Safety Risk Analysis of Large⁃scale Complex Project based on WBS⁃RBS and Interval Numbers

Safety risks are essential to the success or failure of the large⁃scale complex projects.In order to assess and evaluate the safety risks of the large⁃scale complex projects scientifically,a risk assessment method of work breakdown structure and risk breakdown structure(WBS⁃RBS)is proposed to identify the project risks.In this paper,interval numbers are used to evaluate the risk levels,weights are assigned automatically based on the complexity and risk degree of WBS to distinguish three types of nodes in WBS,and a risk assessment algorithm is designed to assess safety risk at all layers of the project.A case study is conducted to demonstrate how to apply the method.The results show the practicality,robustness and efficiency of our new method,which can be applied to different kinds of large⁃scale complex projects in reality.

Research on Safety Risk Assessment Method for Large Scale Field Operation Project

By applying man-machine-environment system engineering theory, safety risks on large scale field operation project have been evaluated in this article. The factors concerning with the man, machine and environment in system were proposed separately. The value for lowest indexs was determined by decision-making of expert group. The weights were calculated based on AHP, and then safety risk assessment in different layers was made. The results show that the assessment method is reasonable, and it is significant for large scale field operation project safety managerment.

Large Scale Infrastructure Projects: The Art of Project or Change Management?

Although it is generally understood that change is a fundamental component of managing projects in the construction industry in general and an inevitable challenge for large scale infrastructure project in particular, there has been little to no attention in the literature to understand change in this context from a more holistic perspective. For this purpose, this work looks at change through the eyes of a framework of six batteries of change that seem essential to charge an organization's capabilities for change. The framework brings together the expertise of four specialists that all have developed their insights over many years of study and practice, and has been validated through an extensive review of the management literature on organization development and change. Reflections on the application of this model in the construction industry and in large scale infrastructure projects demonstrate that energizing organizations to successfully deal with change goes beyond the traditional techniques of managing change from a program or project management perspective. Assessing the six batteries of change in this context can help organizations to develop capabilities for change that build change energy by balancing formal/rational methods with informal/emotional interventions at both a local(department/subproject) and global(business) level.

Managing Software Testing Technical Debt Using Evolutionary Algorithms

Technical debt(TD)happens when project teams carry out technical decisions in favor of a short-term goal(s)in their projects,whether deliberately or unknowingly.TD must be properly managed to guarantee that its negative implications do not outweigh its advantages.A lot of research has been conducted to show that TD has evolved into a common problem with considerable financial burden.Test technical debt is the technical debt aspect of testing(or test debt).Test debt is a relatively new concept that has piqued the curiosity of the software industry in recent years.In this article,we assume that the organization selects the testing artifacts at the start of every sprint.Implementing the latest features in consideration of expected business value and repaying technical debt are among candidate tasks in terms of the testing process(test cases increments).To gain the maximum benefit for the organization in terms of software testing optimization,there is a need to select the artifacts(i.e.,test cases)with maximum feature coverage within the available resources.The management of testing optimization for large projects is complicated and can also be treated as a multi-objective problem that entails a trade-off between the agile software’s short-term and long-term value.In this article,we implement a multi-objective indicatorbased evolutionary algorithm(IBEA)for fixing such optimization issues.The capability of the algorithm is evidenced by adding it to a real case study of a university registration process.