802. Adapting a DFGSP Algorithm to Maximize Knowledge Worker Satisfaction
Invited abstract in session WD-34: Applications of Knowledge Work Technology, stream Advancements of OR-analytics in statistics, machine learning and data science.
Wednesday, 14:30-16:00Room: Michael Sadler LG10
Authors (first author is the speaker)
| 1. | Januj Juneja
|
| San Diego State University | |
| 2. | A. D. Amar
|
| Management Department, Seton Hall University |
Abstract
We propose a scheduling algorithm for assigning tasks to knowledge workers based on the distributed flowshop group scheduling problem (DFGSP). We assume that the tasks are grouped into families and are scheduled across different locations for completion based on the sub-activities which are done to facilitate proper implementation of the scheduling algorithm. The objective function is constructed to provide total worker satisfaction comprising three measures - financial, work perception, and output. The lattermost measure incorporates skills and abilities through five parameters linked to (1) ratio of capital invested in intangibles to traditional assets (δt), (2) net new-knowledge-added (φt), (3) investments in intangibles per revenue dollar (σt), (4) rate of generating organization capital (μt), and (5) work completion rate (νt). The former two measures incorporate other important aspects of worker satisfaction including how approachable is the supervisor, characteristics of the assigned task, worker motivation, workplace autonomy, performance rewards, worker training and the knowhow, pressure to produce, task importance and the availability of tools needed to be efficient at work. We demonstrate the usefulness of our algorithm through the implementation of simulation experiments and conclude with some managerial implications of our research along with directions for future work in this area.
Keywords
- Scheduling
- Algorithms
- Knowledge Engineering and Management
Status: accepted
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