Vendor lock-in occurs when switching to another cloud vendor is costly, resulting in organizations being stuck with a single cloud service provider. While multi-cloud strategies are often used to avoid vendor lock-in, they also come with drawbacks such as increased costs and management complexity. To overcome this, researchers have proposed the H2RUN optimization algorithm to achieve a tradeoff between cost and availability using erasure coding. This algorithm selects the optimal CSP and erasure coding parameters to enhance availability with minimal cost. Experiments on real-time CSPs have shown that H2RUN can minimize costs by up to 48% and increase availability by up to 58%. This solution could benefit organizations that are cautious about achieving a balance between cost and availability when mitigating the risk of vendor lock-in.

Cloud forging is a manifestation of "manufacturing as service" and combines advanced manufacturing, information, and the Internet of Things technology. It is essential to optimize resource selection to make the most of the manufacturing potential of cloud forging. A research paper proposes a cloud forging resource service optimization strategy that uses a genetic algorithm. At the core of this strategy is the focus on the optimal selection of manufacturing resources before scheduling. Three essential indexes for cloud forging resource service optimization are identified: sharing cost, interaction time, and service quality. These three are quantified, and the genetic algorithm is then used to solve the cloud forging resource service optimization model. This results in the selection of the best-performing cloud forging resource service. The method's effectiveness is verified using an example. By using this research and proposed method, manufacturing companies can take advantage of the manufacturing resources offered by cloud forging to make smarter, more informed decisions about their businesses.

A cloud manufacturing scheduling optimization algorithm based on non-cooperative games has been proposed to process multi-objective requirements of products. The algorithm utilizes non-cooperative game strategies to deal with the scheduling question of the deadline and cost constraints of products. The algorithm takes into account optimal allocation resources of cloud manufacturing and uses Nash equilibrium strategy in the selection of network resources in the scheduling process. The proposed algorithm can effectively solve the scheduling problem with deadline and cost constraints in cloud manufacturing. Through the use of non-cooperative games, the algorithm is equipped to factor in multiple objectives and find optimal solutions for cloud manufacturing scheduling problems. Overall, the algorithm demonstrates an important advance in cloud manufacturing scheduling optimization.

Cloud database systems differ from traditional database systems in that they are charged based on their usage, which means users need to consider both query response time and monetary cost for selecting a database product. Existing database query optimization techniques are not suitable for cloud systems, as they focus on one-dimensional objectives, such as reducing query response time or I/O cost. As a result, optimizing queries for cloud databases requires targeting the reduction of monetary cost in addition to query response time, which is a more challenging multi-objective problem. Furthermore, query re-optimization becomes more complex in cloud databases. A new query optimization method has been proposed that addresses both of these issues. The approach achieves two goals: firstly, it identifies a query execution plan that meets user-provided multi-objective requirements, and secondly, it reduces running costs by performing adaptive re-optimization during query execution. The experimental results show that the proposed method can save both time and monetary costs, depending on the type of queries being executed.

A low-cost and accurate automatic fever screening system has been developed to assist in preliminary health assessment during the COVID-19 pandemic. The system integrates a RGB camera, thermal camera, embedded system board, and cloud analytics platform to extract forehead temperature and assess if the person being evaluated is wearing a mask or not. Trial runs have been conducted in crowded settings including schools, restaurants, community centres, and commercial buildings, and the solution has been made widely accessible at a market acceptance price. The system addresses the problems associated with non-contact infrared thermometers and thermal imaging systems, which involve physical proximity, additional manpower and time, inaccuracy, or expense. By utilizing temperature measurement and face detection, the system is an effective measure to reduce the risk of COVID-19 spreading in the community. The development of this system provides a solution to the current global pandemic and can assist in lowering the burden on healthcare systems.

