Unrelated parallel batch processing machine scheduling for production systems under carbon reduction policies: NSGA-II and MOGWO metaheuristics

Fallahi, Ali; Shahidi-Zadeh, B.; Niaki, Seyed Taghi Akhavan

doi:10.1007/s00500-023-08754-0

Cited by 11 publications

(4 citation statements)

References 76 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Several approaches have been developed in the literature to address the conflicts of objectives in multi-objective optimization problems. Among these approaches, fuzzy techniques are becoming more popular due to their ability to determine the satisfaction level of any objective function directly (46,47). This allows decision-makers to calculate Pareto solutions depending on their choices (i.e., the relative degree of importance of objectives).…”

Section: Fuzzy Programming Multi-objective Approachmentioning

confidence: 99%

A Multi-product Humanitarian Supply Chain Network Design Problem: A Fuzzy Multi-objective and Robust Optimization Approach

Fallahi,

Pourghazi,

Mokhtari

2024

IJE

View full text Add to dashboard Cite

In today's dynamic and unpredictable world, the planning and management of humanitarian supply chains hold paramount importance. Efficient logistics management is crucial for effectively delivering essential aid and resources to affected areas during disasters and emergencies, ensuring timely support and relief to vulnerable populations. In this research, we addressed a novel humanitarian supply chain network design problem that considers product differentiation and demand uncertainty. Specifically, we simultaneously incorporate non-perishable, perishable, and blood products as critical components of the network. The problem is formulated as a multi-objective mixed-integer linear programming model aiming to minimize the total cost and total traveled distance of products by making location, allocation, and production decisions. To enhance realism, we account for demand uncertainty in affected areas. To tackle this challenging problem, we proposed a two-phase solution methodology. Firstly, we employed a robust optimization approach to establish a deterministic counterpart for the stochastic model. Subsequently, an efficient fuzzy programming-based approach reformulates the model into a singleobjective form, effectively accommodating decision-makers' preferences. Numerical instances are solved to investigate the performance of the model and solution methodologies. The results demonstrate the effectiveness of our fuzzy approach in finding non-dominated solutions, enabling decision-makers to explore trade-offs. Also, sensitivity analyses were conducted to provide more insights. Finally, some suggestions are presented to extend the current work by feature researchers.

show abstract

Section: Fuzzy Programming Multi-objective Approachmentioning

confidence: 99%

A Multi-product Humanitarian Supply Chain Network Design Problem: A Fuzzy Multi-objective and Robust Optimization Approach

Fallahi,

Pourghazi,

Mokhtari

2024

IJE

View full text Add to dashboard Cite

show abstract

“…Yang et al used multi-objective Ant Lion Optimizer (MOALO) to optimize their prediction results (Yang H. et al, 2022). Meanwhile, Fallahi et al also used the Multi-Objective Grey Wolf Optimizer (MOGWO) to improve the accuracy of carbon emission reduction prediction results (Fallahi et al, 2023). In addition to multi-objective optimization ensemble models, Liang Dua et al successfully synthesized ten candidate models using a Bayesian dynamic ensemble method, which has higher adaptability and better generalization performance (Du et al, 2022).…”

Section: Ensemble Modelmentioning

confidence: 99%

A novel machine learning ensemble forecasting model based on mixed frequency technology and multi-objective optimization for carbon trading price

Li,

Long,

2024

Front. Energy Res.

View full text Add to dashboard Cite

Carbon trading prices are crucial for carbon emissions and transparent carbon market pricing. Previous studies mainly focused on data mining in the prediction direction to quantify carbon trading prices. Although the prospect of high-frequency data forecasting mechanisms is considerable, more mixed-frequency ensemble forecasting is needed for carbon trading prices. Therefore, this article designs a new type of ensemble prediction model to increase the scope of model research. The module is divided into three parts: data denoising, mixed frequency and machine learning, multi-objective optimization, and ensemble forecasting. Precisely, the data preprocessing technology enhanced by adopting a self-attention mechanism can better remove noise and extract effective features. Furthermore, mixed frequency technology is introduced into the machine learning model to achieve more comprehensive and efficient prediction, and a new evaluation criterion is proposed to measure the optimal submodel. Finally, the ensemble model based on deep learning strategy can effectively integrate the advantages of high-frequency and low-frequency data in complex datasets. At the same time, a new multi-objective optimization algorithm is proposed to optimize the parameters of the ensemble model, significantly improving the predictive ability of the integrated module. The results of four experiments and the Mean Absolute Percent Error index of the proposed model improved by 28.3526% compared to machine learning models, indicating that the ensemble model established can effectively address the time distribution characteristics and uncertainty issues predicted by carbon trading price models, which helps to mitigate climate change and develop a low-carbon economy.

show abstract

“…For example, the electrical cost measure has been studied recently in [67,72,73] in diffusion operations, because of its high energy consumption. Machine scheduling under carbon reduction policies is examined in [211]. Problems could also be examined more based on multiple measures.…”

Section: New Problems Orientedmentioning

confidence: 99%

Problems and Solution Methods of Machine Scheduling in Semiconductor Manufacturing Operations: A Survey

Fang,

Cheang,

Lim

2023

Sustainability

View full text Add to dashboard Cite

Machine scheduling problems associated with semiconductor manufacturing operations (SMOs) are one of the major research topics in the scheduling literature. Lots of papers have dealt with different variants of SMOs’ scheduling problems, which are generally difficult to tackle theoretically and computationally. In this paper, the single machine, parallel machines, flow shops, and job shops scheduling problems from SMOs have been reviewed, based on different processing constraints, e.g., batch processing, auxiliary resources, incompatible job families, and reentrant flow, etc., with the cycle time, flow time, and throughput-related performance measures. Given the vast and diverse nature of the current literature, it is urgently needed to make a systematic survey in order to identify the important research problems, research trends, and the progress of the related solution methods, as well as clarify future research perspectives. We hope the findings and observations could provide some insights to the researchers and practitioners in this domain.

show abstract

Unrelated parallel batch processing machine scheduling for production systems under carbon reduction policies: NSGA-II and MOGWO metaheuristics

Cited by 11 publications

References 76 publications

A Multi-product Humanitarian Supply Chain Network Design Problem: A Fuzzy Multi-objective and Robust Optimization Approach

A Multi-product Humanitarian Supply Chain Network Design Problem: A Fuzzy Multi-objective and Robust Optimization Approach

A novel machine learning ensemble forecasting model based on mixed frequency technology and multi-objective optimization for carbon trading price

Problems and Solution Methods of Machine Scheduling in Semiconductor Manufacturing Operations: A Survey

Contact Info

Product

Resources

About