JiTS: Just-in-Time Scheduling for Real-Time Sensor Data Dissemination

Author(s):  
Ke Liu ◽  
N. Abu-Ghazaleh ◽  
Kyoung-Don Kang
2021 ◽  
Author(s):  
Masoud Kamalahmadi ◽  
Qiuping Yu ◽  
Yong-Pin Zhou

Just-in-time scheduling has become ubiquitous in the service industries. Although effective in reducing staffing level, hence labor cost, the potential impact of just-in-time scheduling on workers’ productivity and the firm’s revenue is not well understood. Using a data set of 1,444,044 transactions from 25 stores of a full-service casual dining restaurant chain in the United States, we study how just-in-time scheduling impacts worker productivity. We consider two types of just-in-time schedules: (1) short-notice schedules that are assigned to servers shortly before the day of service (mostly two days in our data) and (2) real-time schedules that are assigned to servers on the day of service. We show that short-notice schedules do not harm server productivity overall, but real-time schedules do by 4.4%. Our analysis indicates this may be because servers reduce their up-selling and cross-selling efforts when working on real-time schedules. We then propose an analytical scheduling model that accounts for both the value of staffing flexibility created through just-in-time scheduling and its impact on server productivity to inform the firm how to use just-in-time scheduling to improve profitability. Through a case study, we demonstrate that with the 4.4% productivity loss during the real-time schedules, the managers should shift from the heavy use of real-time scheduling toward scheduling more servers with longer advance notice. Such a shift not only provides more predictable work schedules for the workers but can also improve restaurants’ expected profit by up to 1%, a significant number for the low-margin restaurant industry. This paper was accepted by Vishal Gaur, operations management.


2014 ◽  
Vol 9 (1) ◽  
pp. 54-64 ◽  
Author(s):  
Gyan Bahadur Thapa

Informed logistics is one of the quite recent and challenging research issues in the domain of supply chain production. The real-time information is taken as one of the productivity factor in all sort of business operations. In this paper, we present a very basic and brief literature of informed logistics together with the recent developments in this area. Observing information flows among logistics functions in production processes, we explain some information technology enablers of informed logistics. We propose an ABSO model for better sharing and operation of information, and finally we indicate the applications of informed logistics in real-time scheduling. DOI: http://dx.doi.org/10.3126/jie.v9i1.10670Journal of the Institute of Engineering, Vol. 9, No. 1, pp. 54–64


2021 ◽  
Author(s):  
Antoine Bertout ◽  
Joël Goossens ◽  
Emmanuel Grolleau ◽  
Roy Jamil ◽  
Xavier Poczekajlo

2021 ◽  
Vol 13 (6) ◽  
pp. 3400
Author(s):  
Jia Ning ◽  
Sipeng Hao ◽  
Aidong Zeng ◽  
Bin Chen ◽  
Yi Tang

The high penetration of renewable energy brings great challenges to power system operation and scheduling. In this paper, a multi-timescale coordinated method for source-grid-load is proposed. First, the multi-timescale characteristics of wind forecasting power and demand response (DR) resources are described, and the coordinated framework of source-grid-load is presented under multi-timescale. Next, economic scheduling models of source-grid-load based on multi-timescale DR under network constraints are established in the process of day-ahead scheduling, intraday scheduling, and real-time scheduling. The loads are classified into three types in terms of different timescale. The security constraints of grid side and time-varying DR potential are considered. Three-stage stochastic programming is employed to schedule resources of source side and load side in day-ahead, intraday, and real-time markets. The simulations are performed in a modified Institute of Electrical and Electronics Engineers (IEEE) 24-node system, which shows a notable reduction in total cost of source-grid-load scheduling and an increase in wind accommodation, and their results are proposed and discussed against under merely two timescales, which demonstrates the superiority of the proposed multi-timescale models in terms of cost and demand response quantity reduction.


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