The use of blockchain technology in supply chain management can result in intelligent inventory management that can reduce operational costs and boost profits. A paper delves into the issue of inventory costs control, and suggests that if the supplier’s inventory is insufficient, the chances of trading a product will be reduced, while the manufacturer's inadequate material inventory will lead to the termination of production, delays, and waste of resources and time. Furthermore, postponed transportation will eventually raise costs such as transportation costs and cancellation of orders. The paper proposes the use of blockchain technology in a cloud environment to secure the data of distributors and prevent unauthorized access to data, and a novel hybrid optimization algorithm called Whale-based Multi Verse Optimization (W-MVO) algorithm is used to minimize inventory costs. Once the cost is reduced, each distributor stores the data in a blockchain under the cloud sector, where each distributor holds a hash function to store its data, which cannot be restored by the other distributers. The proposed model is said to be more effective and reliable than conventional inventory cost control models.

This article highlights the importance of outsourcing in cloud manufacturing and investigates the use of a multi-objective optimization model and heuristic approach to reduce costs for cement equipment manufacturing enterprises. A matrix model based on ontology for outsourcing resource optimization is established and an improved self-adaptive genetic algorithm (SEGA) developed. Results show that the proposed method is more efficient and preferable in searching for optimal solutions, as evidenced by a case study and comparison of performances with a simple genetic algorithm (SGA) and SEGA. The study provides valuable insights into how manufacturing companies can make use of outsourcing to focus on their core business, reduce costs, and achieve better results through optimization techniques. The study is of particular relevance to manufacturing enterprises operating in a cloud manufacturing environment where outsourcing is becoming increasingly common. Overall, the findings suggest that the proposed approach can be used to guide decision-making processes in companies seeking to optimize their outsourcing resource usage.

Cloud manufacturing is a service-oriented paradigm that uses distributed manufacturing resources to perform a manufacturing task while considering Quality of Service (QoS) requirements. The incorporation of eco-friendliness and sustainability into cloud manufacturing has become a pressing concern, given fierce market competition and growing environmental consciousness among consumers. This paper details a multi-objective optimization approach to the selection and scheduling of cloud manufacturing services that consider both economic and environmental perspectives, including carbon emissions and water resource management. The proposed model employs the ε-constraint method to identify the appropriate Pareto set of solutions, subject to carbon cap regulation, for minimizing total costs, carbon emissions, and water resource utilization while accounting for transportation mode choices and carbon emissions arising from manufacturing services and transportation activities. The ε-constraint method demonstrates the effectiveness of the presented approach by generating a diverse set of solutions, and it can feasibly solve the given model. The paper concludes that this methodology is promising for simultaneously considering economic and environmental concerns in cloud manufacturing services.

The study focuses on the supply chain management of potatoes in Garut Regency and offers a scenario for the distribution of goods, which was carried out through a descriptive quantitative method based on time. The data was gathered through secondary sources and interviews with the directors of three major trading companies in the region. The results show that the least cost method was found to be the most effective for the distribution of potatoes, with the lowest cost being the delivery of potatoes from trading company 1 to the Bogor area. The Cikajang sub-district was found to have the highest average harvested area, but it has decreased from year to year. Potato production in the Cikajang District has also declined every year, and this needs to be studied from various aspects. Overall, the study highlights the importance of supply chain management in the business world and the need for effective distribution strategies. The findings can be useful for companies and policymakers looking to improve the supply chain management of potatoes in the region and promote sustainable potato production.

The paper discusses the production planning of mass customization enterprises in a cloud manufacturing environment by analyzing the attribute index of manufacturing resource combination and constructing a multiobjective optimization model based on product delivery date, cost, and quality. Using the NSGA-II algorithm, the Pareto solution set of production plans is obtained, and a six-tier attribute index system evaluation model is established for optimization. The weight coefficients of attribute indexes are calculated using the analytic hierarchy process and entropy weight method, and the improved TOPSIS method is used to obtain the optimal production plan by ranking. The effectiveness and feasibility of the multiobjective model and NSGA-II algorithm are validated by using the example of company A, and the optimal production plan is obtained by synthetically sorting the production plan set according to the comprehensive evaluation model. The paper concludes that the proposed method can effectively improve the production planning of mass customization enterprises in a cloud manufacturing environment.