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This paper aims to provide a method for media resource allocation in Cloud systems for supporting green computing policies, as well as attempting to improve the overall performance of system by optimizing the communication latencies.

A common method for resource allocation is using resource agent that takes the budgets/prices of applicants/resources and creates a probability matrix of allocation according to the policies of system. Two general policies for optimization are latency optimization and green computing. Presented heuristic for latencies is so that the average latencies of communication between applicant and resource are measured, and they will affect the next decision. For gaining green computing, it is attempted to consolidate the allocated resources on smaller number of physical machines. So calculation formula of the price of each resource is modified to decrease the probability of allocating the resources on the machine with least allocated resources.

Results of proposed method indicates its success in both green computing and improving the performance. Experiments show decreasing 21.4 per cent of response time simultaneously with increasing tasks in the tested range. The maximum and minimum of saved energy is acceptable and reported as 79.2 and 16.8 per cent.

Like other centralized solutions, the proposed method suffers from the limitations of centralized resource agent, like bottle neck. But the implementation of distributed resource agent is postponed to future work.

Proposed method presents heuristics for improving the performance and gaining green computing. The key feature is formulating all the details and considering pitch variables for controlling the policies of system.

The human resource (HR) allocation problem is one of the critical dimensions of the project management process. Due to this nature of the problem, researchers are continually optimizing one or more critical scheduling and allocation challenges in different ways. This study aims to optimize two goals, increasing customer satisfaction and reducing costs using the imperialist competitive algorithm.

Cloud-based e-commerce applications are preferred to conventional systems because they can save money in many areas, including resource use, running expenses, capital costs, maintenance and operation costs. In web applications, its core functionality of performance enhancement and automated device recovery is important. HR knowledge, expertise and competencies are becoming increasingly valuable carriers for organizational competitive advantage. As a result, HR management is becoming more relevant, as it seeks to channel all of the workers’ energy into meeting the organizational strategic objectives. The allocation of resources to maximize benefit or minimize cost is known as the resource allocation problem. Since discovering solutions in polynomial time is complicated, HR allocation in cloud-based e-commerce is an Nondeterministic Polynomial time (NP)-hard problem. In this paper, to promote the respective strengths and minimize the weaknesses, the imperialist competitive algorithm is suggested to solve these issues. The imperialist competitive algorithm is tested by comparing it to the literature’s novel algorithms using a simulation.

Empirical outcomes have illustrated that the suggested hybrid method achieves higher performance in discovering the appropriate HR allocation than some modern techniques.

The paper presents a useful method for improving HR allocation methods. The MATLAB-based simulation results have indicated that costs and waiting time have been improved compared to other algorithms, which cause the high application of this method in practical projects.

The main novelty of this paper is using an imperialist competitive algorithm for finding the best solution to the HR allocation problem in cloud-based e-commerce.

Developing an optimized project schedule that considers all decision criteria represents a challenge for project managers. The purpose of this paper is to provide a multi-objectives overall optimization model for project scheduling considering time, cost, resources, and cash flow. This development aims to overcome the limitations of optimizing each objective at once resulting of non-overall optimized schedule.

In this paper, a multi-objectives overall optimization model for project scheduling is developed using particle swarm optimization with a new evolutionary strategy based on the compromise solution of the Pareto-front. This model optimizes the most important decisions that affect a given project including: time, cost, resources, and cash flow. The study assumes each activity has different execution methods accompanied by different time, cost, cost distribution pattern, and multiple resource utilization schemes.

Applying the developed model to schedule a real-life case study project proves that the proposed model is valid in modeling real-life construction projects and gives important results for schedulers and project managers. The proposed model is expected to help construction managers and decision makers in successfully completing the project on time and reduced budget by utilizing the available information and resources.

The paper presented a novel model that has four main characteristics: it produces an optimized schedule considering time, cost, resources, and cash flow simultaneously; it incorporates a powerful particle swarm optimization technique to search for the optimum schedule; it applies multi-objectives optimization rather than single-objective and it uses a unique Pareto-compromise solution to drive the fitness calculations of the evolutionary process.

This paper evaluates the level of the digital economy in Chinese cities based on digital industrialization and industrial digitalization. The research focuses on the effects of spatial mechanism of the urban digital economy on the quality of firms’ exported products.

The authors use the principal component analysis method to evaluate the level of China’s urban digital economy, and spatial metrology to measure the spatial effects of the digital economy on product quality.

The findings suggest that the urban digital economy can expand the quality of firms’ exports. The digital economy has spatial dependence, spatial spillover and spatial heterogeneity on product quality. At the same time, the spatial effect has a significant nonlinear effect and threshold effect. Further decomposition shows that industrial digitalization is the core factor of enterprises’ export products quality, and the micro-mechanism of this impact is mainly manifested in optimization of resource allocation.

The innovation of this paper is reflected explicitly in exploring the quality upgrading of export products from the background of the digital economy, providing a reference for the improvement of China’s export trade competitiveness and the cultivation of a trade power. The authors studied two different mechanisms (specialization division of labor and optimization of resource allocation) to explain the spatial imbalance of export product quality to provide empirical support for enterprises and government departments to formulate quality upgrading policies accurately.

The purpose of this paper is to identify challenges faced in resource management in the UK construction industry and to propose some solutions to these problems.

