Multi-objective imperfect selective maintenance optimization for series-parallel systems with stochastic mission duration

To improve the probability that an engineering system successfully completes its next mission, it is crucial to implement timely maintenance activities, especially when maintenance time or maintenance resources are limited. Taking series-parallel system as the object of study, this paper develops a multi-objective imperfect selective maintenance optimization model. Among it, during the scheduled breaks, potential maintenance actions are implemented for the components, ranging from minimal repair to replacement. Considering that the level of maintenance actions is closely related to the maintenance cost, age reduction coefficient and hazard rate adjustment coefficient are taken into account. Moreover, improved hybrid hazard rate approach is adopted to describe the reliability improvement of the components, and the mission duration is regarded as a random variable. On this basis, a nonlinear stochastic optimization model is established with dual objectives to minimize the total maintenance cost and maximize the system reliability concurrently. The fast elitist non-dominated sorting genetic algorithm (NSGA-II) is adopted to solve the model. Numerical experiments are conducted to verify the effectiveness of the proposed approach. The results indicate that the proposed model can obtain better scheduling schemes for the maintenance resources, and more flexible maintenance plans are gained.

Download Full-text

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

Advances in Production Engineering & Management ◽

10.14743/apem2021.3.407 ◽

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.

Download Full-text

Development a Novel Integrated Distributed Multi-objective Simulation-optimization Model for Coastal Aquifers Management Using NSGA-II and GMS Models

Water Resources Management ◽

10.1007/s11269-021-03012-0 ◽

2021 ◽

Author(s):

Mahmoud Mohammad Rezapour Tabari ◽

Mahbobeh Abyar

Keyword(s):

Coastal Aquifers ◽

Optimization Model ◽

Simulation Optimization ◽

Nsga Ii ◽

Multi Objective ◽

Aquifers Management

Download Full-text

Energy Saving and Low-Cost-Oriented Design Processes of Blank’s Dimensions Based on Multi-Objective Optimization Model

Processes ◽

10.3390/pr7110811 ◽

2019 ◽

Vol 7 (11) ◽

pp. 811 ◽

Cited By ~ 1

Author(s):

Yongmao Xiao ◽

Qingshan Gong ◽

Xiaowu Chen

Keyword(s):

Energy Consumption ◽

Energy Saving ◽

Optimization Model ◽

Low Cost ◽

Minimum Cost ◽

Processing Parameters ◽

Multi Objective Optimization ◽

Nsga Ii ◽

Multi Objective ◽

Set Up

The blank’s dimensions are an important focus of blank design as they largely determine the energy consumption and cost of manufacturing and further processing the blank. To achieve energy saving and low cost during the optimization of blank dimensions design, we established energy consumption and cost objectives in the manufacturing and further processing of blanks by optimizing the parameters. As objectives, we selected the blank’s production and further processing parameters as optimization variables to minimize energy consumption and cost, then set up a multi-objective optimization model. The optimal blank dimension was back calculated using the parameters of the minimum processing energy consumption and minimum cost state, and the model was optimized using the non-dominated genetic algorithm-II (NSGA-II). The effect of designing blank dimension in saving energy and costs is obvious compared with the existing methods.

Download Full-text

Multi-Objective Optimum Design for Wave-Plate Demister Based on NSGA-ΙΙ Algorithm

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.864-867.1163 ◽

2013 ◽

Vol 864-867 ◽

pp. 1163-1167

Author(s):

Zong Liang Qiao ◽

Lei Zhang ◽

Feng Qi Si ◽

Zhi Gao Xu

Keyword(s):

Optimization Model ◽

Power Plants ◽

Structural Parameters ◽

Weight Coefficient ◽

Structure Design ◽

Flue Gas Desulfurization ◽

Support Vector ◽

Nsga Ii ◽

Wave Plate ◽

Multi Objective

For optimizing the structure design of the wave-plate demister vanes in wet flue gas desulfurization system (WFGD) of power plants, the characteristics models of removal efficiency and pressure drop were established by using least squares support vector machine (LSSVM) based on numerical simulation results. The highest relative error between the predicted output and measured value is 2%, it proves the modeling is good for the prediction. Based on the characteristics models, a multi-objective optimization model was established. It used the structural parameters as the optimal variables and the demister characteristics as the objective function. This optimization model was solved by non dominated sorting genetic algorithm (NSGA-II). The simulation data show that the Multi-objective optimum method can get more effective results compared to the weight coefficient method.

