A multidisciplinary systematic review of the use of diagrams as a means of collecting data from research subjects: application, benefits and recommendations

BACKGROUND: In research, diagrams are most commonly used in the analysis of data and visual presentation of results. However there has been a substantial growth in the use of diagrams in earlier stages of the research process to collect data. Despite this growth, guidance on this technique is often isolated within disciplines. METHODS: A multidisciplinary systematic review was performed, which included 13 traditional healthcare and non-health-focused indexes, non-indexed searches and contacting experts in the field. English-language articles that used diagrams as a data collection tool and reflected on the process were included in the review, with no restriction on publication date. RESULTS: The search identified 2690 documents, of which 80 were included in the final analysis. The choice to use diagrams for data collection is often determined by requirements of the research topic, such as the need to understand research subjects' knowledge or cognitive structure, to overcome cultural and linguistic differences, or to understand highly complex subject matter. How diagrams were used for data collection varied by the degrees of instruction for, and freedom in, diagram creation, the number of diagrams created or edited and the use of diagrams in conjunction with other data collection methods. Depending on how data collection is structured, a variety of options for qualitative and quantitative analysis are available to the researcher. The review identified a number of benefits to using diagrams in data collection, including the ease with which the method can be adapted to complement other data collection methods and its ability to focus discussion. However it is clear that the benefits and challenges of diagramming depend on the nature of its application and the type of diagrams used. DISCUSSION/CONCLUSION: The results of this multidisciplinary systematic review examine the application of diagrams in data collection and the methods for analyzing the unique datasets elicited. Three recommendations are presented. Firstly, the diagrammatic approach should be chosen based on the type of data needed. Secondly, appropriate instructions will depend on the approach chosen. And thirdly, the final results should present examples of original or recreated diagrams. This review also highlighted the need for a standardized terminology of the method and a supporting theoretical framework

Simultaneous planning and scheduling for multi-autonomous vehicles

With the increasing applications of autonomous vehicles in dynamic and strictly constrained environments such as automated container terminals, efficient task/resource allocation and motion coordination (i.e., path and speed planning) of multi-autonomous vehicles has become the critical problem and have therefore been recently recognized as the key research issues by both academics and industry. This chapter addresses a generic approach for integration of task allocation, path planning and collision avoidance, which has so far not attracted much attention in the academic literature. A Simultaneous Task Allocation and Motion Coordination (STAMC) approach is presented. Two metaheuristic algorithms, i.e. simulated annealing and ant colony, and an auction algorithm are investigated and applied. The proposed approach is able to solve the scheduling, planning and collision avoidance problems simultaneously; improve the usage of bottleneck areas; handle dynamic traffic conditions and avoid deadlock. Simulation results demonstrated the effectiveness and efficiency of this approach. © Springer-Verlag Berlin Heidelberg 2007