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This study aims to solve the problem of job scheduling and multi automated guided vehicle (AGV) cooperation in intelligent manufacturing workshops.

In this study, an algorithm for job scheduling and cooperative work of multiple AGVs is designed. In the first part, with the goal of minimizing the total processing time and the total power consumption, the niche multi-objective evolutionary algorithm is used to determine the processing task arrangement on different machines. In the second part, AGV is called to transport workpieces, and an improved ant colony algorithm is used to generate the initial path of AGV. In the third part, to avoid path conflicts between running AGVs, the authors propose a simple priority-based waiting strategy to avoid collisions.

The experiment shows that the solution can effectively deal with job scheduling and multiple AGV operation problems in the workshop.

In this paper, a collaborative work algorithm is proposed, which combines the job scheduling and AGV running problem to make the research results adapt to the real job environment in the workshop.

This chapter critically examines whether it may be possible to create an AI-based authentic leader, questioning the inherent contradiction between artificial and authentic. The authors pose central research questions: Does the application of AI – even just as a powerful resource – challenge the tenets of authentic leadership? What are the possibilities and limitations of the concept of authenticity in AI-based management systems? Moreover, with the help of three vignettes illustrating practical applications of AI-based systems in leadership and management tasks, the authors illustrate how technology may be used to either control or empower workers and leaders. The authors call for research to assess whether the search for authenticity in AI-based leadership could lead anywhere, warning that it could entrap us in unresolvable existential and conceptual ambiguity, ultimately diverting our focus from the essence of leadership altogether.

The purpose of this study is to formulate a new class of vehicle routing problem with an objective to minimise the total cost of raw material collection and derive a new approach to solve optimization problems. This study can help to select the optimum number of suppliers based on cost.

To model the raw material vehicle routing problem, a mixed integer linear programming (MILP) problem is formulated. An interesting phenomenon added to the proposed problem is that there is no compulsion to visit all suppliers. To guarantee the demand of semiconductor industry, all visited suppliers should reach a given raw material capacity requirement. To solve the proposed model, the authors developed a novel hybrid approach that is a combination of block and edge recombination approaches. To avoid bias, the authors compare the results of the proposed methodology with other known approaches, such as genetic algorithms (GAs) and ant colony optimisation (ACO).

The findings indicate that the proposed model can be useful in industries, where multiple suppliers are used. The proposed hybrid approach provides a better sequence of suppliers compared to other heuristic techniques.

The data used in the proposed model is generated based on previous literature. The problem derives from the assumption that semiconductor industries use a variety of raw materials.

This study provides a new model and approach that can help practitioners and policymakers select suppliers based on their logistics costs.

This study provides two important contributions in the context of the supply chain. First, it provides a new variant of the vehicle routing problem in consideration of raw material collection; and second, it provides a new approach to solving optimisation problems.

Non-verbal communication (NVC) remains largely understudied despite its importance in today's fast-paced and cross-cultural management and research landscape. This article is significant because it reveals valuable insights into NVC, which represents 65–93% (Mehrabian, 1981) of communication and has the potential to considerably increase management effectiveness and efficiency by providing leaders and researchers with the knowledge they need to understand and handle diversity with competence.

This article draws on social identity theory (SIT) (Tajfel and Turner, 1979) and rapport management theory (RMT) (Brown and Levinson, 1987) to analyse illustrative interview extracts of co-occurring verbal and NVC from an interpretative phenomenological analysis (IPA) study focussed on understanding how London-based Romanian migrant entrepreneurs experience acculturation.

Romanian migrant entrepreneurs use a variety of verbal and non-verbal communication approaches in their acculturation narratives, providing depth and occasionally shifting meaning. These tactics include repeating verbal discourse with non-verbal clues, replacing verbal communication with non-verbal gestures, complementing verbal communication and juxtaposing non-verbal cues with verbal descriptions.

This study makes a valuable contribution to the fields of qualitative organisational management and entrepreneurial studies by addressing the lack of methodological tools available for analysing non-verbal language in interpretative research. This study presents a systematic technique for assessing non-verbal language symbols that has been developed through face-to-face interviews. The article utilises the first-hand interview experience of a Romanian co-researcher to demonstrate the significance of NVC in the transmission of meaning and the formation of identities amongst Romanian migrant entrepreneurs. These findings contribute to a better understanding of organisational management and research practices, particularly about this understudied entrepreneurial minority of Romanian businesses in London, by helping researchers and managers better grasp the cultural and contextual meanings communicated non-verbally. The article holds significance in the context of cross-cultural and organisational management practices.

