Analysis of a Multi-channel Closed Loop Green Supply Chain Using Modified Particle Swarm Optimization Algorithm

Niranjan, T.; Thanigaivelan, R.; Singaravel, B.

doi:10.1007/978-981-16-7282-8_60

Cited by 3 publications

(2 citation statements)

References 16 publications

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“…Sedak et al [33] improved the variational algorithm of the differential evolutionary algorithm to the velocity update equation of the particle swarm algorithm and used the adaptive population spacing parameter to select the appropriate variational operator, for which a hybrid particle swarm differential evolutionary algorithm was proposed to solve the mul-tiobjective optimization. Ant Colony Optimization (ACO) has good robustness and is not easy to fall into local optima [34][35][36]. Therefore, the combination of particle swarm Optimi-zation and Ant Colony Optimization can effectively escape from local optima.…”

Section: Plos Onementioning

confidence: 99%

Multi-objective sustainable supply chain network optimization based on chaotic particle—Ant colony algorithm

et al. 2023

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For the optimal design of the sustainable supply chain network, considering the comprehensiveness of the problem factors, considering the three aspects of economy, environment and society, the goal is to minimize the establishment cost, minimize the emission of environ-mental pollution and maximize the number of labor. A mixed integer programming model is established to maximize the efficiency of the supply chain network. The innovation of this paper, first, is to consider the impact of economic, environmental and social benefits in a continuous supply chain, where the environmental benefits not only consider carbon emissions but also include the emissions of plant wastewater, waste and solid waste as influencing factors. Second, a multi-objective fuzzy affiliation function is constructed to measure the quality of the model solution in terms of the overall satisfaction value. Finally, the chaotic particle ant colony algorithm is proposed, and the problem of premature convergence in the operation of the particle swarm algorithm is solved. Experimental results show that the PSCACO algorithm proposed in this paper is compared with MOPSO, CACO and NSGA-II algorithms, and the convergence effect of the algorithm is concluded to be more effective to verify the effectiveness and feasibility of chaotic particle ant colony algorithm for solving multi-objective functions, which proposes a new feasible solution for the supply chain management.

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Section: Plos Onementioning

confidence: 99%

Multi-objective sustainable supply chain network optimization based on chaotic particle—Ant colony algorithm

et al. 2023

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“…In order to explore the research questions, this paper considers a multichannel CLSC system consisting of a manufacturer, multiple sub-retailers of a chain retailer, and a third-party recycling company, and establishes profit models of each player to determine the optimal pricing decision with uncertain demand under multi-channel cross influence. As for multichannel retailing, one of the difficulties is to solve the model and there are some mature solution algorithms at present (Nia et al, 2014; Niranjan & Parthiban, 2019; Sadeghi et al, 2014). Different from them, considering the robustness of the solution results, CPLEX optimizer is used in this paper.…”

Section: Introductionmentioning

confidence: 99%

Price Optimization of Closed-loop Supply Chain With Uncertain Demand Under Multi-channel Cross Influence

Shu,

Liang,

Yao

et al. 2023

SAGE Open

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Face ubiquitous demand uncertainty and channel cross-influence, how to make price decision to improve profits has become an issue faced by decision makers. This paper concerns multichannel price optimization for a closed-loop supply chain and constructs the deterministic and uncertain models. Taking a real closed-loop clothing supply chain as an example, we carry out parameter assumptions and numerical experiments and analyze the simulation results. Our analysis reveals: (1) The supply chain is more profitable under centralized policy. The company can maximize profits by expanding upstream and downstream. (2) When experienced experts give more accurate error terms, the companies can accurately predict the demand and make price decisions. (3) Raising wholesale price will not maintain manufacturer’s profits growing. Beyond the threshold, the manufacturer’s profit will decrease. (4) The channel cross-influence is beneficial to the supply chain. When other factors remain unchanged, the greater the cross price-demand sensitivity coefficient in a channel, the higher the retailing price and the total profit of the supply chain will be. Decision makers can adjust pricing in time to adapt to changes of cross-influence and uncertain demand. The findings provide theoretical guidance for each player in the closed-loop supply chain.

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Investigation of vegetable oil as dielectric fluid in electric discharge machining

Niranjan¹,

Singaravel²,

Chakradhar³

et al. 2023

Materials Today: Proceedings

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Analysis of a Multi-channel Closed Loop Green Supply Chain Using Modified Particle Swarm Optimization Algorithm

Cited by 3 publications

References 16 publications

Multi-objective sustainable supply chain network optimization based on chaotic particle—Ant colony algorithm

Multi-objective sustainable supply chain network optimization based on chaotic particle—Ant colony algorithm

Price Optimization of Closed-loop Supply Chain With Uncertain Demand Under Multi-channel Cross Influence

Investigation of vegetable oil as dielectric fluid in electric discharge machining

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