Multi-objective optimization model and evolutional solution of network node matching problem

A novel decision method for solving the two-sided matching problem is proposed in this paper, in which the preferences provided by agents are in the format of ordinal numbers and the preference provided by intermediary is in the format of costs. The concept of two-sided matching is firstly introduced, and the two-sided matching problem with ordinal numbers and costs is discribed. Then the related concepts on costs are given. Considering the ordinal number of each agent and the cost of intermediary, a multi-objective optimization model is set up. The method of weighted sums based on membership function is used to convert the multi-objective optimization model into a single-objective model. By solving the model, the matching alternative can be obtained.

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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.

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Research on Task-Service Network Node Matching Method Based on Multi-Objective Optimization Model in Dynamic Hyper-Network Environment

Micromachines ◽

10.3390/mi12111427 ◽

2021 ◽

Vol 12 (11) ◽

pp. 1427

Author(s):

Chenglei Zhang ◽

Jiajia Liu ◽

Hu Han ◽

Xiaojie Wang ◽

Bo Yuan ◽

...

Keyword(s):

3D Printing ◽

Optimization Model ◽

Cloud Manufacturing ◽

Network Node ◽

Network Environment ◽

Multi Objective Optimization ◽

Matching Method ◽

Service Network ◽

Multi Objective ◽

Mathematical Algorithm

In order to reduce the cost of manufacturing and service for the Cloud 3D printing (C3DP) manufacturing grid, to solve the problem of resources optimization deployment for no-need preference under circumstance of cloud manufacturing, consider the interests of enterprises which need Cloud 3D printing resources and cloud platform operators, together with QoS and flexibility of both sides in the process of Cloud 3D printing resources configuration service, a task-service network node matching method based on Multi-Objective optimization model in dynamic hyper-network environment is built for resource allocation. This model represents interests of the above-mentioned two parties. In addition, the model examples are solved by modifying Mathematical algorithm of Node Matching and Evolutionary Solutions. Results prove that the model and the algorithm are feasible, effective and stable.

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Research on Blank Optimization Design Based on Low-Carbon and Low-Cost Blank Process Route Optimization Model

Sustainability ◽

10.3390/su13041929 ◽

2021 ◽

Vol 13 (4) ◽

pp. 1929

Author(s):

Yongmao Xiao ◽

Wei Yan ◽

Ruping Wang ◽

Zhigang Jiang ◽

Ying Liu

Keyword(s):

Optimization Model ◽

Optimization Design ◽

Design Method ◽

Low Cost ◽

Route Optimization ◽

Low Carbon ◽

Multi Objective Optimization ◽

Multi Objective ◽

Blank Design ◽

Process Route

The optimization of blank design is the key to the implementation of a green innovation strategy. The process of blank design determines more than 80% of resource consumption and environmental emissions during the blank processing. Unfortunately, the traditional blank design method based on function and quality is not suitable for today’s sustainable development concept. In order to solve this problem, a research method of blank design optimization based on a low-carbon and low-cost process route optimization is proposed. Aiming at the processing characteristics of complex box type blank parts, the concept of the workstep element is proposed to represent the characteristics of machining parts, a low-carbon and low-cost multi-objective optimization model is established, and relevant constraints are set up. In addition, an intelligent generation algorithm of a working step chain is proposed, and combined with a particle swarm optimization algorithm to solve the optimization model. Finally, the feasibility and practicability of the method are verified by taking the processing of the blank of an emulsion box as an example. The data comparison shows that the comprehensive performance of the low-carbon and low-cost multi-objective optimization is the best, which meets the requirements of low-carbon processing, low-cost, and sustainable production.

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