Multi-objective optimization based algorithms for solving mixed integer linear minimum multiplicative programs

2021 ◽  
Vol 128 ◽  
pp. 105178
Author(s):  
Vahid Mahmoodian ◽  
Hadi Charkhgard ◽  
Yu Zhang
Keyword(s):  
Mixed Integer ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Multiplicative Programs

Multi-objective optimization of a recuperative gas turbine cycle using non-dominated sorting genetic algorithm

2011 ◽  
Vol 225 (8) ◽  
pp. 1041-1051 ◽  
Author(s):  
H Sayyaadi ◽  
H R Aminian
Keyword(s):  
Genetic Algorithm ◽  
Gas Turbine ◽  
Heat Exchangers ◽  
Mixed Integer ◽  
Design Stage ◽  
Tube Length ◽  
Pareto Optimal ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Gas Turbine Cycle

A regenerative gas turbine cycle with two particular tubular recuperative heat exchangers in parallel is considered for multi-objective optimization. It is assumed that tubular recuperative heat exchangers and its corresponding gas cycle are in design stage simultaneously. Three objective functions including the purchased equipment cost of recuperators, the unit cost rate of the generated power, and the exergetic efficiency of the gas cycle are considered simultaneously. Geometric specifications of the recuperator including tube length, tube outside/inside diameters, tube pitch, inside shell diameter, outer and inner tube limits of the tube bundle and the total number of disc and doughnut baffles, and main operating parameters of the gas cycle including the compressor pressure ratio, exhaust temperature of the combustion chamber and the air mass flowrate are considered as decision variables. Combination of these objectives anddecision variables with suitable engineering and physical constraints (including NO x and CO emission limitations) comprises a set of mixed integer non-linear problems. Optimization programming in MATLAB is performed using one of the most powerful and robust multi-objective optimization algorithms, namely non-dominated sorting genetic algorithm. This approach is applied to find a set of Pareto optimal solutions. Pareto optimal frontier is obtained, and a final optimal solution is selected in a decision-making process.

scholarly journals Optimal Operation of Interprovincial Hydropower System Including Xiluodu and Local Plants in Multiple Recipient Regions

Energies ◽  
2019 ◽  
Vol 12 (1) ◽  
pp. 144 ◽  
Author(s):  
Jianjian Shen ◽  
Xiufei Zhang ◽  
Jian Wang ◽  
Rui Cao ◽  
Sen Wang ◽  
...  
Keyword(s):  
Power Grid ◽  
Optimal Operation ◽  
Dry Season ◽  
Power Grids ◽  
Mixed Integer ◽  
Multi Objective Optimization ◽  
Hydropower Generation ◽  
Multi Objective ◽  
Hydropower System ◽  
Local Plants

This paper focuses on the monthly operations of an interprovincial hydropower system (IHS) connected by ultrahigh voltage direct current lines. The IHS consists of the Xiluodu Hydropower Project, which ranks second in China, and local plants in multiple recipient regions. It simultaneously provides electricity for Zhejiang and Guangdong provinces and thus meets their complex operation requirements. This paper develops a multi-objective optimization model of maximizing the minimum of total hydropower generation for each provincial power grid while considering network security constraints, electricity contracts, and plant constraints. The purpose is to enhance the minimum power in dry season by using the differences in hydrology and regulating storage of multiple rivers. The TOPSIS method is utilized to handle this multi-objective optimization, where the complex minimax objective function is transformed into a group of easily solved linear formulations. Nonlinearities of the hydropower system are approximatively described as polynomial formulations. The model was used to solve the problem using mixed integer nonlinear programming that is based on the branch-and-bound technique. The proposed method was applied to the monthly generation scheduling of the IHS. Compared to the conventional method, both the total electricity for Guangdong Power Grid and Zhejiang Power Grid during dry season increased by 6% and 4%, respectively. The minimum monthly power also showed a significant increase of 40% and 31%. It was demonstrated that the hydrological differences between Xiluodu Plant and local hydropower plants in receiving power grids can be fully used to improve monthly hydropower generation.

On Solving the Multi-Objective Software Package Upgradability Problem

2018 ◽  
Vol 9 (2) ◽  
pp. 18-38
Author(s):  
Noureddine Aribi ◽  
Yahia Lebbah
Keyword(s):  
Open Source Software ◽  
Software Package ◽  
Optimization Problem ◽  
Mixed Integer ◽  
Software Systems ◽  
Objective Functions ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Before And After ◽  
The Stability

Free and open source software (FOSS) distributions are increasingly based on the abstraction of packages to manage and accommodate new features before and after the deployment stage. However, due to inter-package dependencies, package upgrade entails challenging shortcomings of deployment and management of complex software systems, inhibiting their ability to cope with frequent upgrade failures. Moreover, the upgrade process may be achieved according to some criteria (maximize the stability, minimize outdated packages, etc.). This problem is actually a multi-objective optimization problem. Throughout the article, the authors propose a Leximax approach based on mixed integer linear programming (MILP) to tackle the upgradability problem, while ensuring efficiency and fairness requirements between the objective functions. Experiments performed on real-world instances, from the MANCOOSI project, show that the authors' approach efficiently finds solutions of consistently high quality.

