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PurposeThis study aims to extend the previous research on the structural relationships between organisational empowerment and frontline employees’ behaviours by exploring the role of the self-regulating processes and its impact on service recovery performance (SRP).MethodsThis study adopts fuzzy-set qualitative comparative analysis (fsQCA). Following the procedure of applying fsQCA, including data calibration, truth table construction and fsQCA analysis, the 287 dyadic data from the express mail industry was collected and analysed.ResultsThe findings show that organisational empowerment is a sufficient antecedent for high SRP, especially in cases involving frontline employees with strong service recovery awareness and positive work engagement. Moreover, in the context of organisational empowerment, a reasonable level of emotional exhaustion represents a positive impact on performance in service recovery.ConclusionThis study offers some comprehensive insights for practitioners to empower stressed frontline employees and monitor their emotions and behaviours using appropriate approaches.

There are many constrained optimization problems in engineering. Bio-inspired optimization algorithms have been widely used to solve various engineering problems. This paper presents a novel optimization algorithm called Lifecycle-based Swarm Optimization, inspired by biology life cycle. LSO algorithm imitates biologic life cycle process through six optimization operators: chemotactic, assimilation, transposition, crossover, selection and mutation. In addition, the spatial distribution of initialization population meets clumped distribution. Experiments were conducted on a Vehicle Routing Problem with Time Windows for demonstration the effectiveness and stability. The results demonstrate remarkable performance of the LSO algorithm on chosen case when compared to two successful optimization techniques.

In view of the superiority of the Lifecycle-based Swarm Optimization algorithm (LSO) in benchmark functions, this paper will further study on the optimizing performance of the LSO algorithm in multi-objective optimization problem. Based on the LSO algorithm, this paper designs the LSO algorithm based on non-dominated sorting (NLSO) which has easy and lesser parameters. The NLSO algorithm divides initialization population into dominating set and non-dominated set, also adjusts dynamically the non-dominated set in the iteration, to accomplish the searching and the approximation of the Pareto optimal set. The experiments demonstrate not only effectiveness and rapidity of the NLSO algorithm, but also the NLSO algorithm outperforms other congeneric algorithms by the calculation of performance index Generational Distance (GD) and Spacing (SP).