scholarly journals Solving a multi-objective model toward home care staff planning considering cross-training and staff’s preferences by NSGA-II and NRGA

2018 ◽  
Vol 0 (0) ◽  
pp. 0-0
Author(s):  
Hamed Habibnejad-Ledari ◽  
Masoud Rabbani ◽  
Nastaran Ghorbani-Kutenaie
Keyword(s):  
Home Care ◽  
Care Staff ◽  
Nsga Ii ◽  
Multi Objective ◽  
Cross Training ◽  
Objective Model ◽  
Staff Planning

scholarly journals A multi-objective selective maintenance optimization method for series-parallel systems using NSGA-III and NSGA-II evolutionary algorithms

2021 ◽  
Vol 16 (3) ◽  
pp. 372-384
Author(s):  
E.B. Xu ◽  
M.S. Yang ◽  
Y. Li ◽  
X.Q. Gao ◽  
Z.Y. Wang ◽  
...  
Keyword(s):  
Rest Period ◽  
Optimization Method ◽  
Parallel System ◽  
Maintenance Cost ◽  
Nsga Ii ◽  
Allocation Model ◽  
Multi Objective ◽  
Objective Model ◽  
Selective Maintenance ◽  
Personnel Allocation

Aiming at the problem that the downtime is simply assumed to be constant and the limited resources are not considered in the current selective maintenance of the series-parallel system, a three-objective selective maintenance model for the series-parallel system is established to minimize the maintenance cost, maximize the probability of completing the next task and minimize the downtime. The maintenance decision-making model and personnel allocation model are combined to make decisions on the optimal length of each equipment’s rest period, the equipment to be maintained during the rest period and the maintenance level. For the multi-objective model established, the NSGA-III algorithm is designed to solve the model. Comparing with the NSGA-II algorithm that only considers the first two objectives, it is verified that the designed multi-objective model can effectively reduce the downtime of the system.

scholarly journals Using NSGAII to Solve Bi-objective Bed Allocation Problems

2015 ◽  
Vol 1 (3) ◽  
pp. 397
Author(s):  
Jalal A. Sultan ◽  
Ban A. Mitras ◽  
Raghad M. Jasim
Keyword(s):  
Queuing Theory ◽  
Optimization Problems ◽  
Admission Rate ◽  
Nsga Ii ◽  
Multi Objective ◽  
Decision Aiding ◽  
Proposed Model ◽  
Objective Model ◽  
Bed Allocation ◽  
Suitable Framework

The Bed Allocation Problem (BAP) is NP-complete and always high dimensional. In this paper, a bi-objective decision aiding model based on queuing theory is introduced for allocation of beds in a hospital. The problem is modeled as an M/PH/n queue. The objectives include maximizing the patient admission rate human resources, in particular, maximization of the nursing work hours. The proposed model is solved by using Non-dominated Sorting Genetic Algorithm-II (NSGA-II), which is a very effective algorithm for solving multi-objective optimization problems and finding optimal Pareto front. The paper describes an application of the model, dealing with a public hospital in Iraq. The results related that multi-objective model was presented suitable framework for bed allocation and optimum use.

scholarly journals Multi-Objective Model Predictive Control for Real-Time Operation of a Multi-Reservoir System

Water ◽  
2020 ◽  
Vol 12 (7) ◽  
pp. 1898 ◽  
Author(s):  
Nay Myo Lin ◽  
Xin Tian ◽  
Martine Rutten ◽  
Edo Abraham ◽  
José M. Maestre ◽  
...  
Keyword(s):  
Decision Making ◽  
Model Predictive Control ◽  
Real Time ◽  
Predictive Control ◽  
Nsga Ii ◽  
Multi Objective ◽  
Reservoir System ◽  
Time Operation ◽  
Real Time Operation ◽  
Objective Model

This paper presents an extended Model Predictive Control scheme called Multi-objective Model Predictive Control (MOMPC) for real-time operation of a multi-reservoir system. The MOMPC approach incorporates the non-dominated sorting genetic algorithm II (NSGA-II), multi-criteria decision making (MCDM) and the receding horizon principle to solve a multi-objective reservoir operation problem in real time. In this study, a water system is simulated using the De Saint Venant equations and the structure flow equations. For solving multi-objective optimization, NSGA-II is used to find the Pareto-optimal solutions for the conflicting objectives and a control decision is made based on multiple criteria. Application is made to an existing reservoir system in the Sittaung river basin in Myanmar, where the optimal operation is required to compromise the three operational objectives. The control objectives are to minimize the storage deviations in the reservoirs, to minimize flood risks at a downstream vulnerable place and to maximize hydropower generation. After finding a set of candidate solutions, a couple of decision rules are used to access the overall performance of the system. In addition, the effect of the different decision-making methods is discussed. The results show that the MOMPC approach is applicable to support the decision-makers in real-time operation of a multi-reservoir system.