Download Full-text

Maintenance optimization of reconfigurable systems based on multi-objective Birnbaum importance

Proceedings of the Institution of Mechanical Engineers Part O Journal of Risk and Reliability ◽

10.1177/1748006x20901983 ◽

2020 ◽

pp. 1748006X2090198

Author(s):

Chenyang Ma ◽

Wei Wang ◽

Zhiqiang Cai ◽

Jiangbin Zhao

Keyword(s):

Genetic Algorithm ◽

System Reliability ◽

System Performance ◽

Cost Effective ◽

System Structure ◽

Maintenance Cost ◽

Reconfigurable Systems ◽

Maintenance Optimization ◽

Multi Objective ◽

Better Than

Reconfigurable systems can meet the changing requirements of system performance by several approaches, such as adjusting the system structure, improving the component performance, and reassigning components. However, it is also challengeable to find a cost-effective maintenance scheme by integrating these maintenance approaches. This article investigates the multi-objective maintenance optimization problem for reconfigurable systems with the consideration of maintenance cost and system reliability. First, the multi-objective maintenance optimization model is established to maximize the system reliability and minimize the total maintenance cost considering the constraints on budget and system performance. Second, a multi-objective Birnbaum importance is proposed to quantify the contribution of the individual component to the system reliability. The multi-objective Birnbaum importance–based non-dominated sorting genetic algorithm II is developed to obtain the optimal maintenance scheme with the maximum system reliability and minimum maintenance cost. Finally, the performance of multi-objective Birnbaum importance–based non-dominated sorting genetic algorithm II is proved by three numerical experiments. Experiment 1 verifies the advantage of multi-objective Birnbaum importance compared with Birnbaum importance to improve the system reliability in direct maintenance. Experiment 2 shows that the effectiveness of multi-objective Birnbaum importance is much better than that of the Birnbaum importance to enhance the performance of non-dominated sorting genetic algorithm II in comprehensive maintenance. Experiment 3 illustrates that the performance of multi-objective Birnbaum importance–based non-dominated sorting genetic algorithm II is better than that of other multi-objective algorithms combining with multi-objective Birnbaum importance.

Download Full-text

Prediction-Based Multi-Objective Optimization for Oil Purchasing and Distribution with the NSGA-II Algorithm

International Journal of Information Technology & Decision Making ◽

10.1142/s0219622016500097 ◽

2016 ◽

Vol 15 (02) ◽

pp. 423-451 ◽

Cited By ~ 7

Author(s):

Lean Yu ◽

Zebin Yang ◽

Ling Tang

Keyword(s):

Information Technology ◽

Optimization Model ◽

Profit Maximization ◽

Optimization Approach ◽

Risk Minimization ◽

Nsga Ii ◽

Training Time ◽

Multi Objective ◽

Planning Optimization ◽

Oil Markets

Due to the uncertainty in oil markets, this paper proposes a novel approach for oil purchasing and distribution optimization by incorporating price and demand prediction, i.e., the prediction-based oil purchasing-and-distribution optimization model. In particular, the proposed method bridges the latest information technology and decision-making technique by introducing the recently proposed information technology (i.e., extreme learning machine (ELM)) into the oil purchasing-and-distribution optimization model. Two main steps are involved: market prediction and planning optimization in the proposed model. In market prediction, the ELM technique is employed to provide fast training time and accurate forecasting results for oil prices and demands. In planning optimization, two objectives of general profit maximization and inventory risk minimization are considered; and the most popular multi-objective evolutionary algorithm (MOEA), nondominated sorting genetic algorithm II (NSGA-II), is implemented to search approximate Pareto optimal solutions. For illustration and verification, the motor gasoline market in the US is focused on as the study sample, and the experimental results demonstrate the superiority of the proposed prediction-based optimization approach over its benchmark models (without market prediction and/or planning optimization), in terms of the highest profit and the lowest risk.