This study aims to investigate the spillover–crossover effects on the work–family interface, with an emphasis on work–family conflict (WFC) and family–work conflict (FWC) on marital satisfaction and personal burnout.

Data were collected from matched dyads by 300 teachers and their spouses (N = 600) in Indonesia. Multiple regression analysis served to test the hypotheses.

The results showed significant spillover–crossover effects of WFC on personal burnout amongst working wives to their spouses, but not working husbands to their spouses. Moreover, there was a spillover effect of FWC on personal burnout amongst working wives, and a crossover effect of FWC on spouses' personal burnout amongst working husbands to their spouses. However, there was no spillover–crossover effect of WFC and FWC on marital satisfaction for both working wives and husbands.

The findings have implications for occupational stress management. Decision-makers have to create a positive atmosphere that reduces WFC in the workplace by providing support to the workers.

This study contributes to the literature by exploring the crossover effects of WFC and FWC amongst marital spouses in a country with inherent conservatism and traditional gender role perspectives.

Considering the potential economic losses this might bring about, researchers have begun to explore ways to mitigate procrastination. Drawing on the job demands-resources model and the spillover-crossover model, this study aims to investigate the association between harmonious passion and procrastination at the intra- and interpersonal levels.

This study used a two-wave multisource questionnaire survey to acquire 256 cases nested in 128 coworker dyads from two hotels in Shanghai. Multilevel analysis and the actor–partner interdependence model were adopted to examine the hypotheses.

The results indicate that employees’ harmonious passion is indirectly negatively associated with procrastination (i.e., cyberslacking and soldiering) through workplace well-being at the intrapersonal level; employees’ harmonious passion is indirectly negatively associated with their coworkers’ procrastination (i.e., cyberslacking and soldiering) through the coworkers’ workplace well-being; and the crossover influence of employees’ harmonious passion on coworkers’ workplace well-being is contingent upon interpersonal conflict at the dyadic level, such that the crossover influence is stronger in condition of low rather than high interpersonal conflict.

Hotels are suggested to provide training programs to employees for enhancing their capabilities to maintain harmonious passion and promote their communication skills to decrease the likelihood to experience interpersonal conflict.

This study offers a comprehensive insight into the association between harmonious passion and procrastination in hospitality employees, which extends the understanding of the outcomes of harmonious passion and the profit of harmonious passion at the interpersonal level.

Community detection of dynamic networks provides more effective information than static network community detection in the real world. The mainstream method for community detection in dynamic networks is evolutionary clustering, which uses temporal smoothness of community structures to connect snapshots of networks in adjacent time intervals. However, the error accumulation issues limit the effectiveness of evolutionary clustering. While the multi-objective evolutionary approach can solve the issue of fixed settings of the two objective function weight parameters in the evolutionary clustering framework, the traditional multi-objective evolutionary approach lacks self-adaptability.

This paper proposes a community detection algorithm that integrates evolutionary clustering and decomposition-based multi-objective optimization methods. In this approach, a benchmark correction procedure is added to the evolutionary clustering framework to prevent the division results from drifting.

Experimental results demonstrate the superior accuracy of this method compared to similar algorithms in both real and synthetic dynamic datasets.

To enhance the clustering results, adaptive variances and crossover probabilities are designed based on the relative change amounts of the subproblems decomposed by MOEA/D (A Multiobjective Optimization Evolutionary Algorithm based on Decomposition) to dynamically adjust the focus of different evolutionary stages.

Innovative behavior is a microfoundation of an organization’s innovation. Knowledge workers are the main creators of innovations. With the boundaries between work and family becoming increasingly ambiguous, the purpose of this study is to explore how the work–family conflict affects knowledge workers’ innovative behavior and when such a conflict arises.

To test the theoretical model, this study collected data from a time-lagged matched sample of 214 dual-career couples. The data were analyzed with the bias-corrected bootstrapping method.

The results of this study showed that work-to-family conflict had not only a direct negative effect on knowledge workers’ innovative behavior but also an indirect effect through spouses’ within-family emotional exhaustion and knowledge workers’ family-to-work conflict. If wives’ gender role perceptions are traditional, then the indirect serial mediating effect is weakened, but if such perceptions are egalitarian, then the mentioned effect is aggravated.

In terms of organizational implications, managers could alter their approach by reducing detrimental factors such as work–family conflict to improve knowledge workers’ innovative behavior. Emotional assistance programs for both knowledge workers and their spouses can be used to prevent the detrimental effect of work–family conflict on innovative behavior. As to social implications, placing dual-career couples into a community of likeminded individuals and promoting their agreement on gender role identity will greatly reduce the negative effects of work–family conflict.

