Research on the impact of green innovation alliance mode on decision-making of two-cycle closed-loop supply chain

Ma, Chunmei; Huang, Dan Ching

doi:10.1007/s10878-019-00496-w

Cited by 8 publications

(1 citation statement)

References 31 publications

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“…A hybrid genetic and imperialist competitive algorithm (HGA) was employed to find a near-optimum solution of a nonlinear integer-programming (NIP) with the objective of minimizing the total cost of the supply chain. Ma and Huang ( 2019 ) presented the life cycle attributes of products into consideration and investigates the situation in which the manufacturer tended to invest in green innovation and establish alliances with other supply chain members. They adopted to analyze and compared the optimal decisions and profits of closed-loop supply chain (CLSC) members as well as the efficiency of three green innovation alliance modes a two-cycle CLSC consisting of one manufacturer, one retailer, and one-third party recycler, Stackelberg game method.…”

Section: Literature Reviewmentioning

confidence: 99%

A sustainable supply chain network considering lot sizing with quantity discounts under disruption risks: centralized and decentralized models

et al. 2022

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This study proposes a framework for the main parties of a sustainable supply chain network considering lot-sizing impact with quantity discounts under disruption risk among the first studies. The proposed problem differs from most studies considering supplier selection and order allocation in this area. First, regarding the concept of the triple bottom line, total cost, environmental emissions, and job opportunities are considered to cover the criteria of sustainability. Second, the application of this supply chain network is transformer production. Third, applying an economic order quantity model lets our model have a smart inventory plan to control the uncertainties. Most significantly, we present both centralized and decentralized optimization models to cope with the considered problem. The proposed centralized model focuses on pricing and inventory decisions of a supply chain network with a focus on supplier selection and order allocation parts. This model is formulated by a scenario-based stochastic mixed-integer non-linear programming approach. Our second model focuses on the competition of suppliers based on the price of products with regard to sustainability. In this regard, a Stackelberg game model is developed. Based on this comparison, we can see that the sum of the costs for both levels is lower than the cost without the bi-level approach. However, the computational time for the bi-level approach is more than for the centralized model. This means that the proposed optimization model can better solve our problem to achieve a better solution than the centralized optimization model. However, obtaining this better answer also requires more processing time. To address both optimization models, a hybrid bio-inspired metaheuristic as the hybrid of imperialist competitive algorithm (ICA) and particle swarm optimization (PSO) is utilized. The proposed algorithm is compared with its individuals. All employed optimizers have been tuned by the Taguchi method and validated by an exact solver in small sizes. Numerical results show that striking similarities are observed between the results of the algorithms, but the standard deviations of PSO and ICA–PSO show better behavior. Furthermore, while PSO consumes less time among the metaheuristics, the proposed hybrid metaheuristic named ICA–PSO shows more time computations in all small instances. Finally, the provided results confirm the efficiency and the performance of the proposed framework and the proposed hybrid metaheuristic algorithm.

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Section: Literature Reviewmentioning

confidence: 99%