Weiguo Liu

In the context of the burgeoning digital economy, data factors as emerging core production factors exert an increasingly prominent impact on corporate innovation. Based on panel data from 4,011 listed companies spanning 2007-2023, this study employs the establishment of data trading platforms as a quasi-natural experiment and utilizes a difference-in-differences (DID) model to empirically examine the impact mechanism of data factor marketization on corporate innovation quality. The results indicate that data factor marketization increase company innovation quality. Mechanism analysis reveals that data factor marketization primarily promotes corporate innovation quality through two pathways: reducing agency costs and enhancing media attention. The establishment of data trading platforms significantly reduces firms’ administrative expense ratios while simultaneously increasing media attention, thereby driving improvements in innovation quality. Heterogeneity analysis shows that the promotion impact of data factor marketization on enterprise innovation quality shows regional differences, differences in the nature of property rights and differences in the quality of information disclosure. The research of this paper provides micro-level evidence for the innovation-driven effect of data factor marketization and provides policy implications for deepening the reform of factor marketization.

This paper investigates the single-machine due-window assignment scheduling with truncated learning effects and past-sequence-dependent setup times simultaneously. The scheduler has the option of job-rejection, i.e., some jobs are processed and the other jobs are rejected. Under three (i.e., common, slack and different) due-window assignment methods, our goal is to minimize the sum of a general earliness-tardiness cost (i.e., the weighted sum of earliness-tardiness, number of early and tardy jobs, starting time and size of due-window) and a total rejection cost. Some optimal properties of the problem are provided, and we demonstrate that the problem is polynomially solvable.

This paper aims to investigate the seru scheduling problem while considering the dual effects of worker cooperation and learning behavior to minimize the makespan and order processing time. Given the complexity of this research problem, an improved shuffled frog leaping algorithm based on a genetic algorithm is proposed. We design a double-layer encoding based on the problem, introduce a single point and uniform crossover operator, and select the crossover method in probability form to complete the evolution of the meme group. To avoid damaging grouping information, the individual encoding structure is transformed into unit form. Finally, numerical experiments were conducted using numerical examples of large and small sizes for verification. The experimental results demonstrate the feasibility of the proposed model and algorithm, as well as the necessity of considering worker dual behavior in the seru scheduling problem.

This article investigates the due-window assignment scheduling problem with setup times on a single machine, where setup times of jobs are past-sequence-dependent. Under common, slack and unrestricted due-window assignment methods, the goal is to determine the optimal job sequence and due-window such that the cost function (i.e., the weighted sum of earliness and tardiness, number of early and tardy jobs, due-window starting time and size) is minimized. We solve the problem optimally by introducing a polynomial time algorithm. An extension to the problem with learning and deterioration effects is also studied.

This article considers a single-machine group scheduling problem with due-window assignment, where the jobs are classified into groups and the jobs in the same group must be processed in succession. The goal is to minimize the weighted sum of lateness and due-window assignment cost, where the weights depend on the position in which a job is scheduled (i.e., position-dependent weights). For the common, slack, and different due-window assignment methods, we prove that the problem can be solved polynomially, i.e., in time, where is the number of jobs.