The Multiple Traveling Salesman Problem (MTSP) was able to model and solve various theoretical and real-life applications. This problem is one of the many difficult issues that have no perfect solution yet. In this paper, on the one hand genetic algorithms with different combinations of operators and simulated annealing were used to solve the MTSP. On the other hand, the genetic algorithm with the combination of operators that gave the best solutions of the MTSP was hybridized with a Simulated Annealing algorithm. The simulation results showed that the hybrid algorithm significantly outperforms most of the comparable methods in obtaining the best-fitness solutions compared to the other methods in most of the test cases. In addition, by scaling the fitness function according to the amplitude of tours, it was obvious that the non-dominated front obtained by the hybrid algorithm was better than the non-dominated front obtained by the other algorithms.

This paper studies the problem of assigning tasks to a workforce with different skills. The problem is modeled as an unrelated parallel scheduling problem, incorporating sequence-dependent setup times (UPMSPSDST). Exact methods generally are not able to solve real large problems of UPMSPSDST. Hence, this research introduces an efficient, straightforward metaheuristic solution leveraging the Ant Colony Optimization (ACO) algorithm. The objective is to minimize the total completion time while assigning jobs to unrelated parallel machines with sequence-dependent preparation times. The algorithm establishes a threshold for improving the Ants (solutions) to select only promising ants for the improvement phase, thereby reducing the computational effort performed by local search operators. The proposed ACO algorithm maintains a basic structure and could be extended to solve other scheduling problems. A set of test instances available in the literature has been used to validate the efficiency of the proposed methodology. In addition, the results have been compared with the best previously published works. The ACO algorithm improves 30% of the best-known solutions (BKS) and reaches 30% of the BKS. The results show that the average performance of the ACO algorithm exceeds the average performance of the methods used by the best previously published works for the UPMSPSDST.

This study endeavors to evaluate the effectiveness of green distribution and green supply chain practices at PT Bio Farma (Persero) in relation to polio vaccine products. The ultimate objective is to establish and attain future green distribution targets. The research methodology utilizes the Green Supply Chain Operations Reference (Green SCOR) model and the Analytical Hierarchy Process (AHP) to analyze the production and distribution data of polio vaccine (OPV) and inactivated polio vaccine (IPV) products from January to December 2023. The performance values for each key component of Green Distribution and Green Supply Chain are as follows: Plan = 22.9, Source = 16.60, Make = 21.53, Deliver = 11.24, and Return = 7.08. The total performance value for Green Distribution and Green Supply Chain is 79.41. According to APICS standards, the performance of Green Distribution and Green Supply Chain is rated as “Good”. Future performance improvements will focus on supply chain and collaborative planning, establishing long-term partnerships with suppliers and customers, optimizing environmentally friendly transportation, and developing the enterprise information system. Our study proposes that the distribution and logistics managers at PT Bio Farma should persistently monitor and assess their operations to enhance the efficiency of their green distribution and green supply chains. By doing so, they can ensure the successful attainment of performance objectives for disseminating the polio vaccine in Indonesia. This research presents a significant advancement by incorporating the Green SCOR framework into the specific domain of polio vaccines, emphasizing sustainability and environmental efficiency. This approach paves the way for new avenues of investigation in green supply chain research and offers a novel viewpoint on how the pharmaceutical industry, specifically vaccine distribution, can utilize sustainable practices to enhance their operations while mitigating environmental ramifications.

This research presents a novel approach to address the Virtual Machine Placement Problem (VMPP) in cloud data centers with the aim of minimizing energy consumption. The main contributions of this study are threefold. Firstly, a Duelist Algorithm specifically designed for VMPP, which introduces a unique concept of duelists combined with optimization techniques. The algorithm aims to strike a balance between exploration and exploitation in the search space, leading to more effective resource allocation and energy-efficient cloud data center management. Secondly, enhance the performance of the Duelist Algorithm by reducing the number of algorithm-specific parameters. This simplifies the implementation process and increases the algorithm's adaptability to various real-world problems, making it more user-friendly and robust. Lastly, conduct a comprehensive comparison of the Duelist Algorithm with the widely used Hybrid Harmony Search Algorithm (HS+SA+LS) in terms of energy consumption and overall efficiency. The experimental results demonstrate that the Duelist Algorithm consistently outperforms the Hybrid Harmony Search Algorithm, achieving remarkable improvements in both best and mean fitness values. Additionally, the Duelist Algorithm exhibits lower standard deviation values, indicating more stable and consistent performance. The findings of this research validate the effectiveness of the proposed Duelist Algorithm in minimizing energy consumption and optimizing cloud resource allocation. The reduction of algorithm-specific parameters further contributes to its versatility and simplicity.

Small and medium entities (SMEs) may not have an appreciable impact on the economy of a country unless they are performing well. In the UAE, the performance of SMEs still appears to be unimpressive despite the actions of the government. Cross-sectional research design and survey methods were used in research work since the managers and employees of SMEs were the primary sources of data. The result from the study revealed that organizational factors positively and significantly impacts SMEs performance. Future researchers will benefit largely from this present study because it will guide them on how and where to focus their research. The findings of the study will assist UAE agencies and institutions in understanding the mediating role of ABC adoption as the key drivers and exert a strong influence on performance for SMEs in UAE. The government and policymakers should look at the involvement of external partners in facilitating innovation activities among SMEs in UAE.

