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The assembly of large and complex products such as cars, trucks, and white goods typically involves a huge amount of production resources such as workers, pieces of equipment, and layout areas. In this context, multi-manned workstations commonly characterize these assembly lines. The simultaneous operators’ activity in the same assembly station suggests considering compatibility/incompatibility between the different mounting positions, equipment sharing, and worker cooperation. The management of all these aspects significantly increases the balancing problem complexity due to the determination of the start/end times of each task. This paper proposes a new mixed-integer programming model to simultaneously optimize the line efficiency, the line length, and the workload smoothness. A customized procedure based on a simulated annealing algorithm is developed to effectively solve this problem. The aforementioned procedure is applied to the balancing of the real assembly line of European sports car manufacturers distinguished by 665 tasks and numerous synchronization constraints. The experimental results present remarkable performances obtained by the proposed procedure both in terms of solution quality and computation time. The proposed approach is the practical reference for efficient multi-manned assembly line design, task assignment, equipment allocation, and mounting position management in the considered industrial fields.

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.

Cet article s’intéresse à la mise en place d’une gouvernance démocratique suite au rachat d’une entreprise par ses salariés sous la forme d’une société coopérative et participative. Les auteurs montrent que trois catégories de travail institutionnel sont nécessaires pour réussir cette transition : 1) transformer l’identité des salariés afin qu’ils se projettent en tant que sociétaires et propriétaires, 2) maintenir la légitimité de l’entreprise dans son champ organisationnel et 3) développer de nouvelles pratiques et outils de gestion démocratiques. Ils contribuent par cette recherche à enrichir la compréhension des formes démocratiques de gouvernance des entreprises. This research deals with the implementation of democratic governance following the buy-out of a company by its employees. We use the institutional work theoretical framework to observe this process. We show through a longitudinal case study that three categories of institutional work are performed by organizational members. The first one consists in transforming the identity of employees to set aside private forms of governance and invest in return democratic and participative forms of governance. The second category has an objective of maintenance of the organization's legitimacy in its organizational field. The last form is dedicated to the development of new practices and managerial democratic tools. We enrich through this research our understanding of democratic forms of governance.

Background Interdisciplinary collaboration is critical for improving healthcare delivery through coordinated care and streamlined healthcare navigation. Community health workers (CHWs) and social workers (SWs) are uniquely positioned to address the needs of individuals with complex social and health challenges. Despite the integration of CHWs and SWs into health and community settings, there is a paucity of literature on what facilitates successful collaboration between these two workforces. This qualitative study, conducted from April 2022 to June 2023, explores multilevel factors related to CHW-SW collaboration in health and community settings. Methods We conducted eight, 90-min virtual focus groups with CHWs ( n  = 20) and SWs ( n  = 17) collaborating in four healthcare and community health settings across the United States (California, Texas, New Jersey, and South Carolina). Focus groups were conducted between April 2022 and June 2023. Results Themes were thematically organized according to the socio-ecological model. Individual and relationship-level factors included: roles and scopes of practice, communication, mutual respect, supportive supervision, and power dynamics. Organizational and community-level factors comprised: commitment to equity, leadership buy-in, standardized training, clear workflows, and shared documentation and physical space. Societal-level factors included: power dynamics, supportive policies and sustainable funding. Conclusions Findings highlighted that CHW-SW collaboration can promote patient-centered care and address social determinants of health when both workforces are well integrated in healthcare systems. Key organizational commitments, community rapport, and relational dynamics should be established to optimize interdisciplinary collaboration and advance health equity.

