任务(项目管理)
计算机科学
吞吐量
产品(数学)
步伐
生产力
相似性(几何)
立方体(代数)
模拟
人工智能
数学
图像(数学)
无线
地理
管理
经济
宏观经济学
几何学
组合数学
电信
大地测量学
作者
Nicole DeHoratius,Özgür Gürerk,Dorothée Honhon,Kyle Hyndman
标识
DOI:10.1177/10591478241275066
摘要
Retail store employees are increasingly being asked to pick orders from inventory. These tasks are performed under intense conditions and are often made more difficult because of high product variety and high degrees of product similarity. We conduct a real-effort task in a virtual environment where subjects must sort cubes into bins. We study task complexity by varying the degree of similarity between the cubes and task intensity by varying the arrival rate of the cube. We focus on four performance metrics: Throughput (number of cubes sorted per minute), Accuracy (percentage of correctly sorted cubes), Productivity (number of correctly sorted cubes per minute), and Error Rate (number of incorrectly sorted cubes per minute). Reducing task complexity increases productivity by as much as 38.2%, and the error rate falls by as much as 93.6%. It also leads to more efficient movements, according to a detailed analysis of our subjects' motion. Increasing task intensity improves throughput but decreases accuracy slightly while varying task intensity improves performance via faster learning. We also observe that subjects tend to cut corners when the task is more complex or more intense.
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