Based on a qualitative research methodology, 14 experts from the UK construction industry were chosen to be participants in the study. The participants were equally divided into two focus groups to discuss resource management using five projects as case studies. Thematic analysis of the discussion reveals seven key factors that affect resource management.

The results show that most of the problems identified are due to poor data management processes and the practice of having data in silos. Overcoming this challenge requires the adoption of big data approaches for resource management to allow the integration of large and different forms of data.

This study seeks to bring to the fore challenges faced in resource management by the UK construction industry and to outline some solutions to address them.

Optimizing both qualitative and quantitative factors is a key challenge in solving construction finance decisions. The semi‐structured nature of construction finance optimization problems precludes conventional optimization techniques. With a desire to improve the performance of the canonical genetic algorithm (CGA) which is characterized by static crossover and mutation probability, and to provide contractors with a profit‐risk trade‐off curve and cash flow prediction, an adaptive genetic algorithm (AGA) model is developed. Ten projects being undertaken by a major construction firm in Hong Kong were used as case studies to evaluate the performance of the genetic algorithm (GA). The results of case study reveal that the AGA outperformed the CGA both in terms of its quality of solutions and the computational time required for a certain level of accuracy. The results also indicate that there is a potential for using the GA for modelling financial decisions should both quantitative and qualitative factors be optimized simultaneously.

The purpose of this paper is to propose a Non-Linear Integer Programming (NLIP) model that solves the resource leveling problem while reducing the negative effect of the total float loss on risk.

An NLIP model is formulated to solve the resource leveling optimization problem incorporating float loss cost (FLC). The proposed model is implemented using “What’s Best solver” for Excel. The FLC is calculated using the float commodity approach. An example is solved using the proposed model in order to illustrate its applicability. Sensitivity analysis is also performed.

The results confirmed that resource leveling reduces the available float of non-critical activities; decreases schedule flexibility and reduces the probability of project completion. The probability of timely completion dropped from 50 percent (for the normal schedule with 32 resource fluctuations) to 13.5 percent for leveled resources with zero fluctuations. Using the proposed method, the number of resource fluctuations is 8 but the probability of completing the project on time improved to 20 percent.

The proposed model allows project managers to exercise new trade-offs between resource leveling and schedule flexibility which will ultimately improve the chances of successful project delivery.

Resource leveling techniques result in reducing the available total float for the non-critical activities. Existing methods focus on moving noncritical activities within their available float and ignore the impact of the resulting float loss. This reduces the schedule flexibility and increase the risk of project delays. The proposed model incorporates the FLC into the resource leveling optimization problem resulting in more efficient schedules with improved resource utilization while keeping some schedule flexibility.

Historically, scheduling of production activities has been one of the most important management problems. Scheduling involves the planning and the co‐ordination of the various activities to achieve the optimum utilisation of resources over a given time period. Production scheduling differs with the typology of the production systems. Various production systems that are encountered in practice are: continuous production, mass production, batch production, job shop production and projects. In this article, we attempt only to discuss the project scheduling problems.

The purpose of this paper is to address issues of performance optimization through accounting for asymmetric responses of customer satisfaction to different types of product or service attributes: core, facilitating and “green” (eco‐friendly). The primary research inquiry was to explore how these attributes affect customer satisfaction and account for interactions among them in order to identify an optimal combination that would maximize customer satisfaction in lodging industry settings.

An experimental design and a web‐based survey were used to collect data from a convenience sample of faculty and staff of two US universities. Univariate and regression analysis were two primary methods of data analysis.

The findings confirmed non‐linear nature of customer satisfaction response and indicated that “green” attributes impact customer satisfaction similarly to facilitating attributes but differently from the core type of attributes in the context of lodging industry.

Generalizability of the findings is bounded by convenience sampling technique. Additionally, only limited number of hotel attributes was examined.

The current findings help to solve the problem of performance optimization and allow creating hotel offerings that yield maximum levels of customer satisfaction and optimal resource allocation.

The study provides additional knowledge about factor structure of customer satisfaction and points on the place and role of “green” attributes in formation of CS in the context of lodging industry.

The integration of building information modelling (BIM) and integrated project delivery (IPD) is highly recommended for better project delivery. Although there is a methodology for this integration, the BIM requires some improvements to foster the adoption of IPD. The purpose of this paper is to present an innovative way to support 4D BIM automation/optimisation within the IPD approach. Similar to structural and architectural design libraries, this research proposes a planning library to enable automating the formulation of schedule, as well as embedding the multi-objective optimisation into the 4D BIM.

The literature review was used to highlight the existing attempts to support the automation process for 4D BIM and the multi-objective schedule optimisation for construction projects. A case study was done to validate the developed framework and measure its applicability.

The results show that there is a cost-saving of 22.86 per cent because of using the proposed automated multi-objective optimisation. The case study shows the significance of integrating activity-based costing into 4D BIM to configure the hierarchy level of overhead activities with the IPD approach; therefore, the maximum level of contribution in managing the IPD project is 33.33 per cent by the trade package level and the minimum contribution is around 8.33 per cent by the project level.

This research presents a new philosophy to develop the 4D BIM model – planning and scheduling – a BIM library of the project activities is developed to enable the automation of the creation of the project schedule with respect to the 3D BIM design sequence. The optimisation of the project duration is considered to be automated within the creation process by using the proposed genetic algorithm model.