To estimate the level and trend of development assistance for community health worker-related projects in low- and middle-income countries between 2007 and 2017. We extracted data from the Organisation for Economic Co-operation and Development's creditor reporting system on aid funding for projects to support community health workers (CHWs) in 114 countries over 2007-2017. We produced estimates for projects specifically described by relevant keywords and for projects which could include components on CHWs. We analysed the pattern of development assistance by purpose, donors, recipient regions and countries, and trends over time. Between 2007 and 2017, total development assistance targeting CHW projects was around United States dollars (US$) 5 298.02 million, accounting for 2.5% of the US$ 209 277.99 million total development assistance for health. The top three donors (Global Fund to Fight AIDS, Tuberculosis and Malaria, the government of Canada and the government of the United States of America) provided a total of US$ 4 350.08 million (82.1%) of development assistance for these projects. Sub-Saharan Africa received a total US$ 3 717.93 million, the largest per capita assistance over 11 years (US$ 0.39; total population: 9 426.25 million). Development assistance to projects that focused on infectious diseases and child and maternal health received most funds during the study period. The share of development assistance invested in the CHW projects was small, unstable and decreasing in recent years. More research is needed on tracking government investments in CHW-related projects and assessing the impact of investments on programme effectiveness.

The UAW's Southern Gamble is the first in-depth assessment of the United Auto Workers' efforts to organize foreign vehicle plants (Daimler-Chrysler, Mercedes-Benz, Nissan, and Volkswagen) in the American South since 1989, an era when union membership declined precipitously. Stephen J. Silvia chronicles transnational union cooperation between the UAW and its counterparts in Brazil, France, Germany, and Japan and documents the development of employer strategies that have proven increasingly effective at thwarting unionization. Silvia shows that when organizing, unions must now fight on three fronts: at the worksite; in the corporate boardroom; and in the political realm. The UAW's Southern Gamble makes clear that the UAW's failed campaigns in the South can teach hard-won lessons about challenging the structural and legal roadblocks to union participation and effectively organizing workers within and beyond the auto industry.

The escalating global scarcity of skilled health care professionals is a critical concern, further exacerbated by rising stress levels and clinician burnout rates. Artificial intelligence (AI) has surfaced as a potential resource to alleviate these challenges. Nevertheless, it is not taken for granted that AI will inevitably augment human performance, as ill-designed systems may inadvertently impose new burdens on health care workers, and implementation may be challenging. An in-depth understanding of how AI can effectively enhance rather than impair work conditions is therefore needed. This research investigates the efficacy of AI in alleviating stress and enriching work conditions, using intensive care units (ICUs) as a case study. Through a sociotechnical system lens, we delineate how AI systems, tasks, and responsibilities of ICU nurses and physicians can be co-designed to foster motivating, resilient, and health-promoting work. We use the sociotechnical system framework COMPASS (Complementary Analysis of Sociotechnical Systems) to assess 5 job characteristics: autonomy, skill diversity, flexibility, problem-solving opportunities, and task variety. The qualitative analysis is underpinned by extensive workplace observation in 6 ICUs (approximately 559 nurses and physicians), structured interviews with work unit leaders (n=12), and a comparative analysis of data science experts' and clinicians' evaluation of the optimal levels of human-AI teaming. The results indicate that AI holds the potential to positively impact work conditions for ICU nurses and physicians in four key areas. First, autonomy is vital for stress reduction, motivation, and performance improvement. AI systems that ensure transparency, predictability, and human control can reinforce or amplify autonomy. Second, AI can encourage skill diversity and competence development, thus empowering clinicians to broaden their skills, increase the polyvalence of tasks across professional boundaries, and improve interprofessional cooperation. However, careful consideration is required to avoid the deskilling of experienced professionals. Third, AI automation can expand flexibility by relieving clinicians from administrative duties, thereby concentrating their efforts on patient care. Remote monitoring and improved scheduling can help integrate work with other life domains. Fourth, while AI may reduce problem-solving opportunities in certain areas, it can open new pathways, particularly for nurses. Finally, task identity and variety are essential job characteristics for intrinsic motivation and worker engagement but could be compromised depending on how AI tools are designed and implemented. This study demonstrates AI's capacity to mitigate stress and improve work conditions for ICU nurses and physicians, thereby contributing to resolving health care staffing shortages. AI solutions that are thoughtfully designed in line with the principles for good work design can enhance intrinsic motivation, learning, and worker well-being, thus providing strategic value for hospital management, policy makers, and health care professionals alike.