A comparison of the MOGA and NSGA-II optimization techniques to reduce the cost of a biomass supply network

2021 ◽  
Author(s):  
Adarsh Kumar Arya
Keyword(s):  
Optimization Techniques ◽  
Supply Network ◽  
Nsga Ii ◽  
Biomass Supply ◽  
The Cost

Fuzzy multi-objective location and routing problem

2020 ◽  
Vol 39 (3) ◽  
pp. 3259-3273
Author(s):  
Nasser Shahsavari-Pour ◽  
Najmeh Bahram-Pour ◽  
Mojde Kazemi
Keyword(s):  
Firefly Algorithm ◽  
High Reliability ◽  
Research Area ◽  
Nsga Ii ◽  
Location Routing Problem ◽  
Routing Problem ◽  
Location Allocation ◽  
Multi Objective ◽  
Location Routing ◽  
The Cost

The location-routing problem is a research area that simultaneously solves location-allocation and vehicle routing issues. It is critical to delivering emergency goods to customers with high reliability. In this paper, reliability in location and routing problems was considered as the probability of failure in depots, vehicles, and routs. The problem has two objectives, minimizing the cost and maximizing the reliability, the latter expressed by minimizing the expected cost of failure. First, a mathematical model of the problem was presented and due to its NP-hard nature, it was solved by a meta-heuristic approach using a NSGA-II algorithm and a discrete multi-objective firefly algorithm. The efficiency of these algorithms was studied through a complete set of examples and it was found that the multi-objective discrete firefly algorithm has a better Diversification Metric (DM) index; the Mean Ideal Distance (MID) and Spacing Metric (SM) indexes are only suitable for small to medium problems, losing their effectiveness for big problems.

scholarly journals Carbon Emission Reduction of Apparel Material Distribution Based on Multi-Objective Genetic Algorithm (NSGA-II)

2019 ◽  
Vol 11 (9) ◽  
pp. 2571
Author(s):  
Xujing Zhang ◽  
Lichuan Wang ◽  
Yan Chen
Keyword(s):  
Genetic Algorithm ◽  
Carbon Emissions ◽  
Optimal Solution ◽  
Material Distribution ◽  
Low Carbon ◽  
Nsga Ii ◽  
Management Objectives ◽  
Multi Objective Genetic Algorithm ◽  
Distribution Process ◽  
The Cost

Low-carbon production has become one of the top management objectives for every industry. In garment manufacturing, the material distribution process always generates high carbon emissions. In order to reduce carbon emissions and the number of operators to meet enterprises’ requirements to control the cost of production and protect the environment, the paths of material distribution were analyzed to find the optimal solution. In this paper, the model of material distribution to obtain minimum carbon emissions and vehicles (operators) was established to optimize the multi-target management in three different production lines (multi-line, U-shape two-line, and U-shape three-line), while the workstations were organized in three ways: in the order of processes, in the type of machines, and in the components of garment. The NSGA-II algorithm (non-dominated sorting genetic algorithm-II) was applied to obtain the results of this model. The feasibility of the model and algorithm was verified by the practice of men’s shirts manufacture. It could be found that material distribution of multi-line layout produced the least carbon emissions when the machines were arranged in the group of type.

scholarly journals Multi-Objective Optimization of Switched Reluctance Machine Design Using Jaya Algorithm (MO-Jaya)

Mathematics ◽  
2021 ◽  
Vol 9 (10) ◽  
pp. 1107
Author(s):  
Mohamed Afifi ◽  
Hegazy Rezk ◽  
Mohamed Ibrahim ◽  
Mohamed El-Nemr
Keyword(s):  
Optimization Techniques ◽  
Design Parameters ◽  
Jaya Algorithm ◽  
Switched Reluctance Machine ◽  
Nsga Ii ◽  
Switched Reluctance ◽  
Multi Objective ◽  
Reluctance Machine ◽  
Linear Relationships ◽  
Forward Equations

The switched reluctance machine (SRM) design is different from the design of most of other machines. SRM has many design parameters that have non-linear relationships with the performance indices (i.e., average torque, efficiency, and so forth). Hence, it is difficult to design SRM using straight forward equations with iterative methods, which is common for other machines. Optimization techniques are used to overcome this challenge by searching for the best variables values within the search area. In this paper, the optimization of SRM design is achieved using multi-objective Jaya algorithm (MO-Jaya). In the Jaya algorithm, solutions are moved closer to the best solution and away from the worst solution. Hence, a good intensification of the search process is achieved. Moreover, the randomly changed parameters achieve good search diversity. In this paper, it is suggested to also randomly change best and worst solutions. Hence, better diversity is achieved, as indicated from results. The optimization with the MO-Jaya algorithm was made for 8/6 and 6/4 SRM. Objectives used are the average torque, efficiency, and iron weight. The results of MO-Jaya are compared with the results of the non-dominated sorting genetic algorithm (NSGA-II) for the same conditions and constraints. The optimization program is made in Lua programming language and executed by FEMM4.2 software. The results show the success of the approach to achieve better objective values, a broad search, and to introduce a variety of optimal solutions.

