Recovery of the end-of-use products has become a topic of considerable interest in the advanced manufacturing industry due in part to uncertainties in the quality and volume of product returns. The Internet of Things (IoT) that enables the tracing, detecting, storing, and analyzing the product life cycle data for each individual item can mitigate or eliminate these uncertainties. In this paper, an integrated three-stage model is presented based on IoT technology for the optimization of procurement, production and product recovery, pricing and strategy of return acquisition. The remaining value is used to measure the return condition. The model considers three recovery options related to refurbishing, component reuse and disposal, and the value deterioration for satisfying the product demand in each stage of product life cycle (PLC). A novel particle swarm optimization (PSO) algorithm based on two heuristic methods is proposed to solve the problem. A numerical example and sensitivity analysis are used to illustrate the performance of both algorithm and applicability of the model.

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The application of Internet of Things promotes the cooperation among firms, and it also introduces some information security issues. Due to the vulnerability of the communication network, firms need to invest in information security technologies to protect their confidential information. In this paper, considering the multiple-step propagation of a security breach in a fully connected network, an information security investment game among n firms is investigated. We make meticulous theoretic and experimental analyses on both the Nash equilibrium solution and the optimal solution. The results show that a larger network size (n) or a larger one-step propagation probability (q) has a negative effect on the Nash equilibrium investment. The optimal investment does not necessarily increase in n or q, and its variation trend depends on the concrete conditions. A compensation mechanism is proposed to encourage firms to coordinate their strategies and invest a higher amount equal to the optimal investment when they make decisions individually. At last, our model is extended by considering another direct breach probability function and another network structure, respectively. We find that a higher connection density of the network will result in a greater expected cost for each firm.

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In a multiple attribute decision making (MADM) problem, quantitative and qualitative attributes can be assessed by numerical values and subjective judgements. Numerical value can be accurate or uncertain, while qualitative attribute could be evaluated by linguistic variables. The evidential reasoning (ER) approach provides a process for dealing with MADM problems of both a quantitative and qualitative nature under uncertainty. In the existing ER approach, however, only benefit and cost attributes are considered in the evidence combination process. In this paper, deviated and fixed attributes are introduced into MADM and the frames of discernment for representing these two kinds of attributes are proposed. The transformation rules from the assessment values of deviated and fixed attributes to belief degrees in the ER structure are then studied. In the existing MADM literature, interval value is assumed to be uniformly distributed and complete in the sense that any value in the interval is equally likely and that the probabilities of values in the interval being taken sum to one. In real life decision situations, however, interval value could be incomplete in that the sum of probabilities of values in the interval being taken can be less than one. In this paper, incomplete interval value is introduced to a decision making process, and the transformation rule of incomplete interval value to belief degrees in the frame of discernment is analyzed. The characteristics of the transformation rule are studied. Two case studies are provided to illustrate the implementation of the proposed new concept and technique and the potential in supporting MADM under uncertainty.

The sharing economy has developed rapidly in recent years using both business‐to‐customer (B2C) and customer‐to‐customer (C2C) models. This has exerted a profound impact on incumbent firms that follow a traditional sales model. Although the effects of B2C or C2C sharing in certain scenarios have been studied by prior literature, the effect of external B2C sharing has not been considered. Furthermore, the possible distinction between the two sharing effects as well as incumbent firms’ decisions on the sales and sharing models under the internal and external environments have not been addressed. This study compares the effects of B2C and C2C sharing in an internal sharing scenario where an incumbent firm can extend into the sharing business. Due to the difference in sharing agents, we also consider an external sharing scenario where an independent entrant firm can provide B2C or C2C sharing and strategically set price. From the perspectives of product cost and sharing transaction cost, we present several new managerial insights to expand on existing literature. First, under the internal sharing scenario, interestingly, the incumbent firm benefits from extending into the B2C or C2C sharing business only when the product cost is above a threshold, and it prefers to extend into the C2C sharing business unless the per‐period transaction cost of C2C sharing is much higher than that of B2C sharing. In addition, it is shown that the incumbent firm may need to produce more products for sales, or maintain lower sharing supply when it extends into the B2C sharing business than those when it extends C2C sharing, which is somewhat counter‐intuitive. Under the external sharing scenario, we observe that the B2C sharing business benefits the incumbent firm merely when the product cost is high, similar to the impact of C2C sharing. Meanwhile, if the per‐period transaction cost of C2C sharing is much higher than that of B2C sharing, then the positive impact of external B2C sharing on the incumbent firm’s profitability should be stronger than that of external C2C sharing. Moreover, external B2C sharing actually increases the sales demand of the incumbent firm under the conditions of low product cost and high sharing transaction cost, while external C2C sharing might increase it as well in the condition of high product cost. Both external B2C and C2C sharing may lead to a higher rental price than internal sharing in the presence of high product cost. Furthermore, even though the product cost is low, there should be higher customer surplus and total social welfare in the external B2C or C2C sharing scenarios. For constructing a comprehensive framework of sharing scenarios, we also extend our model to a setting where the incumbent firm could extend both B2C and C2C sharing in the internal and external environments. It is shown that the incumbent firm always extends into the sharing business to compete against the entrant firm in the external sharing scenario.

Understanding hot carrier dynamics between plasmonic nanomaterials and its adsorbate is of great importance for plasmon‐enhanced photoelectronic processes such as photocatalysis, optical sensing and spectroscopic analysis. However, it is often challenging to identify specific dominant mechanisms for a given process because of the complex pathways and ultrafast interactive dynamics of the photoelectrons. Here, using CO2 reduction as an example, the underlying mechanisms of plasmon‐driven catalysis at the single‐molecule level using time‐dependent density functional theory calculations is clearly probed. The CO2 molecule adsorbed on two typical nanoclusters, Ag20 and Ag147, is photoreduced by optically excited plasmon, accompanied by the excitation of asymmetric stretching and bending modes of CO2. A nonlinear relationship has been identified between laser intensity and reaction rate, demonstrating a synergic interplay and transition from indirect hot‐electron transfer to direct charge transfer, enacted by strong localized surface plasmons. These findings offer new insights for CO2 photoreduction and for the design of effective pathways toward highly efficient plasmon‐mediated photocatalysis.