Material requirement planning is a type of production planning problems that is used to plan about a final product, its sub-assemblies, and its raw parts simultaneously by considering time phased demands of the final product. In this study a multi-product material requirement planning problem with limited manufacturing resources is considered. As an important novelty, a multi-mode demand strategy is considered in this problem where the total customers’ satisfaction degrees of the selected demand modes is maximized. Furthermore, three types of capacities such as regular, over time, and outsourcing capacities are considered for such system as another novelty. The problem is formulated as a bi-objective model to maximize total profit and total satisfaction degree of the customers simultaneously. To respect the uncertain nature of the problem, it is formulated in a belief-degree based uncertain form. This is for the first time in the literature of material requirement planning that this type of uncertainty is considered. The uncertain problem is converted to a crisp form using some techniques such as expected value model and chance constrained model. Then, a new hybrid form of the fuzzy programming approach is developed to solve the bi-objective crisp formulations. A case study from the petroleum industries of Iran is used to perform the required computational experiments. The required experiments are done, and possible comparisons are made on the obtained results. Furthermore, some managerial insights are given in order to be used in the production system of the case study. According to the obtained results, the proposed hybrid fuzzy programming approach is superior to existing approaches in at least 38 percent of the experiments.

As an innovative approach, Direct Acyclic Graph (DAG)-based blockchain is designed to overcome the scalability and performance limitations of traditional blockchain systems, which rely on sequential structures. The graph-based architecture of DAG allows for faster transactions and parallel processing, making it a compelling option across various industries. To enhance the analytical understanding of DAG-based blockchains, this paper begins by introducing a Markov model tailored for a DAG-based blockchain system, specifically focusing on the Tangle structure and the interaction between tips and newly arrived transactions. We then establish a continuous-time Markov process to analyze the DAG-based blockchain, demonstrating that this process is a level-dependent quasi-birth-and-death (QBD) process. We further prove that the QBD process is both irreducible and positively recurrent. Building on this foundation, we conduct a performance analysis of the DAG-based blockchain system by deriving the stationary probability vector of the QBD process. Notably, we introduce a novel method to calculate the average sojourn time of any arriving internal tip within the system using first passage times and Phase-type (PH) distributions. Finally, numerical examples are provided to validate our theoretical findings and to illustrate the influence of system parameters on the performance metrics.

An erratum to this article is available online at https://doi.org/10.1007/s11518-025-5676-6.

An erratum to this article is available online at https://doi.org/10.1007/s11518-025-5676-6.

The inpatient bed allocation that allows beds shared among different departments is an important and challenging problem for a healthcare system. When the objective function(s) and (some) constraints need to be estimated via expensive and noisy stochastic simulation, a simulation optimization algorithm is required to solve this problem. In literature, there is a heuristic algorithm highly customized for one specific inpatient bed allocation problem, and it performs quite well on that problem. However, its lack of theoretical convergence and high specialization may not give practitioners enough confidence to apply it on real inpatient bed allocation problems. To mitigate such issues, this paper proposes to use the empirical stochastic branch-and-bound (ESB&B) algorithm, which is theoretically convergent and suitable for relatively general problems. A modest improvement for the original ESB&B algorithm is made and how to adapt the ESB&B algorithm to inpatient bed allocation problems is presented. Numerical experiments reveal the generality and fairly satisfying performance of the ESB&B algorithm, and the superiority of the improved ESB&B algorithm over the original one.

Financial flexibility, due to its capability in optimizing resource allocation, enhancing operational efficiency, and responding to market changes, has become a crucial strategy for “specialized, refined, differentiated, and innovative” (SRDI) enterprises to address financing difficulties and cash flow management pressures, thereby achieving sustainable development. This study, based on financial flexibility theory, conducts an empirical analysis using data from China’s listed SRDI enterprises from 2016 to 2023. The results show that there is an inverted U-shaped relationship between financial flexibility and corporate sustainable development performance. Heterogeneity analysis indicates that the impact of financial flexibility on sustainable development performance varies significantly under different ownership structures and dimensions. This study not only enriches the theory of financial flexibility but also reveals its complex impact mechanisms within SRDI enterprises, providing practical guidance and data support for enterprises to achieve sustainable development in a complex market environment.

This study delves into the dynamics of a supply chain scenario wherein a capital-abundant retailer extends buyer financing to a capital-constrained supplier. The supplier procures components from the spot market and subsequently procures the final product, subject to a certain probability of disruption. We introduce a Geometric Brownian Motion (GBM) model to capture the variability of component costs. We characterize the equilibrium results under different financing schemes, including buyer financing and bank financing. The retailer’s equilibrium order time under buyer financing is earlier than that under bank financing iff the probability of disruption is relatively small. When the trend of the component cost is downward, the retailer will place the order at the end, and buyer financing is advantageous for the retailer but detrimental for the supplier. If the component cost shows a slight upward trend with minor fluctuations and settles into a moderate range, immediate buyer financing and transaction execution can yield a win-win outcome for the retailer and supplier with high delivery probability. We also numerically compare our optimal policy with other naive policies, and extend our model to an an Ornstein-Uhlenbeck (OU) process.