scholarly journals Multiobjective Order Assignment Optimization in a Global Multiple-Factory Environment

2014 ◽  
Vol 2014 ◽  
pp. 1-14 ◽  
Author(s):  
Rong-Chang Chen ◽  
Pei-Hsuan Hung
Keyword(s):  
Genetic Algorithm ◽  
Assignment Problem ◽  
Real Data ◽  
Crucial Issue ◽  
Multiobjective Genetic Algorithm ◽  
Customer Order ◽  
Wide Range ◽  
Feasible Solutions ◽  
Order Assignment ◽  
Customer Orders

In response to radically increasing competition, many manufacturers who produce time-sensitive products have expanded their production plants to worldwide sites. Given this environment, how to aggregate customer orders from around the globe and assign them quickly to the most appropriate plants is currently a crucial issue. This study proposes an effective method to solve the order assignment problem of companies with multiple plants distributed worldwide. A multiobjective genetic algorithm (MOGA) is used to find solutions. To validate the effectiveness of the proposed approach, this study employs some real data, provided by a famous garment company in Taiwan, as a base to perform some experiments. In addition, the influences of orders with a wide range of quantities demanded are discussed. The results show that feasible solutions can be obtained effectively and efficiently. Moreover, if managers aim at lower total costs, they can divide a big customer order into more small manufacturing ones.

scholarly journals Economic Analysis on Value Chain of Taxi Fleet with Battery-Swapping Mode Using Multiobjective Genetic Algorithm

2012 ◽  
Vol 2012 ◽  
pp. 1-15 ◽  
Author(s):  
Guobao Ning ◽  
Zijian Zhen ◽  
Peng Wang ◽  
Yang Li ◽  
Huaixian Yin
Keyword(s):  
Genetic Algorithm ◽  
Economic Analysis ◽  
Value Chain ◽  
Mode Selection ◽  
Real Data ◽  
Economic Benefits ◽  
Taxi Driver ◽  
Analysis Model ◽  
Multiobjective Genetic Algorithm ◽  
Station Group

This paper presents an economic analysis model on value chain of taxi fleet with battery-swapping mode in a pilot city. In the model, economic benefits of charging-swapping station group, taxi company, and taxi driver in the region have been taken into consideration. Thus, the model is a multiobjective function and multiobjective genetic algorithm is used to solve this problem. According to the real data collected from the pilot city, the multiobjective genetic algorithm is tested as an effective method to solve this problem. Furthermore, the effects of price of electricity, price of battery package, life cycle of battery package, cost of battery-swapping devices and infrastructure, and driving mileage per day on the benefits of value holders are analyzed, which provide theoretical and practical reference for the deployment of electric vehicles, for the national subsidy criteria adjusment, technological innovation instruction, commercial mode selection, and infrastructure construction.

scholarly journals A New Genetic Algorithm with Agent-Based Crossover for Generalized Assignment Problem

2019 ◽  
Vol 48 (3) ◽  
pp. 389-400 ◽  
Author(s):  
Murat DÖRTERLER
Keyword(s):  
Genetic Algorithm ◽  
Assignment Problem ◽  
Dominant Gene ◽  
Minimum Cost ◽  
Fertility Rate ◽  
Generalized Assignment Problem ◽  
Agent Based ◽  
Local Searches ◽  
Generalized Assignment ◽  
Feasible Solutions

Generalized assignment problem (GAP) considers finding minimum cost assignment of n tasks to m agents provided each task should be assigned to one agent only. In this study, a new Genetic Algorithm (GA) with some new methods is proposed to solve GAPs. The agent-based crossover is based on the concept of dominant gene in genotype science and increases fertility rate of feasible solutions. The solutions are classified as infeasible, feasible and mature with reference to their conditions. The new local searches provide not only feasibility in high diversity but high profitability for the solutions. A solution is not given up through maturation-based replacement until it reaches its best.  Computational results show that the agent-based crossover has much higher fertility rate compared to classical crossover. Also, the proposed GA creates either optimal or approximately optimal solutions.