scholarly journals Balance of Cost, Time, and Quality Related to Construction Projects Regarding the Reinforced Concrete of Underground Structures using a Meta-Heuristic Algorithm

2017 ◽  
Vol 63 (4) ◽  
pp. 103-121 ◽  
Author(s):  
S. A. Hosseini ◽  
A. Akbarpour ◽  
H. Ahmadi ◽  
B. Aminnejad
Keyword(s):  
Reinforced Concrete ◽  
Construction Projects ◽  
Minimum Cost ◽  
Business Environment ◽  
Underground Structures ◽  
Nsga Ii ◽  
Multi Objective ◽  
Objective Model ◽  
Best Execution

AbstractUnderground spaces having features such as stability, resistance, and being undetected can play a key role in reducing vulnerability by relocating infrastructures and manpower. In recent years, the competitive business environment and limited resources have mostly focused on the importance of project management in order to achieve its objectives. In this research, in order to find the best balance among cost, time, and quality related to construction projects using reinforced concrete in underground structures, a multi-objective mathematical model is proposed. Several executive approaches have been considered for project activities and these approaches are analyzed via several factors. It is assumed that cost, time, and quality of activities in every defined approach can vary between compact and normal values, and the goal is to find the best execution for activities, achieving minimum cost and the maximum quality for the project. To solve the proposed multi-objective model, the genetic algorithm NSGA-II is used.

scholarly journals MULTI-OBJECTIVE GREEN MIXED VEHICLE ROUTING PROBLEM UNDER ROUGH ENVIRONMENT

Transport ◽  
2021 ◽  
Vol 0 (0) ◽  
pp. 1-13
Author(s):  
Joydeep Dutta ◽  
Partha Sarathi Barma ◽  
Anupam Mukherjee ◽  
Samarjit Kar ◽  
Tanmay De ◽  
...  
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Point Of View ◽  
Third Party ◽  
Third Party Logistics ◽  
Nsga Ii ◽  
Routing Problem ◽  
Multi Objective ◽  
Proposed Model ◽  
Objective Model

This paper proposes a multi-objective Green Vehicle Routing Problem (G-VRP) considering two types of vehicles likely company-owned vehicle and third-party logistics in the imprecise environment. Focusing only on one objective, especially the distance in the VRP is not always right in the sustainability point of view. Here we present a bi-objective model for the G-VRP that can address the issue of the emission of GreenHouse Gases (GHGs). We also consider the demand as a rough variable. This paper uses the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) to solve the proposed model. Finally, it uses Multicriteria Optimization and Compromise Solution (abbreviation in Serbian – VIKOR) method to determine the best alternative from the Pareto front.

scholarly journals A Multi-Objective DV-Hop Localization Algorithm Based on NSGA-II in Internet of Things

Mathematics ◽  
2019 ◽  
Vol 7 (2) ◽  
pp. 184 ◽  
Author(s):  
Penghong Wang ◽  
Fei Xue ◽  
Hangjuan Li ◽  
Zhihua Cui ◽  
Jinjun Chen
Keyword(s):  
Internet Of Things ◽  
Wireless Sensor ◽  
Localization Algorithm ◽  
Nsga Ii ◽  
Multi Objective ◽  
Network Topologies ◽  
Objective Model ◽  
Distance Vector ◽  
Estimation Precision ◽  
Simulation Results

Locating node technology, as the most fundamental component of wireless sensor networks (WSNs) and internet of things (IoT), is a pivotal problem. Distance vector-hop technique (DV-Hop) is frequently used for location node estimation in WSN, but it has a poor estimation precision. In this paper, a multi-objective DV-Hop localization algorithm based on NSGA-II is designed, called NSGA-II-DV-Hop. In NSGA-II-DV-Hop, a new multi-objective model is constructed, and an enhanced constraint strategy is adopted based on all beacon nodes to enhance the DV-Hop positioning estimation precision, and test four new complex network topologies. Simulation results demonstrate that the precision performance of NSGA-II-DV-Hop significantly outperforms than other algorithms, such as CS-DV-Hop, OCS-LC-DV-Hop, and MODE-DV-Hop algorithms.

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