scholarly journals Rolling-Horizon Optimization As a Speed-Up Method - Assessment Using the Electricity System Model JMM

2020 ◽  
Author(s):  
Thomas Kallabis
Keyword(s):  
System Model ◽  
Rolling Horizon ◽  
Electricity System ◽  
Speed Up ◽  
Rolling Horizon Optimization

scholarly journals A rolling horizon optimization framework for the simultaneous energy supply and demand planning in microgrids

2015 ◽  
Vol 155 ◽  
pp. 485-501 ◽  
Author(s):  
Javier Silvente ◽  
Georgios M. Kopanos ◽  
Efstratios N. Pistikopoulos ◽  
Antonio Espuña
Keyword(s):  
Energy Supply ◽  
Supply And Demand ◽  
Rolling Horizon ◽  
Optimization Framework ◽  
Demand Planning ◽  
Rolling Horizon Optimization

scholarly journals Integration of Production Planning and Scheduling Based on RTN Representation under Uncertainties

Algorithms ◽  
2019 ◽  
Vol 12 (6) ◽  
pp. 120
Author(s):  
Tao Zhang ◽  
Yue Wang ◽  
Xin Jin ◽  
Shan Lu
Keyword(s):  
Production Planning ◽  
Integrated Model ◽  
Rolling Horizon ◽  
Optimization Strategy ◽  
Planning And Scheduling ◽  
Production Planning And Scheduling ◽  
Proposed Model ◽  
Simulation Results ◽  
Period Decrease ◽  
Rolling Horizon Optimization

Production planning and scheduling are important bases for production decisions. Concerning the traditional modeling of production planning and scheduling based on Resource-Task Network (RTN) representation, uncertain factors such as utilities are rarely considered as constraints. For the production planning and scheduling problem based on RTN representation in an uncertain environment, this paper formulates the multi-period bi-level integrated model of planning and scheduling, and introduces the uncertainties of demand and utility in planning and scheduling layers respectively. Rolling horizon optimization strategy is utilized to solve the bi-level integrated model iteratively. The simulation results show that the proposed model and algorithm are feasible and effective, can calculate the consumption of utility in every period, decrease the effects of uncertain factors on optimization results, more accurately describe the uncertain factors, and reflect the actual production process.

scholarly journals Centralized Ambulance Diversion Policy Using Rolling-Horizon Optimization Framework to Minimize Patient Tardiness

Healthcare ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 266
Author(s):  
Sohye Baek ◽  
Young Hoon Lee ◽  
Seong Hyeon Park
Keyword(s):  
Programming Model ◽  
Mixed Integer ◽  
Time Interval ◽  
Rolling Horizon ◽  
Decentralized Optimization ◽  
Hospital System ◽  
Ambulance Diversion ◽  
Optimization Framework ◽  
The Impact ◽  
Rolling Horizon Optimization

Ambulance diversion (AD) is a common method for reducing crowdedness of emergency departments by diverting ambulance-transported patients to a neighboring hospital. In a multi-hospital system, the AD of one hospital increases the neighboring hospital’s congestion. This should be carefully considered for minimizing patients’ tardiness in the entire multi-hospital system. Therefore, this paper proposes a centralized AD policy based on a rolling-horizon optimization framework. It is an iterative methodology for coping with uncertainty, which first solves the centralized optimization model formulated as a mixed-integer linear programming model at each discretized time, and then moves forward for the time interval reflecting the realized uncertainty. Furthermore, the decentralized optimization, decentralized priority, and No-AD models are presented for practical application, which can also show the impact of using the following three factors: centralization, mathematical model, and AD strategy. The numerical experiments conducted based on the historical data of Seoul, South Korea, for 2017, show that the centralized AD policy outperforms the other three policies by 30%, 37%, and 44%, respectively, and that all three factors contribute to reducing patients’ tardiness. The proposed policy yields an efficient centralized AD management strategy, which can improve the local healthcare system with active coordination between hospitals.

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