Research on collaborative task assignment method of assembly line

Purpose The purpose of this paper is to construct a task assignment model for U-shaped production lines with collaborative task, which is optimized by minimizing the number of workers and balancing the workload of the operators. The ultimate goal is to increase productivity by increasing the U-line balance and balancing the load on the operators. Design/methodology/approach First, task selection and update mechanism are analyzed and the task selection mechanism suitable for collaborative task is proposed. Second, M-COMOSAL is obtained by improving the original COMOSAL. Finally, The M-COMOSAL algorithm and the COMAOSAL algorithm are used to perform job assignment on the double-acting clutch U-shaped assembly line. Findings According to the allocation scheme obtained by M-COMSOAL, the beat can be adjusted according to the change of order demand. The final allocation scheme is superior to the COMSOAL algorithm in terms of number of workers, working time, production tempo and balance rate. In particular, compared with the old scheme, the new scheme showed a decrease of 16.7% in the number of employees and a 18.8% increase in the production line balance rate. Thus, the method is helpful to reduce the number of operators and balance the workload. Originality/value The new algorithm proposed in this paper for the assignment of collaborative task can minimize the number of workers and balance the load of operators, which is of great significance for improving the balance rate of U-shaped production lines and the utilization of personnel or equipment.

Validating the Strategic Task Overload Management (STOM) Model Using MATB II and Eye-tracking

Operators can be overloaded and struggle to make sense of and prioritize multiple tasks. Task selection in these cases is of utmost importance. We replicated an experiment using the Multi Attribute Task Battery II (MATB II) for validating a model of strategic task switching (STOM), adding eye tracking measurement, resulting in a new assessment. Task difficulty affected how operators switched tasks, while priority had little to no effect. Newly measured for STOM, eye tracking revealed a link between task difficulty and time spent performing a task but failed to meet predictions for interest and priority effects. The outcome of the validation effort as it relates to the STOM model, as well as eye tracking implications, are discussed.

The Effects of Task Selection Approaches to Emphasis Manipulation on Cognitive Load and Knowledge Transfer

Emphasis manipulation is a way to help learners by directing their attention to particular subcomponents of a learning task. This study investigated the effects of different approaches to emphasis manipulation on knowledge transfer and cognitive load. This was done by examining the impact of three task selection strategies: system-controlled, learner-controlled, and shared-controlled. Forty-five students (n = 45) in the first or second year of high school were randomly assigned to three groups and each group used a different type of task selection to manipulate emphasis in a complex learning context. The system-controlled group carried out learning tasks that were identified as essential by the system. The learner-controlled group selected and carried out learning tasks they needed to learn. The shared-controlled group chose and carried out learning tasks that they wanted to learn from a list of suggested learning tasks. The tasks had four learning phases: pre-test, training, mental-effort rating, and transfer test. After participants completed the training, their cognitive load was measured. One week after the training, a transfer test was conducted to measure the constituent skill acquisition. The findings revealed that the system-controlled task selection strategy was the most effective in optimizing cognitive load and enhancing knowledge transfer. In addition, learners benefited from personalized guidance on learning task selection based on their expertise. Given that the shared-controlled task selection method was more effective thank the learner-controlled task selection, this study’s results indicate that learners should be provided with information about how to select learning tasks when they are allowed to do so.

Selecting Mathematical Tasks for Assessing Student’s Understanding: Pre-Service Teachers’ Sensitivity to and Adaptive Use of Diagnostic Task Potential in Simulated Diagnostic One-To-One Interviews

Teachers’ diagnostic competences are regarded as highly important for classroom assessment and teacher decision making. Prior conceptualizations of diagnostic competences as judgement accuracy have been extended to include a wider understanding of what constitutes a diagnosis; novel models of teachers’ diagnostic competences explicitly include the diagnostic process as the core of diagnosing. In this context, domain-general and mathematics-specific research emphasizes the importance of tasks used to elicit student cognition. However, the role of (mathematical) tasks in diagnostic processes has not yet attracted much systematic empirical research interest. In particular, it is currently unclear whether teachers consider diagnostic task potential when selecting tasks for diagnostic interviews and how this relationship is shaped by their professional knowledge. This study focuses on pre-service mathematics teachers’ selection of tasks during one-to-one diagnostic interviews in live simulations. Each participant worked on two 30 mins interviews in the role of a teacher, diagnosing a student’s mathematical understanding of decimal fractions. The participants’ professional knowledge was measured afterward. Trained assistants played simulated students, who portrayed one of four student case profiles, each having different mathematical (mis-)conceptions of decimal fractions. For the interview, participants could select tasks from a set of 45 tasks with different diagnostic task potentials. Two aspects of task selection during the diagnostic processes were analyzed: participants’ sensitivity to the diagnostic potential, which was reflected in higher odds for selecting tasks with high potential than tasks with low potential, and the adaptive use of diagnostic task potential, which was reflected in task selection influenced by a task’s diagnostic potential in combination with previously collected information about the student’s understanding. The results show that participants vary in their sensitivity to diagnostic task potential, but not in their adaptive use. Moreover, participants’ content knowledge had a significant effect on their sensitivity. However, the effects of pedagogical content and pedagogical knowledge did not reach significance. The results highlight that pre-service teachers require further support to effectively attend to diagnostic task potential. Simulations were used for assessment purposes in this study, and they appear promising for this purpose because they allow for the creation of authentic yet controlled situations.