Robust Scheduling for Large Projects

1993 ◽  
Author(s):  
Valdis Berzins ◽  
Salah Badr
Keyword(s):  
2015 ◽  
Vol 135 (6) ◽  
pp. 713-720
Author(s):  
Wan-Ling Li ◽  
Tomohiro Murata ◽  
Muhammad Hafidz Fazli bin Md Fauadi

Author(s):  
Rongpeng Liu ◽  
Yunhe Hou ◽  
Yujia Li ◽  
Shunbo Lei ◽  
Wei Wei ◽  
...  
Keyword(s):  

2021 ◽  
Vol 13 (13) ◽  
pp. 7119
Author(s):  
Abbas Rabiee ◽  
Ali Abdali ◽  
Seyed Masoud Mohseni-Bonab ◽  
Mohsen Hazrati

In this paper, a robust scheduling model is proposed for combined heat and power (CHP)-based microgrids using information gap decision theory (IGDT). The microgrid under study consists of conventional power generation as well as boiler units, fuel cells, CHPs, wind turbines, solar PVs, heat storage units, and battery energy storage systems (BESS) as the set of distributed energy resources (DERs). Additionally, a demand response program (DRP) model is considered which has a successful performance in the microgrid hourly scheduling. One of the goals of CHP-based microgrid scheduling is to provide both thermal and electrical energy demands of the consumers. Additionally, the other objective is to benefit from the revenues obtained by selling the surplus electricity to the main grid during the high energy price intervals or purchasing it from the grid when the price of electricity is low at the electric market. Hence, in this paper, a robust scheduling approach is developed with the aim of maximizing the total profit of different energy suppliers in the entire scheduling horizon. The employed IGDT technique aims to handle the impact of uncertainties in the power output of wind and solar PV units on the overall profit.


2020 ◽  
Vol 53 (1) ◽  
pp. 206-207
Author(s):  
Sajay Velmurugan ◽  
Varghese Kurian ◽  
Prasanna Mohandoss ◽  
Shankar Narasimhan ◽  
Sridharakumar Narasimhan

2017 ◽  
Vol 49 (2) ◽  
pp. 603-628 ◽  
Author(s):  
Ramtin Pedarsani ◽  
Jean Walrand ◽  
Yuan Zhong

Abstract Modern processing networks often consist of heterogeneous servers with widely varying capabilities, and process job flows with complex structure and requirements. A major challenge in designing efficient scheduling policies in these networks is the lack of reliable estimates of system parameters, and an attractive approach for addressing this challenge is to design robust policies, i.e. policies that do not use system parameters such as arrival and/or service rates for making scheduling decisions. In this paper we propose a general framework for the design of robust policies. The main technical novelty is the use of a stochastic gradient projection method that reacts to queue-length changes in order to find a balanced allocation of service resources to incoming tasks. We illustrate our approach on two broad classes of processing systems, namely the flexible fork-join networks and the flexible queueing networks, and prove the rate stability of our proposed policies for these networks under nonrestrictive assumptions.


Author(s):  
Andrea Maria Zanchettin

AbstractMotivated by the increasing demand of mass customisation in production systems, this paper proposes a robust and adaptive scheduling and dispatching method for high-mix human-robot collaborative manufacturing facilities. Scheduling and dispatching rules are derived to optimally track the desired production within the mix, while handling uncertainty in job processing times. The sequencing policy is dynamically adjusted by online forecasting the throughput of the facility as a function of the scheduling and dispatching rules. Numerical verification experiments confirm the possibility to accurately track highly variable production requests, despite the uncertainty of the system.


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