With the background of implementing carbon peaking and carbon neutralization, identifying methods to realize energy-saving and carbon reduction effectively has become an important issue in the intelligent energy-conservation manufacturing industry. During the process of achieving this goal, determining an optimal location for a low-carbon and intelligent manufacturing industrial park is a foremost decision-making problem for manufacturing corporations’ energy-efficient development. The article established a multi-criteria decision framework to assist manufacturing companies when selecting suitable industrial park sites. To begin with, an evaluation criteria framework is confirmed by literature search. Then, a fuzzy optimization model, which combines the fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and the fuzzy VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) is presented, where fuzzy TOPSIS is used to determine the decision-maker criteria weights. Then, criteria weights are calculated by the optimization model with construction of a Lagrange function. Moreover, the fuzzy VIKOR method is applied to sort alternatives and choose the best alternative location. In addition, five alternative sites for a manufacturing company are evaluated and ranked according to the values of the ranking index as a numerical case to demonstrate the proposed framework’s application. Finally, a comprehensive analysis of diverse methods and sensitivity analyses for the volatility in criteria weights and decision-maker weights is illustrated to confirm that the framework is practicable for the problem of intelligent and sustainable manufacturing industrial park-site selection.

The multiple attribute decision making (MADM) with interval grey uncertain linguistic (IGUL) is a topic of current interest, and various methods have been developed. However, few approaches taking the behavioral characteristics of the decision maker into account. In this paper, an extension of TODIM (i.e., an acronym in Portuguese of interactive and multiple attribute decision making) method, in which three behavioral characteristics of the decision maker (i.e., risk aversion, reference dependence and loss aversion) are considered, is proposed. First, the δ -Hamming distance is defined to deal with the interval grey uncertain linguistic variables by considering the level of the decision maker’s risk aversion, and its distinguish ability is validated by compared to the classical Hamming distance. Then, the details of the TODIM· SIR method is demonstrated: (i) considering the reference dependence behaviour of the decision maker, the positive-ideal alternative (i.e., PIA ) and the negative-ideal alternative (i.e., NIA ) are defined, and the gain and loss degrees of each alternative relative to NIA and PIA are computed based on the δ -Hamming distance; (ii) taking the loss aversion behaviour of the decision maker into account, the perceived dominance degree of the decision maker for the gain and the loss is calculated; (iii) according to the idea of the Superiority and Inferiority Ranking method (i.e., SIR, an outranking method), the Gain-flow and the Loss-flow are defined, and the partial ranking orders and the complete ranking order are obtained. Finally, two numerical examples are given to illustrate the robustness and validity of the method, and a comparative analysis is also conducted to compare the TODIM· SIR method with both the classical TODIM method and the classical SIR method.

Global retail e-commerce sales have surged, yet product fit uncertainty remains a significant challenge, leading to rising product return rates. To address consumer concerns about return shipping costs, major Chinese online retail platforms have introduced return shipping insurance (RSI). Retailers can choose between Retailer-RSI (RRSI), which is provided by the retailer, and Customer-RSI (CRSI), which is purchased by consumers. Despite these options, information asymmetry causes insurers to assess return rates with bias—referred to as managerial confidence bias. Consequently, platforms are increasingly partnering with insurers to enhance their RSI offerings. This study develops a game-theoretical model to examine the dynamics between a platform and an insurer, as well as the impact of managerial confidence bias on RSI strategies. Our analysis reveals that the platform–insurer relationship is crucial in determining the optimal RSI strategy. Under an independent insurer, RSI is viable only if the insurer underestimates product return rates (i.e., exhibits overconfidence bias); RRSI is preferred if the bias is sufficiently strong, whereas CRSI is chosen otherwise. In contrast, under a dependent insurer, CRSI is favored by the retailer only when its return handling costs are substantially high; otherwise, RRSI is preferred. Furthermore, RSI consistently increases consumer surplus by reducing return hassle costs while only mildly raising the product price. However, the independent insurer’s bias leads to its own profit loss, resulting in a “loss–win–win–win” scenario across stakeholders. In contrast, the dependent insurer, supported by platform subsidies, can yield a “win–win–win–win” outcome that aligns stakeholder interests and enhances long-term platform benefits.