Coordinated scheduling of production and logistics for large-scale closed-loop manufacturing using Benders decomposition optimization

Lu, Shan; Chen, Chen; Wang, Yue; Li, Zhe; Li, Xinchao

doi:10.1016/j.aei.2022.101848

Cited by 3 publications

(3 citation statements)

References 36 publications

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“…In terms of economic phenomena, Kim and Chung [13] established a supply chain model to investigate the impact of backflow on strengthening the manufacturing industry and address issues, such as economic recession and job shortages, in order to determine whether manufacturing centers, suppliers, and reverse logistics facilities should be relocated from the host country to their home country, and maximize total profits based on the level of backflow drivers in the traditional manufacturing industry. Lu et al [14] studied the production and logistics coordination scheduling problem for a large-scale closed-loop manufacturing system, designed a decentralized network, and expressed the coordination scheduling problem as a mixed integer programming model that includes both binary and integer variables. They developed an iterative solution method based on Bender's decomposition which improves the solution efficiency in large-scale situations by updating custom constraints.…”

Section: Closed-loop Logisticsmentioning

confidence: 99%

A Necessity-Based Optimization Approach for Closed-Loop Logistics Considering Carbon Emission Penalties and Rewards under Uncertainty

Li,

Liu,

Chen

et al. 2023

Mathematics

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The recycling of waste products can bring enormous economic and environmental benefits to supply chain participants. Under the government’s reward and punishment system, the manufacturing industry is facing unfolded pressure to minimize carbon emissions. However, various factors related to the design of closed-loop logistics networks are uncertain in nature, including demand, facility capacity, transportation cost per unit of product per kilometer, landfill cost, unit carbon penalty cost, and carbon reward amount. As such, this study proposes a new fuzzy programming model for closed-loop supply chain network design which directly relies on fuzzy methods based on the necessity measure. The objective of the proposed optimization model is to minimize the total cost of the network and the sum of carbon rewards and penalties when selecting facility locations and transportation routes between network nodes. Based on the characteristics of the problem, a genetic algorithm based on variant priority encoding is proposed as a solution. This new solution encoding method can make up for the shortcomings of the four traditional encoding methods (i.e., Prüfer number-based encoding, spanning tree-based encoding, forest data structure-based encoding, and priority-based encoding) to speed up the computational time of the solution algorithm. Several alternative solution approaches were considered to evaluate the proposed algorithm including the precision optimization method (CPLEX) and priority-based encoding genetic algorithm. The results of numerous experiments indicated that even for large-scale numerical examples, the proposed algorithm can create optimal and high-quality solutions within acceptable computational time. The applicability of the model was demonstrated through a sensitivity analysis which was conducted by changing the parameters of the model and providing some important management insights. When external parameters change, the solution of the model maintains a certain level of satisfaction conservatism. At the same time, the changes in the penalty cost and reward amount per unit of carbon emissions have a significant impact on the carbon penalty revenue and total cost. The results of this study are expected to provide scientific support to relevant supply chain enterprises and stakeholders.

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Section: Closed-loop Logisticsmentioning

confidence: 99%

A Necessity-Based Optimization Approach for Closed-Loop Logistics Considering Carbon Emission Penalties and Rewards under Uncertainty

Li,

Liu,

Chen

et al. 2023

Mathematics

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“…Production scheduling optimization algorithms help determine the optimal sequence and timing of manufacturing operations. Additionally, optimization is utilized in capacity plan-ning, facility layout design, and supply-chain optimization to streamline operations and minimize waste [29][30][31].…”

mentioning

confidence: 99%

“…Compute the best vertex y 1 using Equation(30); Compute the worst vertex y D+1 using Equation(31);Compute the next-worst vertex y D using Equation(32);Compute the centroid x excluding the worst vertex using Equation(33); Compute the reflection vertex x r using Equation (5); xr using the selected opposition-based learning strategy (Equations (…”

mentioning

confidence: 99%

Integrating the Opposition Nelder–Mead Algorithm into the Selection Phase of the Genetic Algorithm for Enhanced Optimization

Zitouni,

Harous

2023

ASI

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In this paper, we propose a novel methodology that combines the opposition Nelder–Mead algorithm and the selection phase of the genetic algorithm. This integration aims to enhance the performance of the overall algorithm. To evaluate the effectiveness of our methodology, we conducted a comprehensive comparative study involving 11 state-of-the-art algorithms renowned for their exceptional performance in the 2022 IEEE Congress on Evolutionary Computation (CEC 2022). Following rigorous analysis, which included a Friedman test and subsequent Dunn’s post hoc test, our algorithm demonstrated outstanding performance. In fact, our methodology exhibited equal or superior performance compared to the other algorithms in the majority of cases examined. These results highlight the effectiveness and competitiveness of our proposed approach, showcasing its potential to achieve state-of-the-art performance in solving optimization problems.

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Omni-fulfillment network design for supply chain service platform considering cost, service and resilience tradeoffs

Jiang

Kong

Chan

2023

Advanced Engineering Informatics

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Coordinated scheduling of production and logistics for large-scale closed-loop manufacturing using Benders decomposition optimization

Cited by 3 publications

References 36 publications

A Necessity-Based Optimization Approach for Closed-Loop Logistics Considering Carbon Emission Penalties and Rewards under Uncertainty

A Necessity-Based Optimization Approach for Closed-Loop Logistics Considering Carbon Emission Penalties and Rewards under Uncertainty

Integrating the Opposition Nelder–Mead Algorithm into the Selection Phase of the Genetic Algorithm for Enhanced Optimization

Omni-fulfillment network design for supply chain service platform considering cost, service and resilience tradeoffs

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