Multi objective optimization model and algorithm of logistics service transaction matching on blockchain platform

2021 ◽  
pp. 1-13
Author(s):  
Xiaokun Wang ◽  
Chenxi Zhao ◽  
Yuan Wang ◽  
Hong Xiao
Keyword(s):  
Optimization Model ◽  
Evaluation Index System ◽  
Evaluation Index ◽  
Service Users ◽  
Multi Objective Optimization ◽  
Data Types ◽  
Nsga Ii ◽  
Multi Objective ◽  
Logistics Service ◽  
Satisfaction Degree

The logistics service transaction matching of the blockchain platform is an important part of the transaction. Firstly, the evaluation index system of the logistics service transaction satisfaction degree in the blockchain platform is constructed. According to the evaluation index of the logistics service users and providers, the actual situation of the logistics service transaction and the principle of maximizing the satisfaction degree of the logistics service users and providers, we set the expectation range of both sides and do data standardization, design the calculation method of satisfaction information amount for different expectation data types, build a multi-objective optimization model for logistics service transaction matching on blockchain platform, and seek the matching solution that makes all service users and providers the most satisfaction. Considering the large amount of data in blockchain, the concurrent design of NSGA-II algorithm based on Hadoop is carried out. Through the calculation of the example, in the matching of multi-objective optimization model of logistics service transaction matching on the blockchain platform, when the users and providers of logistics service are more satisfied about the indicators with each other, and then it gets more satisfactory matching results.

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

Processes ◽  
2019 ◽  
Vol 7 (11) ◽  
pp. 811 ◽  
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.

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

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.

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

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.

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

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.

scholarly journals A Multi-Objective Optimization Model Using Improved NSGA-II for Optimizing Metal Mines Production Process

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 28847-28858 ◽  
Author(s):  
Xiaowei Gu ◽  
Xunhong Wang ◽  
Zaobao Liu ◽  
Wenhua Zha ◽  
Xiaochuan Xu ◽  
...  
Keyword(s):  
Production Process ◽  
Optimization Model ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Multi Objective ◽  
Metal Mines

scholarly journals Multi-Objective Optimization of Forth Flotation Process: An Application in Gold Ore

2021 ◽  
Vol 13 (15) ◽  
pp. 8314
Author(s):  
Wen Zhang ◽  
Qinghe Yuan ◽  
Shun Jia ◽  
Zhaojun (Steven) Li ◽  
Xianhui Yin
Keyword(s):  
Optimization Model ◽  
Recovery Rate ◽  
Gold Recovery ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Flotation Process ◽  
Gold Ore ◽  
Multi Objective ◽  
Concentrate Grade ◽  
Output Indicators

In order to improve production control ability in the gold ore flotation process, the output index in this process was studied. Flotation is an effective gold recovery process. Gold concentrate grade and gold recovery rate are the key output indicators of the flotation process. However, in the existing studies exploring the impact of parameter changes on the output indicators, the control ability of the output indicators is insufficient, and the interaction between variables is inadequately considered. Therefore, a multi-objective optimization model based on response surface methodology and the non-dominated sorting genetic algorithm-II (NSGA-II) is proposed in this paper. Firstly, the experiment was designed based on the Box-Behnken principle. Based on the experimental results, the interaction between variables was analyzed and the response polynomial was fitted. Secondly, a multi-objective optimization model was constructed, and the NSGA-II was used to solve the model. Finally, an example of gold ore flotation was used to verify the effectiveness of the method. The optimal solution was a gold concentrate grade of 75.46 g/t and a gold recovery rate of 85.98%.

scholarly journals A Multi-Objective Optimization Model for Designing Business Portfolio in the Oil Industry

2018 ◽  
Vol 8 (6) ◽  
pp. 3657-3667
Author(s):  
A. K. Kiamehr ◽  
A. Azar ◽  
M. D. Nayeri
Keyword(s):  
Conceptual Model ◽  
Optimization Model ◽  
Quantitative Methods ◽  
Corporate Strategy ◽  
Oil Industry ◽  
Strategic Decision ◽  
Strategic Decision Making ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Multi Objective

Designing a business portfolio is one of the key decisions in developing corporate strategy. Most of the previous models are either non-quantitative or financial with an emphasis on optimizing a portfolio of investments or projects. This research represents a multi-objective optimization model that firstly, employs quantitative methods in strategic decision-making, and secondly, quantifies and considers non-financial, strategic variables in problem modeling. In this regard, links between businesses within a portfolio have been classified into four groups of market synergy, capabilities synergy, parenting costs, and sharing benefits, and have been structured as a conceptual model. Although the conceptual model can be applied to various industries, it is formulated for designing the portfolio of multi-business companies in Iran oil industry. The model has been solved for three cases by NSGA-II algorithm and strategic insights have been explored for different corporate types.

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

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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