Production rates for various activities and overall construction project duration are significantly influenced by crew formation. Crews are composed of available renewable resources. Construction companies tend to reduce the number of permanent employees, which reduces fixed costs, but at the same time limits production capacity. Therefore, construction project planning must be carried out by means of scheduling methods which allow for resource constrains. Authors create a mathematical model for optimized scheduling of linear construction projects with consideration of resources and work continuity constraints. Proposed approach enables user to select optimal crew formation under limited resource supply. This minimizes project duration and improves renewable resource utilization in construction linear projects. This paper presents mixed integer linear programming to model this problem and uses a case study to illustrate it.

An accurate use of the ability to steer computer efficiency is essential from the database point of view. Effective resource allocation is dependent on the performance indicators gathered from running systems. There must be an appropriate balance between accurate measurements, performance indicators and speed of the reallocation algorithms of the computing resources. The extended measurement of efficiency which the authors propose for applications is: the average number of queries within a time unit for particular groups of users. This paper presents an analysis of using the Workload Manager utility in the AIX 5L operating system to improve the efficiency of applications in the MySQL database environment, and an analysis of methods which allows the use of Workload Manager for steering efficiency dynamically.

In this study, the concepts of simultaneous user association and resource allocation in non-orthogonal multiple access systems have been investigated. Subscribers are randomly distributed in them. In the paper, a novel cooperative energy harvesting model is introduced so that user equipment near to the base stations acts as relay for further subscribers. In order to consider the local limitations of alternative energy resources, it was assumed that alternative energy would be shared among the base stations by means of the dynamic grid network. In this architecture, non-orthogonal resource allocation and user association frameworks should be reconfigured because conventional schemes use orthogonal multiple access. Hence, this paper suggests a novel approach to joint optimum cooperative power allocation and user association techniques to achieve a maximum degree of energy efficiency for the whole system in which the quality of experience parameters are assumed to be bounded during multi-cell multicast sessions. The model was also modified to develop joint multi-layered resource control and user association that can distinguish the service pattern in cooperative energy heterogeneous systems with non-orthogonal multiple access to obtain more resource optimality than in the current approaches. The effectiveness of the suggested approach is confirmed by numerical results. Also, the results reveal that non-orthogonal multiple access can provide greater energy efficiency than the conventional orthogonal multiple access approaches such as e.g. the MAX-SINR scheme.

The unpredictable and huge data generation nowadays by smart devices from IoT and mobile Crowd Sensing applications like (Sensors, smartphones, Wi-Fi routers) need processing power and storage. Cloud provides these capabilities to serve organizations and customers, but when using cloud appear some limitations, the most important of these limitations are Resource Allocation and Task Scheduling. The resource allocation process is a mechanism that ensures allocation virtual machine when there are multiple applications that require various resources such as CPU and I/O memory. Whereas scheduling is the process of determining the sequence in which these tasks come and depart the resources in order to maximize efficiency. In this paper we tried to highlight the most relevant difficulties that cloud computing is now facing. We presented a comprehensive review of resource allocation and scheduling techniques to overcome these limitations. Finally, the previous techniques and strategies for allocation and scheduling have been compared in a table with their drawbacks.

Small and medium-sized enterprises (SMEs) are facing barriers to grow due to the lack of structured procedures for upgrading and allocating the limited resources. To overcome these drawbacks and to improve business capabilities, a structured framework to conduct a comprehensive diagnostic and upgrading study is presented in this paper. The proposed framework involves four phases. First, the external and internal strategic factors, which can affect the enterprises’ performance are evaluated using strategic planning and assessment tools. Second, key upgrade performance indicators are developed and evaluated using multi-attribute rating techniques to guide, evaluate, and track progress of upgrading process. Third, a set of upgrade strategies are generated and evaluated using resource allocation model. Finally, a periodic re-evaluation plan is introduced to monitor the implementation progress. The developed framework for performance evaluation and upgrading is suitable to be used as a structured know-how procedure in manufacturing enterprises and can support entrepreneurs in their strategic decisions. To validate the proposed framework, a data set was collected from a local housecore company. As a result, one package of the efficient frontier strategies that represents the best use of resources was proposed for implementation.