Multi-objective optimisation of sustainable closed-loop supply chain networks in the tire industry

Kiani Mavi, Reza; Hosseini Shekarabi, Seyed Ashkan; Kiani Mavi, Neda; Arisian, Sobhan; Moghdani, Reza

doi:10.1016/j.engappai.2023.107116

Cited by 10 publications

(1 citation statement)

References 76 publications

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“…In As for computation time in the testing phase, with the embedding of DRL, the computation time has increased. But in some practical scenarios, such as power system optimization [57] and supply chain management [58], accuracy is more important than timeliness. At the same time, with the increase in computing power, it also provides more chances for pursuing accuracy.…”

Section: Further Analysismentioning

confidence: 99%

Parameter Control Framework for Multiobjective Evolutionary Computation Based on Deep Reinforcement Learning

Zhou,

Zhang,

Niu

et al. 2024

International Journal of Intelligent Systems

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To address the challenge of parameter adjustment in complex environments, this paper introduces a transfer learning-based parameter control framework via deep reinforcement learning for multiobjective evolutionary algorithms (MOEAs). To avoid the requirement for accurate Pareto front information, this framework is proposed with comprehensive global-state information, including basic problem features, the relative position of individuals, the distribution of fitness value, and the grid-IGD. Building on this framework, four reinforced multiobjective evolutionary algorithms (r-MOEAs) are proposed and tested on four DTLZ benchmarks and eight WFG benchmarks. The results of the comparative analyses reveal that compared with the original MOEAs, the four r-MOEAs exhibit faster convergence and stronger robustness. It is also confirmed that our proposed parameter control framework has the capability to learn knowledge from different experiences and improve the performance of MOEAs.

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Section: Further Analysismentioning

confidence: 99%

Parameter Control Framework for Multiobjective Evolutionary Computation Based on Deep Reinforcement Learning

Zhou,

Zhang,

Niu

et al. 2024

International Journal of Intelligent Systems

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The role of agricultural biomass in supply chain decarbonization

Lyu,

Arisian,

et al. 2024

Ann Oper Res

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To achieve supply chain decarbonization, environmentally conscious manufacturers are transforming their energy infrastructure. While certain manufacturers are adopting a pure bioenergy strategy in their production processes, others are opting for a hybrid energy approach that combines traditional energy with bioenergy. This choice is often influenced by limitations in land capacity and the developmental stage of biomass conversion technologies. This paper introduces a game-theoretic model that explores the optimal approach to achieving supply chain decarbonization by strategically selecting energy portfolios. Our findings reveal that in scenarios where the market size is small, manufacturers tend to adopt a hybrid energy strategy, particularly when the average yield of biomass is low. However, as the biomass yield increases, manufacturers lean towards a hybrid (or pure) bioenergy strategy in smaller (or larger) markets. In larger markets, the manufacturer’s energy strategies become more complex and are influenced by various factors. Our results emphasize that farmers should base their planting decisions on considerations such as available land, initial investment, and agricultural biomass yield. This paper urges manufacturers to effectively navigate the complexities of the carbon tax policy and make informed decisions that promote a sustainable energy strategy. By utilizing technological advancements, governments and manufacturers can collect and analyze data on factors such as market size, biomass yield, and carbon tax policy, ultimately working towards a more efficient, productive, and environmentally sustainable future.

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Assessing the economic and environmental performance of a closed-loop supply chain for waste tires: an industrial case study

Aydin,

Al-Shihabi,

Alzarooni

et al. 2024

Environ Sci Pollut Res

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Multi-objective optimisation of sustainable closed-loop supply chain networks in the tire industry

Cited by 10 publications

References 76 publications

Parameter Control Framework for Multiobjective Evolutionary Computation Based on Deep Reinforcement Learning

Parameter Control Framework for Multiobjective Evolutionary Computation Based on Deep Reinforcement Learning

The role of agricultural biomass in supply chain decarbonization

Assessing the economic and environmental performance of a closed-loop supply chain for waste tires: an industrial case study

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