Assessment of third-party logistics providers by introducing a new stochastic two-phase compromise solution model with last aggregation

Seyed Meysam Mousavi | Mohammadkhani         A.

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URL :   http://research.shahed.ac.ir/WSR/WebPages/Report/PaperView.aspx?PaperID=169782
Date :  2022/06/07
Publish in :    Computers & Industrial Engineering

Link :  https://www.sciencedirect.com/science/article/abs/pii/S0360835222003783
Keywords :Reverse logistics networks, Third-party reverse logistics providers, Multi-attributes group decision making, Stochastic uncertainty, VIKOR method, CRITIC method.

Abstract :

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In the conditions of increasing governments and consumers awareness of environmental protection and sustainability, the enterprises have been encouraged to devote more attention to reverse logistics (RL) activities. Assessment of third-party reverse logistics providers (3PRLPs) is a crucial decision strategy for companies who choose to outsource their RL activities to third-party partners. To ensure accurate information is reflected in the decision process, it is imperative to consider uncertain information. To this aim, this paper draws attention to the selection of the most preferred RL providers using a novel comprehensive stochastic compromise solution by considering the uncertainty associated with decision variables. This paper presents a novel multi-attribute group decision-making (MAGDM) framework by integrating a new stochastic criteria importance through inter-criteria correlation (CRITIC) method and an innovative last aggregation stochastic two-phase compromise solution based on the visekriterijumsko kompromisno rangiranje (in Serbian) (VIKOR) approach to provide systematic decision support for companies to select the most preferred RL providers. In the proposed framework, the CRITIC method is utilized to calculate the priority weights of the attributes. The innovative last aggregation stochastic two-phase compromise solution is employed to evaluate decision-makers (DMs) and alternatives simultaneously. Moreover, the proposed compromise method exploits the pessimistic, most likely, and optimistic solutions to deal with stochastic uncertainty and provide DMs with more flexibility in the final evaluation. The approach is also validated by applying a sample case for selecting 3PRLPs along with sensitivity analysis on the weights of the different attributes in order to investigate the robustness of the results. Consequently, to demonstrate the reliability of the suggested approach, comparative analyses of the final ranking are conducted. The outcome of the study reveals the proposed model provides a viable, high-performance, and flexible decision-making process, by which the DMs can provide more prompt and correct judgments on the selection of the best 3PRLPs.