scholarly journals A multivariable multiobjective predictive controller

2013 ◽  
Vol 23 (1) ◽  
pp. 35-45 ◽  
Author(s):  
Faten Ben Aicha ◽  
Faouzi Bouani ◽  
Mekki Ksouri
Keyword(s):  
Genetic Algorithm ◽  
Predictive Control ◽  
Mimo System ◽  
Weighting Factor ◽  
Nsga Ii ◽  
Weighted Sum Method ◽  
Prediction Horizon ◽  
Predictive Controller ◽  
Pareto Method ◽  
The Cost

Predictive control of MIMO processes is a challenging problem which requires the specification of a large number of tuning parameters (the prediction horizon, the control horizon and the cost weighting factor). In this context, the present paper compares two strategies to design a supervisor of the Multivariable Generalized Predictive Controller (MGPC), based on multiobjective optimization. Thus, the purpose of this work is the automatic adjustment of the MGPC synthesis by simultaneously minimizing a set of closed loop performances (the overshoot and the settling time for each output of the MIMO system). First, we adopt the Weighted Sum Method (WSM), which is an aggregative method combined with a Genetic Algorithm (GA) used to minimize a single criterion generated by the WSM. Second, we use the Non- Dominated Sorting Genetic Algorithm II (NSGA-II) as a Pareto method and we compare the results of both the methods. The performance of the two strategies in the adjustment of multivariable predictive control is illustrated by a simulation example. The simulation results confirm that a multiobjective, Pareto-based GA search yields a better performance than a single objective GA.

scholarly journals Mathematical modeling for optimizing the blood supply chain network

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Amir Rahimzadeh Dehaghani ◽  
Muhammad Nawaz ◽  
Rohullah Sultanie ◽  
Tawiah Kwatekwei Quartey-Papafio
Keyword(s):  
Supply Chain ◽  
Blood Supply ◽  
Supply Chain Network ◽  
Distribution Centers ◽  
Nsga Ii ◽  
Content Type ◽  
Supply Chain Networks ◽  
Multi Objective ◽  
Blood Supply Chain ◽  
The Cost

PurposeThis research studies a location-allocation problem considering the m/m/m/k queue model in the blood supply chain network. This supply chain includes three levels of suppliers or donors, main blood centers (laboratories for separation, storage and distribution centers) and demand centers (hospitals and private clinics). Moreover, the proposed model is a multi-objective model including minimizing the total cost of the blood supply chain (the cost of unmet demand and inventory spoilage, the cost of transport between collection centers and the main centers of blood), minimizing the waiting time of donors in blood donating mobile centers, and minimizing the establishment of mobile centers in potential places.Design/methodology/approachSince the problem is multi-objective and NP-Hard, the heuristic algorithm NSGA-II is proposed for Pareto solutions and then the estimation of the parameters of the algorithm is described using the design of experiments. According to the review of the previous research, there are a few pieces of research in the blood supply chain in the field of design queue models and there were few works that tried to use these concepts for designing the blood supply chain networks. Also, in former research, the uncertainty in the number of donors, and also the importance of blood donors has not been considered.FindingsA novel mathematical model guided by the theory of linear programming has been proposed that can help health-care administrators in optimizing the blood supply chain networks.Originality/valueBy building upon solid literature and theory, the current study proposes a novel model for improving the supply chain of blood.

Optimization of Multistage Machining Systems, Part 1: Mathematical Solution

1992 ◽  
Vol 114 (4) ◽  
pp. 524-531 ◽  
Author(s):  
J. S. Agapiou
Keyword(s):  
Optimization Problem ◽  
Cutting Parameters ◽  
Optimization Method ◽  
Idle Time ◽  
Optimization Techniques ◽  
Machining Parameters ◽  
Machining Time ◽  
Physical Constraints ◽  
Time Requirements ◽  
The Cost

The optimization problem for multistage machining systems has been investigated. Due to uneven time requirements at different stages in manufacturing, there could be idle times at various stations. It may be advantageous to reduce the values of machining parameters in order to reduce the cost at stations that require less machining time. However, optimization techniques available through the literature do not effectively utilize the idle time for the different stations generated during the balancing of the system. Proposed in this paper is an optimization method which utilizes the idle time to the full extent at all machining stations, with the intention of improving tool life and thus achieving cost reduction. The mathematical analysis considers the optimization of the production cost with an equality constraint of zero idle time for the stations with idle time. Physical constraints regarding the cutting parameters, force, power, surface finish, etc., as they arise in different operations, are also considered. The aforementioned problem has been theoretically analyzed and a computational algorithm developed. The advantages and effectiveness of the proposed approach are finally established through an example.