scholarly journals A Decision-Making Tool for a Complex Post Enrollment-Based Course Timetabling

2020 ◽  
Vol 55 (6) ◽  
Author(s):  
Ngo Tung Son
Keyword(s):  
Combinatorial Problems ◽  
Mixed Integer ◽  
Objective Functions ◽  
Multi Objective Optimization ◽  
Spring Semester ◽  
Multi Objective ◽  
Training Costs ◽  
Proposed Model ◽  
Decision Making Tool ◽  
Single Objective

The article describes a new method to construct an enrollment-based course timetable in universities, based on a multi-objective optimization model. The model used mixed-integer and binary variables towards creating a schedule. It satisfies students' preferences for study time, with the number of students in the same class being optimal for training costs while ensuring timetabling business constraints. We use a combination of compromise programming and linear scalarizing to transform many objective functions into single-objective optimization. A scheme of the Genetic Algorithm was developed to solve the proposed model. The proposed method allows approaching several types of multi-objective combinatorial problems. The algorithm was tested by scheduling a study schedule for 3,000 students in the spring semester of 2020 at FPT University, Hanoi, Vietnam. The obtained results show the average students' preference level of 69%. More than 30% of students have a satisfaction level of more than 80% of the timetable after two hours of execution time.

Multi-objective TMA management optimization using the point merge system

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ramazan Kursat Cecen
Keyword(s):  
Mathematical Model ◽  
Mixed Integer ◽  
Optimization Approach ◽  
Multi Objective Optimization ◽  
Content Type ◽  
Multi Objective ◽  
First Come First Serve ◽  
Management Optimization ◽  
The Mathematical Model ◽  
Single Objective

Purpose The purpose of this study is to provide conflict-free operations in terminal manoeuvre areas (TMA) using the point merge system (PMS), airspeed reduction (ASR) and ground holding (GH) techniques. The objective is to minimize both total aircraft delay (TD) and the total number of the conflict resolution manoeuvres (CRM). Design/methodology/approach The mixed integer linear programming (MILP) is used for both single and multi-objective optimization approaches to solve aircraft sequencing and scheduling problem (ASSP). Compromise programming and ε-constraint methods were included in the methodology. The results of the single objective optimization approach results were compared with baseline results, which were obtained using the first come first serve approach, in terms of the total number of the CRM, TD, the number of aircraft using PMS manoeuvres, ASR manoeuvres, GH manoeuvres, departure time updates and on-time performance. Findings The proposed single-objective optimization approach reduced both the CRM and TD considerably. For the traffic flow rates of 15, 20 and 25 aircraft, the improvement of CRM was 53.08%, 41.12% and 32.6%, the enhancement of TD was 54.2%, 48.8% and 31.06% and the average number of Pareto-optimal solutions were 1.26, 2.22 and 3.87, respectively. The multi-objective optimization approach also exposed the relationship between the TD and the total number of CRM. Practical implications The proposed mathematical model can be implemented considering the objectives of air traffic controllers (ATCOs) and airlines operators. Also, the mathematical model is able to create conflict-free TMA operations and, therefore, it brings an opportunity for ATCOs to reduce frequency occupancy time. Originality/value The mathematical model presents the total number of CRM as an objective function in the ASSP using the MILP approach. The mathematical model integrates ATCOs’ and airline operators’ perspective together with new objective functions.

scholarly journals Multi-Objective Optimization for Order Assignment in Food Delivery Industry with Human Factor Considerations

2020 ◽  
Vol 12 (19) ◽  
pp. 7955
Author(s):  
Zhilan Lou ◽  
Wanchen Jie ◽  
Shuzhu Zhang
Keyword(s):  
Human Factor ◽  
Food Delivery ◽  
Mixed Integer ◽  
Operational Cost ◽  
Multi Objective Optimization ◽  
Workload Balancing ◽  
Multi Objective ◽  
Delivery Performance ◽  
Proposed Model ◽  
Order Assignment

The order assignment in the food delivery industry is of high complexity due to the uneven distribution of order requirements and the large-scale optimization of workforce resources. The delivery performance of employees varies in different conditions, which further exacerbates the difficulty of order assignment optimization. In this research, a non-linear multi-objective optimization model is proposed with human factor considerations in terms of both deteriorating effect and learning effect, in order to acquire the optimal solutions in practice. The objectives comprised the minimization of the operational cost in multiple periods and the workload balancing among multiple employees. The proposed model is further transformed to a standardized mixed-integer linear model by the exploitation of linearization procedures and normalization operations. Numerical experiments show that the proposed model can be easily solved using commercial optimization softwares. The results indicate that the variance of employee performance can affect the entire delivery performance, and significant improvement of workload balancing can be achieved at the price of slight increase of the operational cost. The proposed model can facilitate the decision-making process of order assignment and workforce scheduling in the food delivery industry. Moreover, it can provide managerial insights for other labor-intensive service-oriented industries.

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