scholarly journals Generation Units Maintenance in Combined Heat and Power Integrated Systems Using the Mixed Integer Quadratic Programming Approach

Energies ◽  
2020 ◽  
Vol 13 (11) ◽  
pp. 2840 ◽  
Author(s):  
Omid Sadeghian ◽  
Arash Moradzadeh ◽  
Behnam Mohammadi-Ivatloo ◽  
Mehdi Abapour ◽  
Fausto Pedro Garcia Marquez
Keyword(s):  
Numerical Simulation ◽  
Demand Response ◽  
Combined Heat And Power ◽  
Maintenance Scheduling ◽  
Mixed Integer ◽  
Short Term ◽  
Generation Scheduling ◽  
Integer Quadratic Programming ◽  
Mixed Integer Quadratic Programming

Yearly generation maintenance scheduling (GMS) of generation units is important in each system such as combined heat and power (CHP)-based systems to decrease sudden failures and premature degradation of units. Imposing repair costs and reliability deterioration of system are the consequences of ignoring the GMS program. In this regard, this research accomplishes GMS inside CHP-based systems in order to determine the optimal intervals for predetermined maintenance required duration of CHPs and other units. In this paper, cost minimization is targeted, and violation of units’ technical constraints like feasible operation region of CHPs and power/heat demand balances are avoided by considering related constraints. Demand-response-based short-term generation scheduling is accomplished in this paper considering the maintenance intervals obtained in the long-term plan. Numerical simulation is performed and discussed in detail to evaluate the application of the suggested mixed-integer quadratic programming model that implemented in the General Algebraic Modeling System software package for optimization. Numerical simulation is performed to justify the model effectiveness. The results reveal that long-term maintenance scheduling considerably impacts short-term generation scheduling and total operation cost. Additionally, it is found that the demand response is effective from the cost perspective and changes the generation schedule.

Metaheuristic Search with Inequalities and Target Objectives for Mixed Binary Optimization – Part II

2010 ◽  
Vol 1 (2) ◽  
pp. 1-17 ◽  
Author(s):  
Fred Glover ◽  
Saïd Hanafi
Keyword(s):  
Linear Programming ◽  
Programming Models ◽  
Mixed Integer ◽  
Embedded Memory ◽  
Short Term ◽  
Solution Strategies ◽  
Diversification Strategies ◽  
Metaheuristic Search ◽  
New Inequalities

Recent metaheuristics for mixed integer programming have included proposals for introducing inequalities and target objectives to guide this search. These guidance approaches are useful in intensification and diversification strategies related to fixing subsets of variables at particular values. The authors’ preceding Part I study demonstrated how to improve such approaches by new inequalities that dominate those previously proposed. In Part II, the authors review the fundamental concepts underlying weighted pseudo cuts for generating guiding inequalities, including the use of target objective strategies. Building on these foundations, this paper develops a more advanced approach for generating the target objective based on exploiting the mutually reinforcing notions of reaction and resistance. The authors demonstrate how to produce new inequalities by “mining” reference sets of elite solutions to extract characteristics these solutions exhibit in common. Additionally, a model embedded memory is integrated to provide a range of recency and frequency memory structures for achieving goals associated with short term and long term solution strategies. Finally, supplementary linear programming models that exploit the new inequalities for intensification and diversification are proposed.

scholarly journals Combinatorial Optimization of Service Order and Overtaking for Demand-Oriented Timetabling in a Single Railway Line

2018 ◽  
Vol 2018 ◽  
pp. 1-21 ◽  
Author(s):  
Dewei Li ◽  
Shishun Ding ◽  
Yizhen Wang
Keyword(s):  
Travel Time ◽  
High Speed ◽  
Cost Benefit ◽  
Theory And Practice ◽  
Mixed Integer ◽  
High Speed Rail ◽  
Railway Line ◽  
Integer Quadratic Programming ◽  
Total Travel Time ◽  
Mixed Integer Quadratic Programming

Train timetabling is crucial for passenger railway operation. Demand-oriented train timetable optimization by minimizing travel time plays an important role in both theory and practice. Most of the current researches of demand-oriented timetable models assume an idealized situation in which the service order is fixed and in which zero overtaking exists between trains. In order to extend the literature, this paper discusses the combinatorial effect of service order and overtaking by developing four mixed-integer quadratic programming timetabling models with different service order as well as overtaking conditions. With the objective of minimizing passengers’ waiting time and in-vehicle time, the models take five aspects as constraints, namely dwell time, running time, safety interval, overtaking, and capacity. All four models are solved by ILOG CPLEX; and the results, which are based on Shanghai-Hangzhou intercity high-speed rail data, show that either allowing overtaking or changing service order can effectively optimize the quality of timetable with respect to reducing the total passengers’ travel time. Although optimizing train overtaking and service order simultaneously can optimize the timetable more significantly, compared to overtaking, allowing the change of service order can help passengers save total travel time without extending the train travel time. Moreover, considering the computation effort, satisfying both of the conditions in the meantime, when optimizing timetable has not got a good cost benefit.

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