UAS Mission Path Planning System (MPPS) Using Hybrid-Game Coupled to Multi-Objective Optimiser

2009 ◽  
Author(s):  
DongSeop Lee ◽  
Jacques Periaux ◽  
Luis Felipe Gonzalez
Keyword(s):  
Path Planning ◽  
Computational Cost ◽  
Optimization Techniques ◽  
Planning System ◽  
Unmanned Aerial Systems ◽  
Three Dimension ◽  
Design Quality ◽  
Nsga Ii ◽  
Start Position ◽  
Multi Objective

This paper presents the application of advanced optimization techniques to Unmanned Aerial Systems (UAS) Mission Path Planning System (MPPS) using Multi-Objective Evolutionary Algorithms (MOEAs). Two types of multi-objective optimizers are compared; the MOEA Non-dominated Sorting Genetic Algorithms II (NSGA-II) and a Hybrid Game strategy are implemented to produce a set of optimal collision-free trajectories in three-dimensional environment. The resulting trajectories on a three-dimension terrain are collision-free and are represented by using Be´zier spline curves from start position to target and then target to start position or different position with altitude constraints. The efficiency of the two optimization methods is compared in terms of computational cost and design quality. Numerical results show the benefits of adding a Hybrid-Game strategy to a MOEA and for a MPPS.

Robust Design of Power Distribution Systems Using an Enhanced Multi-Objective Genetic Algorithm

2012 ◽  
pp. 179-200 ◽  
Author(s):  
Cristiane G. Taroco ◽  
Eduardo G. Carrano ◽  
Oriane M. Neto
Keyword(s):  
Power Distribution ◽  
Distribution Systems ◽  
Search Algorithm ◽  
Efficient Solutions ◽  
Optimization Techniques ◽  
Nsga Ii ◽  
Robust Solutions ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Reliability And Robustness

The growing importance of electric distribution systems justifies new investments in their expansion and evolution. It is well known in the literature that optimization techniques can provide better allocation of the financial resources available for such a task, reducing total installation costs and power losses. In this work, the NSGA-II algorithm is used for obtaining a set of efficient solutions with regard to three objective functions, that is cost, reliability, and robustness. Initially, a most likely load scenario is considered for simulation. Next, the performances of the solutions achieved by the NSGA-II are evaluated under different load scenarios, which are generated by means of Monte Carlo Simulations. A Multi-objective Sensitivity Analysis is performed for selecting the most robust solutions. Finally, those solutions are submitted to a local search algorithm to estimate a Pareto set composed of just robust solutions only.

Economy Based Resource Allocation in IaaS Cloud

2013 ◽  
Vol 3 (2) ◽  
pp. 1-11 ◽  
Author(s):  
Hemant Kumar Mehta ◽  
Eshan Gupta
Keyword(s):  
Resource Allocation ◽  
Open Source ◽  
Service Provider ◽  
Lower Cost ◽  
Optimization Techniques ◽  
Considerable Extent ◽  
Experimental Results ◽  
Computing Power ◽  
Power Storage ◽  
The Cost

Infrastructure as a Service (IaaS) offers hardware resources (computing power, storage and network) as a service to its customers. The customers order these resources in the form of a lease. Aim of any service provider is to make a leasing plan to maximize the number of accepted leases. Opennebula is popular open source toolkit for building IaaS cloud. Opennebula has its own lease manager and it can also be integrated with Haizea which is an open source lease manager. An economy based algorithm should focus on incentives of both the consumers and the providers. In this paper, an economy based leasing algorithm is developed and integrated with Haizea. This economy based algorithm takes care of incentives of both the parties i.e. customer and service provider. It uses the concept of optimization techniques to optimize the costs. The incentive for customers is lower cost of execution of its lease on the capable node as compare to existing non-economy based algorithms. If an appropriate resource is not found, then the algorithm uses negotiation on budget and resource demand; that increases the number of accepted lease. Thus, incentive for providers is an increase in profit as the amount of accepted leases increase. Experimental results show that the proposed economy based leasing algorithm reduces the cost of execution of the consumer’s lease and increases the profit of the provider to a considerable extent.

scholarly journals Design of Power Supply Package for Electricity Sales Companies Considering User Side Energy Storage Configuration

Energies ◽  
2019 ◽  
Vol 12 (17) ◽  
pp. 3219 ◽  
Author(s):  
Mu ◽  
Gao ◽  
Yang ◽  
Liang
Keyword(s):  
Energy Storage ◽  
Power Supply ◽  
Business Models ◽  
Effective Means ◽  
Cost Benefit ◽  
Nsga Ii ◽  
Electricity Cost ◽  
Elite Strategy ◽  
New Business ◽  
The Cost

With the deepening of the reform of the power system, electricity sales companies are required to explore new business models and provide multi-faceted marketing programs for users. At the same time, with the reduction of energy storage (ES) costs and the gradual maturity of technology, user side ES, especially Battery ES, has become an effective means for enhancing users' power supply reliability and reducing electricity bills. Battery ES, as the standby power supply, has a vast user side application. The configuration of ES can help users to ameliorate power quality and reduce electricity cost. It is a critical strategy for electricity sales companies to improve their competitiveness as well. Firstly, this paper analyzes the user side ES and introduces the user side ES development status and relevant policies. Then, we establish an ES configuration optimization model based on the cost–benefit system. To determine the optimal ES capacity of the system’s storage capacity, non-dominated sorting genetic algorithm with elite strategy (NSGA-II) is used as the method solving model. Finally, according to the cost-effectiveness of ES and the period of a contract signed by users, a price package with ES configuration is designed for users to choose.

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