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Workload contribution calculation approaches in the existing literature overestimate or underestimate indirect workload, which increases both workload fluctuation and shop floor throughput performance. This study optimizes a Corrected Aggregate Workload (CAW) approach to control the workload contribution of workstations and Work In Progress (WIP) levels, thereby improving the shop floor throughput performance.

This study adopts simulation experiment by SimPy, and experimental factors are: (1) two workload contribution methods (CAW method and considering Position Corrected Aggregate Workload [PCAW] method); (2) two release methods (LUMS COR release and immediate release); (3) eleven workload norms for LUMS COR release (from 7- to 15-time units), and infinite workload norm for immediate release; and (4) two dispatching rules (First Come First Served, FCFS and Operation Due Date, ODD). Each scenario is replicated 100 times, and for each replication data are collected for 10,000 time units, being the warm-up period set to 3,000-time units.

The results of this study confirm that the PCAW calculation method outperforms the CAW method, especially during higher workload norm levels. The PCAW method is considered the better solution in practice due to its excellent performance in terms of percentage tardiness and mean tardiness time. The efficient workload contribution approach, as discussed in this study, has the potential to offset delivery performance loss that results from throughput performance loss.

This study proposes a novel approach that considers the workstations’ position in the routing of the job and the position of jobs CAW method. The results demonstrated that it allows shop floor throughput time to be short and feasible. It controls WIP by workload contribution of workstations, resulting in a lean shop floor. Therefore, workload contribution calculation is of particular significance for high-variety Make-To-Order (MTO) companies.

With the development trend of China’s service-oriented manufacturing moving toward intelligence and personalization, the deep integration of manufacturing and service has become a synergistic challenge for enterprises.

An improved migratory bird optimization (IMBO) algorithm is proposed to solve the multiobjective FJSP model. First, this paper designs an integer encoding method based on job-machine. The algorithm adopts the greedy decoding method to obtain the optimal scheduling solution. Second, this paper combines three initialization rules to enhance the quality of the initial population. Third, three neighborhood search strategies are combined to improve the search capability and convergence of the solution space. Furthermore, the IMBO algorithm introduces the concepts of nondominated ranking and crowding degree to update the population better. Finally, the optimal solution is obtained after multiple iterations.

Through the simulation of 15 benchmark studies and a production example of a furniture enterprise, the IMBO algorithm is compared with three other algorithms: the improved particle swarm optimization algorithm, the global and local search with reinitialization-based genetic algorithm and the hybrid grey wolf optimization algorithm. The experiment results show the effectiveness of the IMBO algorithm in solving the multiobjective FJSP.

The study does not consider the influence of disturbance factors, such as emergency interventions and equipment failures, on scheduling in actual production processing. It is necessary to further study the dynamic FJSP problem.

The study proposes an IMBO algorithm to solve the multiobjective FJSP problem. It also uses three initialization rules to broaden the range of the solution space. The study applies multiple crossover strategies to avoid the algorithm falling into local optimality.

Riverston, Sri Lanka, is famous for its natural and cultural significance. The tourism industry is emerging there and expanding with more community engagement. Riverston being a community-based tourism site, this study aimed at discovering the perception of the Riverston community on tourism development further focusing on the market opportunities, challenges, and strategies for the way forward. Data were collected from the Riverston area community who engage in tourism through in-depth interviews with semi-structured questions, and the sample was selected based on the purposive sampling technique. Data were analyzed through content analysis. The findings disclosed that challenging the authentic culture, traditional livelihood shifting toward tourism, lack of awareness and knowledge of the tourism industry among the community, the impact of legal restrictions on the community, and lack of education are the sociocultural challenges for the community on tourism development. The Riverston area is getting developed in terms of infrastructure with tourism development, and that benefits the living conditions of the community. Further, results indicated forest conversation policies had impacted the community’s living in high magnitude. It persuades people to move away from agriculture to the tourism sector. In addition, the timely need for a well-planned sustainable tourism approach and awareness from ground-level tourism practitioners was underscored by the data set.

The purpose of this article is to conduct a bibliometric analysis of the cellular manufacturing system (CMS) literature published from 1982–2021 to identify key issues and trends for the future.

A six-stage methodology is used to conduct a literature review, which includes: (1) article collection; (2) inclusion/exclusion criteria; (3) reviewing the articles; (4) analyzing the articles; (5) framework development; and (6) future research directions. A total of 936 CMS-specific articles are reviewed. This paper made use of three software tools: the R package, VOSviewer and SciMAT.

According to the findings, the majority of CM researchers focused on cell formation and design. The USA, Iran and India are the top three leading publishers. Additionally, the gap and future direction of CM are discussed.

To the best of the authors' knowledge, the current study is the first attempt to investigate CMS evaluation through bibliometric and thematic analysis and provides a decisional framework as well as steps for CMS adoption. For individuals who are interested in understanding more about CMS and its evolution, this paper offers a starting point.

The present paper explores the concept of a cashier-free checkout system from customers' perspective. Thus, the study attempts to find out the overall sentiment of people towards cashier-free checkout retail stores.

The study culled 7,348 comments from 10 videos about the Amazon Go store available on YouTube. The paper extracted positive and negative sentiments as well as their affective scores from the comments using Python 3.10.

The results revealed that all key service attributes of Amazon Go stores have a positive impact on customers' overall in-store shopping experience except pay. Similarly, the viewpoints expressed about these stores positively influence their overall in-store shopping experiences except for job. The job has an adverse effect on the outcome variable compared to like and support.