Accuracy and sensitivity of different Bayesian methods for genomic prediction using simulation and real data

2020 ◽  
Vol 19 (3) ◽  
Author(s):  
Saheb Foroutaifar
Keyword(s):  
Data Analysis ◽  
Bayesian Methods ◽  
Milk Fat ◽  
Prediction Accuracy ◽  
Real Data ◽  
Fat Percentage ◽  
Real Data Analysis ◽  
Wide Range ◽  
High Heritability ◽  
Qtl Effects

AbstractThe main objectives of this study were to compare the prediction accuracy of different Bayesian methods for traits with a wide range of genetic architecture using simulation and real data and to assess the sensitivity of these methods to the violation of their assumptions. For the simulation study, different scenarios were implemented based on two traits with low or high heritability and different numbers of QTL and the distribution of their effects. For real data analysis, a German Holstein dataset for milk fat percentage, milk yield, and somatic cell score was used. The simulation results showed that, with the exception of the Bayes R, the other methods were sensitive to changes in the number of QTLs and distribution of QTL effects. Having a distribution of QTL effects, similar to what different Bayesian methods assume for estimating marker effects, did not improve their prediction accuracy. The Bayes B method gave higher or equal accuracy rather than the rest. The real data analysis showed that similar to scenarios with a large number of QTLs in the simulation, there was no difference between the accuracies of the different methods for any of the traits.

scholarly journals Efficiency and fairness criteria in the assignment of students to projects

2021 ◽  
Author(s):  
Nahid Rezaeinia ◽  
Julio César Góez ◽  
Mario Guajardo
Keyword(s):  
Assignment Problem ◽  
Real Data ◽  
Administrative Staff ◽  
Program Administration ◽  
Master Program ◽  
Educational Environments

AbstractTeamwork has increasingly become more popular in educational environments. With the also increasing mobility trends in the educational sector, internationalization and other diversity features have gained importance in the structure of teams. In this paper, we discuss an assignment problem arising in the allocation of students to business projects in a master program in Norway. Among other problem features, the students state their preferences on the projects they most want to conduct. There are also requirements from the companies that propose the projects and from the program administration. We develop a bi-objective approach to consider efficiency and fairness criteria in this assignment problem. We test the model using real data of 2017 and 2018, in joint collaboration with the administrative staff of the program. In light of the good results, our proposed solutions have been implemented in practice in 2019 and 2020. The implementation of these solutions have been beneficial for the administration, the students, and the companies.

scholarly journals Remote Sensing Image Stripe Detecting and Destriping Using the Joint Sparsity Constraint with Iterative Support Detection

2019 ◽  
Vol 11 (6) ◽  
pp. 608 ◽  
Author(s):  
Yun-Jia Sun ◽  
Ting-Zhu Huang ◽  
Tian-Hui Ma ◽  
Yong Chen
Keyword(s):  
Remote Sensing ◽  
Real Data ◽  
Total Variation Regularization ◽  
Joint Sparsity ◽  
Alternating Direction ◽  
Proposed Model ◽  
Comparison Results ◽  
Wide Range ◽  
Image Edges ◽  
Full Consideration

Remote sensing images have been applied to a wide range of fields, but they are often degraded by various types of stripes, which affect the image visual quality and limit the subsequent processing tasks. Most existing destriping methods fail to exploit the stripe properties adequately, leading to suboptimal performance. Based on a full consideration of the stripe properties, we propose a new destriping model to achieve stripe detection and stripe removal simultaneously. In this model, we adopt the unidirectional total variation regularization to depict the directional property of stripes and the weighted ℓ 2 , 1 -norm regularization to depict the joint sparsity of stripes. Then, we combine the alternating direction method of multipliers and iterative support detection to solve the proposed model effectively. Comparison results on simulated and real data suggest that the proposed method can remove and detect stripes effectively while preserving image edges and details.

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