Post-Earthquake Fire Spread: A Tokyo Case Study

Tang,

Osaragi

2024

Sustainability

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As an integral part of the 2030 Agenda for Sustainable Development, Disaster Risk Reduction (DRR) is essential for human safety and city sustainability. In recent years, natural disasters, which have had a tremendous negative impact on economic and social development, have frequently occurred in cities. As one of these devastating disasters, earthquakes can severely damage the achievements of urban development and impact the sustainable development of cities. To prepare for potential large earthquakes in the future, efficient evacuation plans need to be developed to enhance evacuation efficiency and minimize casualties. Most previous research focuses on minimization of distance or cost while ignoring risk factors. We propose a multi-objective optimization model with the goal of reducing the risk during the evacuation process, which is called the risk reduction model (RRM). Problem-specific indicators for screening optimal solutions are introduced. The research selects the Ogu area in Tokyo as a case study, where there is a relatively high density of wooden structures, increasing the risks of building collapse and fire spread after an earthquake, and is based on a two-phase evacuation flow that considers secondary evacuation for fire response. The results indicate that, in this case, RRM can, in most situations, reduce the risk level during the evacuation process and improve evacuation efficiency and success rate without significantly increasing the total evacuation distance. It proves to be superior to the traditional distance minimization model (DMM), which prioritizes minimizing the total distance as the objective function.

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Section: Solution Proceduresmentioning

confidence: 99%

Multi-Objective Evcuation Planning Model Considering Post-Earthquake Fire Spread: A Tokyo Case Study

Tang,

Osaragi

2024

Sustainability

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“…With the continuous development of metaheuristic algorithms, these algorithms play a crucial role in a variety of fields, such as path planning [2,3], image segmentation [4,5], feature selection [6,7], neural network hyperparameter optimization [8,9], task allocation [10,11], supply chain management [12,13], waste collection [14], wireless sensor optimization problems [15,16], and antenna array synthesis issues [17,18]. And, they show great potential in promoting the development of engineering technology, improving productivity, and solving multi-objective optimization problems [19,20]. Their flexibility and adaptability enable the provision of solutions for different types of problems, ensuring that they play a vital role in practical applications.…”

Section: Introductionmentioning

confidence: 99%

Improved Multi-Strategy Sand Cat Swarm Optimization for Solving Global Optimization

Zhang,

He,

Wang

et al. 2024

Biomimetics

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The sand cat swarm optimization algorithm (SCSO) is a novel metaheuristic algorithm that has been proposed in recent years. The algorithm optimizes the search ability of individuals by mimicking the hunting behavior of sand cat groups in nature, thereby achieving robust optimization performance. It is characterized by few control parameters and simple operation. However, due to the lack of population diversity, SCSO is less efficient in solving complex problems and is prone to fall into local optimization. To address these shortcomings and refine the algorithm’s efficacy, an improved multi-strategy sand cat optimization algorithm (IMSCSO) is proposed in this paper. In IMSCSO, a roulette fitness–distance balancing strategy is used to select codes to replace random agents in the exploration phase and enhance the convergence performance of the algorithm. To bolster population diversity, a novel population perturbation strategy is introduced, aiming to facilitate the algorithm’s escape from local optima. Finally, a best–worst perturbation strategy is developed. The approach not only maintains diversity throughout the optimization process but also enhances the algorithm’s exploitation capabilities. To evaluate the performance of the proposed IMSCSO, we conducted experiments in the CEC 2017 test suite and compared IMSCSO with seven other algorithms. The results show that the IMSCSO proposed in this paper has better optimization performance.

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Data-driven robust optimization for a sustainable steel supply chain network design: Toward the circular economy

Khalili-Fard,

Sabouhi,

Bozorgi-Amiri

2024

Computers & Industrial Engineering

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Multi-objective optimization of closed-loop supply chains to achieve sustainable development goals in uncertain environments

Cited by 14 publications

References 94 publications

Multi-Objective Evcuation Planning Model Considering Post-Earthquake Fire Spread: A Tokyo Case Study

Multi-Objective Evcuation Planning Model Considering Post-Earthquake Fire Spread: A Tokyo Case Study

Improved Multi-Strategy Sand Cat Swarm Optimization for Solving Global Optimization

Data-driven robust optimization for a sustainable steel supply chain network design: Toward the circular economy

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