Starting from the perspective of the behavior outcome of knowledge management, this study advances the existing knowledge management literature by enriching the antecedents of knowledge workers’ innovative behavior, illuminating a spillover–crossover–spillover effect of work–family conflict on knowledge workers’ innovative behavior and identifying the boundary condition of this transmission process.

Two-level support with Level 1 consisting of a set of beams and Level 2 consisting of a tree-like structure is an efficient support structure for extrusion-based additive manufacturing (EBAM). However, the literature for finding a slim two-level support is rare. The purpose of this paper is to design a lightweight two-level support structure for EBAM.

To efficiently solve the problem, the lightweight design problem is split into two subproblems: finding a slim Level 1 support and a slim Level 2 support. To solve these two subproblems, this paper develops three efficient metaheuristic algorithms, i.e. genetic algorithm (GA), genetic programming (GP) and particle swarm optimization (PSO). They are problem-independent and are powerful in global search. For the first subproblem, considering the path direction is a critical factor influencing the layout of Level 1 support, this paper solves it by splitting the overhang region into a set of subregions, and determining the path direction (vertical or horizontal) in each subregion using GA. For the second subproblem, a hybrid of two metaheuristic algorithms is proposed: the GP manipulates the topologies of the tree support, while the PSO optimizes the position of nodes and the diameter of tree branches. In particular, each chromosome is encoded as a single virtual tree for GP to make it easy to manipulate Crossover and Mutation. Furthermore, a local strategy of geometric search is designed to help the hybrid algorithm reach a better result.

Simulation results show that the proposed method is preferred over the existing method: it saves the materials of the two-level support up to 26.34%, the materials of the Level 1 support up to 6.62% and the materials of the Level 2 support up to 37.93%. The proposed local strategy of geometric search can further improve the hybrid algorithm, saving up to 17.88% of Level 2 support materials.

The proposed approach for sliming Level 1 support requires the overhanging region to be a rectilinear polygon and the path direction in a subregion to be vertical or horizontal. This limitation limits the further material savings of the Level 1 support. In future research, the proposed approach can be extended to handle an arbitrary overhang region, each with several choices of path directions.

The details of how to integrate the proposed algorithm into the open-source program CuraEngine 4.13.0 is presented. This is helpful for the designers and manufacturers to practice on their own 3D printers.

The path planning of the overhang is a critical factor influencing the distribution of supporting points and will thus influence the shape of the support structure. Different from existing approaches that use single path directions, the proposed method optimizes the volume of the support structure by planning hybrid paths of the overhangs.

This paper aims to offer a hybrid genetic algorithm and the ant colony optimization (GA-ACO) algorithm for task mapping and resource management. The paper aims to reduce the makespan and total response time in fog computing- medical cyber-physical system (FC-MCPS).

Swift progress in today’s medical technologies has resulted in a new kind of health-care tool and therapy techniques like the MCPS. The MCPS is a smart and reliable mechanism of entrenched clinical equipment applied to check and manage the patients’ physiological condition. However, the extensive-delay connections among cloud data centers and medical devices are so problematic. FC has been introduced to handle these problems. It includes a group of near-user edge tools named fog points that are collaborating until executing the processing tasks, such as running applications, reducing the utilization of a momentous bulk of data and distributing the messages. Task mapping is a challenging problem for managing fog-based MCPS. As mapping is an non-deterministic pol ynomial-time-hard optimization issue, this paper has proposed a procedure depending on the hybrid GA-ACO to solve this problem in FC-MCPS. ACO and GA, that is applied in their standard formulation and combined as hybrid meta-heuristics to solve the problem. As such ACO-GA is a hybrid meta-heuristic using ACO as the main approach and GA as the local search. GA-ACO is a memetic algorithm using GA as the main approach and ACO as local search.

MATLAB is used to simulate the proposed method and compare it to the ACO and MACO algorithms. The experimental results have validated the improvement in makespan, which makes the method a suitable one for use in medical and real-time systems.

The proposed method can achieve task mapping in FC-MCPS by attaining high efficiency, which is very significant in practice.

The proposed approach can achieve the goal of task scheduling in FC-MCPS by attaining the highest total computational efficiency, which is very significant in practice.

This research proposes a GA-ACO algorithm to solve the task mapping in FC-MCPS. It is the most significant originality of the paper.