Download Full-text

Manufacturer Order Fulfillment Based on Multi-Objective Optimization NSGA II Model

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.340.136 ◽

2013 ◽

Vol 340 ◽

pp. 136-140

Author(s):

Liang You Shu ◽

Ling Xiao Yang

Keyword(s):

Optimization Model ◽

Optimization Problems ◽

Population Based ◽

Pareto Optimal ◽

Order Fulfillment ◽

Multi Objective Optimization ◽

Nsga Ii ◽

Pareto Optimal Front ◽

Multi Objective ◽

Local Search Procedure

The aim of this paper is to study the production and delivery decision problem in the Manufacturer Order Fulfillment. Owing to the order fulfillment optimization condition of the manufacturer, the multi-objective optimization model of manufacturers' production and delivery has been founded. The solution of the multi-objective optimization model is also very difficult. Fast and Elitist Non-dominated Sorting Genetic Algorithm (NSGA II) have been applied successfully to various test and real-world optimization problems. These population based the algorithm provide a diverse set of non-dominated solutions. The obtained non-dominated set is close to the true Pareto-optimal front. But its convergence to the true Pareto-optimal front is not guaranteed. Hence SBX is used as a local search procedure. The proposed procedure is successfully applied to a special case. The results validate that the algorithm is effective to the multi-objective optimization model.

Download Full-text

Using NSGA-II to solve multi-objective competitive location problem with cooperative coverage for distance-based attractiveness

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189592 ◽

2021 ◽

pp. 1-12

Author(s):

Sheng-Chuan Wang ◽

Ta-Cheng Chen

Keyword(s):

Genetic Algorithms ◽

Optimization Model ◽

Location Problem ◽

Solution Procedure ◽

Competitive Location ◽

Multi Objective Optimization ◽

Demand Point ◽

Nsga Ii ◽

Solution Sets ◽

Multi Objective

Multi-objective competitive location problem with cooperative coverage for distance-based attractiveness is introduced in this paper. The potential facilities compete to be selected to serve all demand points which are determined by maximizing total collective attractiveness of all demand points from assigned facilities and minimizing the fixed and distance costs between all demand points and selected facilities. Facility attractiveness is represented as a coverage of the facility with full, partial and none coverage corresponding to maximum full and partial coverage radii. Cooperative coverage, which the demand point is covered by at least one facility, is also considered. The problem is formulated as a multi-objective optimization model and solution procedure based on elitist non-dominated sorting genetic algorithms (NSGA-II) is developed. Experimental example demonstrates the best non-dominated solution sets obtained by developed solution procedure. Contributions of this paper include introducing competitive location problem with facility attractiveness as a distance-based coverage of the facility, re-categorizing facility coverage classification and developing solution procedure base upon NSGA-II.

Download Full-text

Research on the Allocation of 3D Printing Emergency Supplies in Public Health Emergencies

Frontiers in Public Health ◽

10.3389/fpubh.2021.657276 ◽

2021 ◽

Vol 9 ◽

Author(s):

Jianjia He ◽

Gang Liu ◽

Thi Hoai Thuong Mai ◽

Ting Ting Li

Keyword(s):

Public Health ◽

3D Printing ◽

Optimization Model ◽

Supply And Demand ◽

Algorithm Analysis ◽

Multi Objective Optimization ◽

Matching Problem ◽

Nsga Ii ◽

Multi Objective ◽

Public Health Emergencies

Significant public health emergencies greatly impact the global supply chain system of production and cause severe shortages in personal protective and medical emergency supplies. Thus, rapid manufacturing, scattered distribution, high design degrees of freedom, and the advantages of the low threshold of 3D printing can play important roles in the production of emergency supplies. In order to better realize the efficient distribution of 3D printing emergency supplies, this paper studies the relationship between supply and demand of 3D printing equipment and emergency supplies produced by 3D printing technology after public health emergencies. First, we fully consider the heterogeneity of user orders, 3D printing equipment resources, and the characteristics of diverse production objectives in the context of the emergent public health environment. The multi-objective optimization model for the production of 3D printing emergency supplies, which was evaluated by multiple manufacturers and in multiple disaster sites, can maximize time and cost benefits of the 3D printing of emergency supplies. Then, an improved non-dominated sorting genetic algorithm (NSGA-II) to solve the multi-objective optimization model is developed and compared with the traditional NSGA-II algorithm analysis. It contains more than one solution in the Pareto optimal solution set. Finally, the effectiveness of 3D printing is verified by numerical simulation, and it is found that it can solve the matching problem of supply and demand of 3D printing emergency supplies in public health emergencies.

Download Full-text