In this article, the reliability model and the opportunistic maintenance optimization model are formulated for the preset self-repairing mechanism which is artificially designed and applied to many engineering systems. The preset self-repairing mechanism is first introduced into the reliability model, and a series system consisting of two units is built to describe the proposed model. One unit in the system is subject to external shocks and has the preset self-repairing mechanism, the other does not have the recovery mechanism and its lifetime distribution follows exponential distribution. For the system, the analytical expression of reliability is derived, and a maintenance optimization model taking the long-run average cost per unit time as objective function is established. The decision parameters of the maintenance policy are preventive and opportunistic degradation levels. Besides, a preventive maintenance policy is proposed for comparison with the opportunistic maintenance policy. Finally, the numerical examples are provided to obtain the optimal decision parameters and demonstrate the effectiveness of opportunistic maintenance policies.

This paper studies a reliability modeling for a k-out-of-n: F load sharing system that operates in a shock environment. Such a system consists of a protective device and n components with load sharing. The base hazard rate of the loading sharing system is affected by random shocks and the protective device. Random shocks can be classified into two types: invalid shock and valid shock. An invalid shock has no influence on the system whereas a valid shock makes the base hazard rate larger. The system fails, if the number of failed components is at least k, the system suffers at least M random shocks or the protective device fails, whichever occurs first. A Markov process is used to evaluate system reliability in this paper. A distributed computer system is given to show application of the proposed model.

In the digital age, data-driven credit payment services play a significant role in constructing sustainable supply chains, which can stimulate consumption by reducing consumers’ cash pressure, thereby promoting a sustainable economic development. Our study investigates a dual-channel supply chain consisting of a supplier and a retailer, wherein the supplier ex-ante decides whether to implement the credit payment policy in the consumer market, and then the retailer determines whether to provide credit payment services in the reselling channel. We uncover that the supplier’s preference toward credit payment policy is not unidirectional. Specifically, the supplier establishes credit payment policy in the consumer market unless the discount of cash opportunity cost is lower than the price discount of credit payment services. Moreover, we find that, under credit payment policy of the supplier, the retailer opts to provide credit payment services when the discount of cash opportunity cost is higher than the price discount of credit payment services. Interestingly, our results demonstrate that, compared with non-credit payment policy, credit payment policy may restrict the development of economics sustainability, which undermines the whole supply chain.

With supply disruption occurring frequently nowadays, suppliers increasingly undertake costly effort to improve its own supply reliability. Meanwhile, some retailers are issued with blockchain technology, which reduces information asymmetry for suppliers, thereby mitigating supply disruptions. This study investigates how the demand information transparency via the retailer's adoption of blockchain affect the supplier's process reliability level. Although conventional wisdom suggests that the supplier will enjoy an information superiority if keeping the demand information private, we reveal that the retailer may opt to adopt the blockchain technology to achieve demand information transparency with the supplier, and such information transparency of blockchain-adoption system can incentivize the supplier to set an efficient supply reliability improvement level.