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Task complexity is one of the significant factors that influences and is used for forecasting employee performance and determining labor cost. However, the complexity level of tasks is unstructured, dynamic and complicated to perform. This paper develops a new method for evaluating the complexity level of tasks in the production process to support production managers to control their manufacturing systems in terms of flexibility, reliability to production planning and labor cost.

The complexity level of tasks will be analyzed based on the structuralist concept. Using the structure of task, the factors that significantly affect the task complexity in an assembly line will be defined, and the complexity level of the task will be evaluated by measuring the number of task components. Using the proportional 2-tuples linguistic values, the difference between the complexity levels of tasks can be compared and described clearly.

Based on the structure of the task, three contributory factors including input factors, process-operation factors and output factors that significantly affect the task complexity in an assembly line are identified in the present study. The complexity level of the task is quantified through analyzing the details of the three factors according to two criteria and six sub-criteria within the textile case study.

The proposed approach provides a new insight about the factors that have an effect on the complexity of tasks in production and remedies some of limitations of previous methods. The combination of experts' experience and scientific knowledge will improve the accuracy in determining the complexity level of tasks.

Kapok was well-known for its oleophilic properties, but its mechanical properties and morphology impeded it from forming suitable absorbent materials. This study aims to demonstrate the process of creating an oil-absorbent web from a blend of treated kapok and polypropylene fibers.

Kapok fibers were separated from dried fruits, then the wax was removed with an HCl solution at different concentrations. The morphological and structural changes of these fibers were investigated using scanning electron microscopy images. The blending ratios of kapok and polypropylene fibers were 60/40, 70/30 and 80/20, respectively. The fiber blends were fed to a laboratory carding machine to form a web and then consolidated using the heat press technique. The absorption behavior of the formed web was evaluated regarding oil absorption capacity and oil retention capacity according to ASTM 726.

The results showed that the HCl concentration of 1.0% (wt%) gave the highest wax removal efficiency without damaging the kapok fibers. This study found that oil absorbency is influenced by the fiber blending ratio, web tensile strength and elongation, porosity, oil type and environmental conditions. The oil-absorbency of the web can be re-used for at least 20 cycles.

This study only looked at three types of oils: diesel, kerosene and vegetable oils.

When the problem of oil spills in rivers and seas is growing and causing serious environmental and economic consequences, using physical methods to recover oil spills is the most effective solution.

This research adds to the possibility of using kapok fiber in the form of a web of non-woven fabric for practical purposes.

This paper aims to develop a decision support system for predicting the knitting production’s efficiency based on the input parameters of an order. This tool supports the operations managers to make reliable decisions of estimated delivery time, which will result in reducing waste arising from late delivery, overtime and increased labor.

The decision tree method with a set of logical IF-THEN rules is used to determine the knitting production’s efficiency. Each path of the decision tree represents a rule of the following form: “IF <Condition> THEN <Efficiency label>.” Starting with identifying and categorizing input specifications, the model is then applied to the observed data to regenerate the results of efficiency into classification instances.

The production’s efficiency is the result of the interaction between input specifications such as yarn’s component, knitting fabric specifications and machine speed. The rule base is generated through a decision tree built to classify the efficiency into five levels, including very low, low, medium, high and very high. Based on this, production managers can determine the delivery time and schedule the manufacturing planning more accurately. In this research, the correct classification instances, which is simply a ratio of the correctly predicted observations to the total ones, reach 80.17%.

This research proposes a new methodology for estimating the efficiency of weft knitting production based on a decision tree method with an application of real data. This model supports the decision-making process of the estimated delivery time.