Multi-objective modelling of sustainable closed-loop supply chain network with price-sensitive demand and consumer’s incentives

Mogale, D. G.; De, Arijit; Ghadge, Abhijeet; Aktaş, Emel

doi:10.1016/j.cie.2022.108105

Cited by 50 publications

(8 citation statements)

References 78 publications

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“…Tey used the NSGA-II algorithm to solve the model, and the results indicated a good performance in terms of maximum expansion and distancing. Mogale et al [16] provided a CLSCs network, in which, the demand was considered sensitive to the price, consumer motivation, and the quality level. Te core goal of the proposed model is to reduce the total cost and carbon emissions produced by the activities resulting from production, distribution, transportation, and disposal.…”

Section: Literature Reviewmentioning

confidence: 99%

Designing a Scenario-Based Fuzzy Model for Sustainable Closed-Loop Supply Chain Network considering Statistical Reliability: A New Hybrid Metaheuristic Algorithm

et al. 2023

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In this study, a new nonlinear mathematical programming model of mixed integer was presented to formulate the problem of designing a sustainable closed loop supply chain, in which the three aspects of sustainability, i.e., social effect such as job creation, customer satisfaction, and distributors, environmental effects such as reducing air pollution, and economic effects such as reducing supply chain costs, increasing supply chain reliability, quality of returned products by customers, and product routing were considered. In order to solve the proposed model, a new hybrid metaheuristic algorithm based on the distinctive features of gray wolf algorithm and genetic algorithm was proposed in addition to MOPSO and NSGA-II algorithms. After tuning their parameters by the Taguchi method, their performance in problems with different dimensions was tested and evaluated by MID, DM, and SM criteria. The results of statistical analysis of indices indicated that no significant difference between the performance of the three algorithms at 5% error level. In general, GW-NS, NSGA-II and MOPSO algorithms had better performance in terms of MID index, respectively. In addition, GW-NS, NSGA-II, and MOPSO algorithms performed better in terms of DM index. NSGA-II, MOPSO, and GW-NS algorithms performed better in terms of SM index, respectively. In addition, the variability of DM index in all three algorithms was almost the same, but in MID index, GW-NS algorithm, and in SM index, MOPSO algorithm had the highest change and less sustainability.

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Section: Literature Reviewmentioning

confidence: 99%

Designing a Scenario-Based Fuzzy Model for Sustainable Closed-Loop Supply Chain Network considering Statistical Reliability: A New Hybrid Metaheuristic Algorithm

et al. 2023

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“…Various approaches have been proposed to estimate crowd density; the previous studies employ image processing techniques. In this approach, after removing the image background and thinning the edges, the number of individual objects in the image was approximated using linear regression of the number of pixels [21][22][23]. The other methods relied on the detection of individual objects.…”

Section: Related Workmentioning

confidence: 99%

Crowd Density Estimation in Spatial and Temporal Distortion Environment Using Parallel Multi-Size Receptive Fields and Stack Ensemble Meta-Learning

et al. 2022

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The estimation of crowd density is crucial for applications such as autonomous driving, visual surveillance, crowd control, public space planning, and warning visually distracted drivers prior to an accident. Having strong translational, reflective, and scale symmetry, models for estimating the density of a crowd yield an encouraging result. However, dynamic scenes with perspective distortions and rapidly changing spatial and temporal domains still present obstacles. The main reasons for this are the dynamic nature of a scene and the difficulty of representing and incorporating the feature space of objects of varying sizes into a prediction model. To overcome the aforementioned issues, this paper proposes a parallel multi-size receptive field units framework that leverages the majority of the CNN layer’s features, allowing for the representation and participation in the model prediction of the features of objects of all sizes. The proposed method utilizes features generated from lower to higher layers. As a result, different object scales can be handled at different framework depths, and various environmental densities can be estimated. However, the inclusion of the vast majority of layer features in the prediction model has a number of negative effects on the prediction’s outcome. Asymmetric non-local attention and the channel weighting module of a feature map are proposed to handle noise and background details and re-weight each channel to make it more sensitive to important features while ignoring irrelevant ones, respectively. While the output predictions of some layers have high bias and low variance, those of other layers have low bias and high variance. Using stack ensemble meta-learning, we combine individual predictions made with lower-layer features and higher-layer features to improve prediction while balancing the tradeoff between bias and variance. The UCF CC 50 dataset and the ShanghaiTech dataset have both been subjected to extensive testing. The results of the experiments indicate that the proposed method is effective for dense distributions and objects of various sizes.

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“…This becomes more important given the higher price of reusable containers as compared to the conventional packages (Rausch et al 2021). In most of the markets, there is a need for some form of incentives to motivate consumers to take part in sustainability initiatives (Mogale et al 2022).…”

Section: Introductionmentioning

confidence: 99%

The U.S. Government's Program of Welfare for the Wealthy

Benton,

Stasch

2024

Journal of Macromarketing

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It is important that macromarketers understand how income and wealth inequality affect the way people think, live, and die. It is also important that they understand the source(s) of that inequality. This paper discusses how the power elite and the rich used the law to strategically withhold from the bottom 99 percent of the population the wealth generated in the post-World War II economy. This included more than a dozen specific stealth activities the U.S. government enacted from 1970 to 2010, although only a few are highlighted in this commentary. These activities created a Program of Welfare for the Wealthy that worked against the general welfare of the American people. We refer to these as stealth activities because much of what happens in government occurs behind the scenes, in the dark.

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Multi-objective modelling of sustainable closed-loop supply chain network with price-sensitive demand and consumer’s incentives

Cited by 50 publications

References 78 publications

Designing a Scenario-Based Fuzzy Model for Sustainable Closed-Loop Supply Chain Network considering Statistical Reliability: A New Hybrid Metaheuristic Algorithm

Designing a Scenario-Based Fuzzy Model for Sustainable Closed-Loop Supply Chain Network considering Statistical Reliability: A New Hybrid Metaheuristic Algorithm

Crowd Density Estimation in Spatial and Temporal Distortion Environment Using Parallel Multi-Size Receptive Fields and Stack Ensemble Meta-Learning

The U.S. Government's Program of Welfare for the Wealthy

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