In Dubai, the government alone cannot bring about economic development and provide jobs to all and sundry. Businesses established by individuals and corporate entities aid the government in the provision of jobs to people and contribute to the economic growth and development of a country. The role of SMEs is crucial in most of the countries of the world and they contribute significantly towards provision of employment opportunities in their local communities and development of their economies. The main objective of this study is to examine the factors influencing the performance of SMEs in the UAE. The findings from the analysis found that technological factors and environmental factors have a positive and significant impact on SMEs performance. This study has a significant contribution to the body of literature as it provides a worthy theoretical framework in it. Besides theoretical contribution, this study also provides practical implications to SMEs, government and policymakers. The future direction of this study would enhance the future body of literature.

In this paper, the authors proposed a new framework for Mobile application adoption by unorganized retailers. Unorganized retail is a renowned retail business in India. Unorganized retail is a so-called low-cost retail format where some of the retail stores are Kirana store, Grocery store, provisional store, ready to eat store, mom & pop store, peddlers, Hawkers, and stationery store. Retailers face stiff competition from omnichannel, multi-channel retailers, and e-tailers. To counter this competition, unorganized retailers adopt some Mobile applications which are relevant to their business. While some of the Mobile Applications are WhatsApp business, Facebook page, telegram, blogs, google maps Mandi app, Udaan app, Katha book app, OK credit, Dukan app, and just dial. This paper explains a deep discussion about unorganized retailers, unorganized retailers' contributions to the economy, the Digital India program, and Mobile Application. A new comprehensive framework has been proposed for mobile application adoption by the unorganized retailer after getting insights from theories like Technology Adoption Model (TAM), Extension of Technology Adoption Model (TAM), Technology, Organization and Environment (TOE), and Diffusion of Innovation (DOI). In this research, the author segments three major clusters which influence mobile technology adoption. The three major clusters are Technology, Organization, and Environment. These clusters had 10 constructs that influence mobile technology adoption. This research elaborately discussed the drivers of technology adoption in an unorganized retail context. We close by concluding that mobile application adoption by the unorganized retailer and reshaped the unorganized retail.

Manual labour in slaughterhouses is hazardous work. Workers suffer from injuries and occupational illnesses resulting from repetitive movements with sharp knives. There is a need for a robotic tool which can perform versatile tasks with a high level of precision. This knife must be able to imitate the same primary cuttings of a professional butcher and produce meat products which are acceptable to the end-market. This paper reports the results of a world-wide assessment of the fresh pork meat cuts as a performance indicator of knives specifically adapted for automated operation. These knives included Victorinox knife, bespoke double bladed Uddeholm knife, vibrating knife and novel smart knife with built-in sensing mechanism that detects in real time the contact with meat and cut depth. Physical appearances of cuts were assessed anonymously by independent responders with different backgrounds. All knives were deemed acceptable in terms of cutting quality. There was also no discernible difference of opinion between manual and robot cuts. This indicates that the new knife for robot-assisted cutting is acceptable by market.

This study aims to analyse the total quality management tools for improving service quality and client satisfaction in healthcare settings through a systematic qualitative review. Data was collected through the web of sciences (WOS), Scopus, EBSCO, PubMed, and Medline. Initially, we found 573 articles from all the sources, but after eliminating the non-relevant articles, only 24 usable articles were finalized. Furthermore, 12 articles were purely related to TQM, service quality, and client satisfaction. This study concludes that TQM practices and tools improve service quality and client satisfaction in healthcare organizations. This study provides excellent managerial and practical insights. Managers should implement the TQM tools to improve service quality and client satisfaction. This way, customer satisfaction is enhanced, and patient satisfaction is improved, leading to high operational and overall performance. This study also reveals a need for further studies to clarify the role of TQM tools on service quality and patient satisfaction.

The poor maintenance and usage of the equipment and machines of a vegetable oil manufacturing company adversely affect its competitive advantage. These industries are faced with numerous equipment maintenance challenges in the path to increasing their throughput as well as profitability. To address the said maintenance challenges, process data were obtained for the Overall Equipment Effectiveness (OEE) factors after their Total Productive Maintenance (TPM) implementation in the company. Minitab 21 software was used to analyze the data collected, and the results showed that the mean for quality, availability, and performance obtained were 96.929%, 63.35%, and 61.20%, respectively. This shows that the quality of products is the greatest OEE factor that vegetable oil manufacturing companies must consider meticulously to reduce the six big losses in their production processes. Response Surface Method (RSM) with Central Composite design, with the application of Design Expert 13 software, was used to model, analyze, and optimize the Overall Equipment Effectiveness (OEE) using availability, quality, and performance as the input parameters. The analysis of both the actual and coded values, which is the main contribution of the study, showed that quality has the greatest value followed by availability and performance. It was found that, to effectively reduce the six big losses, the quality, performance, and availability should be targeted as 98.3052%, 81.6022%, and 80.103%, respectively.