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This study aims to understand how abusive supervision influences employees’ unethical pro-organizational behavior (UPB). In particular, the mediating effect of moral disengagement and moderating role of traditionality on this relationship were examined.

This study conducted a two-wave questionnaire survey using data collected from 629 employees from different companies in China. Moderated mediation analysis was conducted to test the hypotheses.

The results show that moral disengagement mediates the relationship between abusive supervision and employees’ UPB. Employee traditionality enhances the relationship between abusive supervision and moral disengagement as well as the indirect effect of abusive supervision on employees’ UPB via moral disengagement.

First, by exploring the positive impact of abusive supervision on UPB, the authors enhance the current understanding of the role of negative leadership in the development of UPB and enrich the research on the antecedents of UPB and outcomes of abusive supervision. Second, based on social cognitive theory, this study enriches the literature on abusive supervision and employee UPB by identifying moral disengagement as a mediator. Third, to the best of the authors’ knowledge, the authors are among the first to incorporate traditionality to tell a Chinese story about how traditional employees respond to the effects of abusive supervision on UPB, providing a new lens for the cultural boundary condition in the occurrence mechanism of UPB.

Assembly lines are widely employed in manufacturing processes to produce final products in a flow efficiently. The simple assembly line balancing problem is a basic version of the general problem and has still attracted the attention of researchers. The type-I simple assembly line balancing problems (SALBP-I) aim to minimise the number of workstations on an assembly line by keeping the cycle time constant.

This paper focuses on solving multi-objective SALBP-I problems by utilising an artificial bee colony based-hyper heuristic (ABC-HH) algorithm. The algorithm optimises the efficiency and idleness percentage of the assembly line and concurrently minimises the number of workstations. The proposed ABC-HH algorithm is improved by adding new modifications to each phase of the artificial bee colony framework. Parameter control and calibration are also achieved using the irace method. The proposed model has undergone testing on benchmark problems, and the results obtained have been compared with state-of-the-art algorithms.

The experimental results of the computational study on the benchmark dataset unequivocally establish the superior performance of the ABC-HH algorithm across 61 problem instances, outperforming the state-of-the-art approach.

This research proposes the ABC-HH algorithm with local search to solve the SALBP-I problems more efficiently.