This paper states how a cashier-free checkout system is a better and more innovative retailing solution than in-store cash counter practices. Additionally, the paper provides guidance on how to deal with textual data and quantitative ratings separately when establishing empirical relationships.

This study examines the case of an initial training, called “Dojo”, invented and implemented at a production site in the Czech Republic. It clarifies the initial training program implementation process and offers a conceptual framework for cooperative management of subsidiary activities at the site and firm.

This study conducts an in-depth analysis of qualitative data from the Czech production site over a five-year period. The theoretical base is the theorization and labeling phase of management innovation (MI), the final phase of which legitimizes a new management practice. Interview data, archival data, pictures and financial data are used for the analysis.

To legitimize the Dojo in the operational flow controlled by the site and firm, the Czech production site acquires validation of the Dojo from employees and board members of the Japanese and European headquarters, helping the site build trustful relationships with them. Training programs, process standardization and skills standardization of the workers offer benefits to the trainees, production site and firm.

The authors offer theoretical insights into MI at the subsidiary-level, which past studies have not differentiated at the firm-level. The authors also provide details of the implementation and management of initial training for newly hired blue-collar workers at the production site. The findings complement related literature on human resource management and operational management.

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.

In this paper, the authors examine the strategies used to reduce labour costs in three public hospitals in South Africa, which were effective and why. In the democratic era, after the revelations of large-scale corruption, the authors ask whether their case studies provide lessons for how public service institutions might re-make themselves, under circumstances of austerity.

A comparative qualitative case study approach, collecting data using a combination of interviews with managers, focus group discussions and interviews with shop stewards and staff was used.

Management in two hospitals relied on their financial power, divisions between unions and employees' loyalty. They lacked the insight to manage different actors, and their efforts to outsource services and draw on the Extended Public Works Program failed. They failed to support staff when working beyond their scope of practice, reducing employees' willingness to take on extra responsibilities. In the remaining hospital, while previous management had been removed due to protests by the unions, the new CEO provided stability and union–management relations were collaborative. Her legitimate power enabled unions and management to agree on appropriate cost cutting strategies.

Finding an appropriate balance between the new reality of reduced financial resources and the needs of staff and patients, requires competent unions and management, transparency and trust to develop legitimate power; managing in an authoritarian manner, without legitimate power, reduces organisational capacity. Ensuring a fair and orderly process to replace ineffective management is key, while South Africa grows cohorts of competent managers and builds managerial experience.

The purpose of this paper is to explore and disseminate knowledge about quantum-inspired computing technology's potential to solve complex challenges faced by the operational agility capability in Industry 4.0 manufacturing and logistics operations.

A multi-case study approach is used to determine the impact of quantum-inspired computing technology in manufacturing and logistics processes from the supplier perspective. A literature review provides the basis for a framework to identify a set of flexibility and agility operational capabilities enabled by Industry 4.0 Information and Digital Technologies. The use cases are analyzed in depth, first individually and then jointly.

Study results suggest that quantum-inspired computing technology has the potential to harness and boost companies' operational flexibility to enhance operational agility in manufacturing and logistics operations management, particularly in the Industry 4.0 context. An exploratory model is proposed to explain the relationships between quantum-inspired computing technology and the deployment of operational agility capabilities.

This is study explores the use of quantum-inspired computing technology in Industry 4.0 operations management and contributes to understanding its potential to enable operational agility capability in manufacturing and logistics operations.

Although the previous generations of the Industrial Revolution have brought many advantages to human life, scientists have been looking for a substantial breakthrough in creating technologies that can improve the quality of human life. Nowadays, we can make our factories smarter using new concepts and tools like real-time self-optimization. This study aims to take a step towards implementing key features of smart manufacturing including  preventive self-maintenance, self-scheduling and real-time decision-making.

A new bi-objective mathematical model based on Industry 4.0 to schedule received customer orders, which minimizes both the total earliness and tardiness of orders and the probability of machine failure in smart manufacturing, was presented. Moreover, four meta-heuristics, namely, the multi-objective Archimedes optimization algorithm (MOAOA), NSGA-III, multi-objective simulated annealing (MOSA) and hybrid multi-objective Archimedes optimization algorithm and non-dominated sorting genetic algorithm-III (HMOAOANSGA-III) were implemented to solve the problem. To compare the performance of meta-heuristics, some examples and metrics were presumed and solved by using the algorithms, and the performance and validation of meta-heuristics were analyzed.

The results of the procedure and a mathematical model based on Industry 4.0 policies showed that a machine performed the self-optimizing process of production scheduling and followed a preventive self-maintenance policy in real-time situations. The results of TOPSIS showed that the performances of the HMOAOANSGA-III were better in most problems. Moreover, the performance of the MOSA outweighed the performance of the MOAOA, NSGA-III and HMOAOANSGA-III if we only considered the computational times of algorithms. However, the convergence of solutions associated with the MOAOA and HMOAOANSGA-III was better than those of the NSGA-III and MOSA.

In this study, a scheduling model considering a kind of Industry 4.0 policy was defined, and a novel approach was presented, thereby performing the preventive self-maintenance and self-scheduling by every single machine. This new approach was introduced to integrate the order scheduling system using a real-time decision-making method. A new multi-objective meta-heuristic algorithm, namely, HMOAOANSGA-III, was proposed. Moreover, the crowding-distance-quality-based approach was presented to identify the best solution from the frontier, and in addition to improving the crowding-distance approach, the quality of the solutions was also considered.