Analysis and Modeling of Supply Chain Management of Fresh Products Based on Genetic Algorithm in E-commerce Environment

2020 ◽  
Author(s):  
Yaoting Chen
Keyword(s):  
Genetic Algorithm ◽  
Supply Chain ◽  
Weighted Sum ◽  
Pareto Optimal Solutions ◽  
Optimal Solutions ◽  
Optimal Process ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Proposed Model ◽  
Fresh Products

Abstract BackgroundSupply chain provides the chance to enhance chain performances by decrease these uncertainties. It is a demand for some level of co-ordination of activities and processes within and between organization in the supply chain to decrease uncertainties and increase more cost for customers. Partner selection is an important issue in the supply chain management of fresh products in E-commerce environment. In this paper, we utilized a multi-objective genetic algorithm for evaluation supply chain of fresh products in E-commerce environment. ResultsThe proposed multi-objective genetic algorithm is to search the set of Pareto-optimal solutions for these conflicting objectives using by weighted sum approach. The proposed model suitable for fresh products in E-commerce environment to optimize supply chain are derived. The value of objective 1 (f1) performs approximately nonlinearly with the increasing the value of objective 2,3 and 4 (f2,f3 and f4). At the value of objective 1 of 3.2*105, f2, f3 and f4 is about 4.3*105, 86 and 5.6*104. When the value of objective 1 is increased to 7.6*105, the minimum f2, f3 and f4 is about 3.0*105, 38 and 2.56*104. It is noted that the value of objective 1 is increased from 6.4*105 to 7.6*105, the variation of f2, f3 and f4 is 11.7%, 17.4% and 3.4% respectively. It is pointed out that the variation of f2 and f3 with f1 and f4 is kept within obvious ranges. This practical result highlights the fact that the effects of the fact that effects of f2 and f3 are important factors affecting the performance supply chain network of fresh product in E-commerce environment.ConclusionsIn this paper, we utilized a multi-objective genetic algorithm for evaluation supply chain of fresh products in E-commerce environment. Four objectives for optimal process are included in the proposed model: (1) maximization of green appraisal score, (2) minimization of transportation time and total time comprised of product time, (3) maximization of average product quality, (4) minimization of transportation cost and total cost comprised of product cost. In order to evaluate optimal process, set of Pareto-optimal solutions is obtained based on the weighted sum method.

Multi-Objective Genetic Algorithm for Tasks Allocation in Cloud Computing

2019 ◽  
Vol 9 (3) ◽  
pp. 37-57
Author(s):  
Youssef Harrath ◽  
Rashed Bahlool
Keyword(s):  
Genetic Algorithm ◽  
Cloud Computing ◽  
Lower Bound ◽  
Experimental Results ◽  
Pareto Optimal ◽  
Pareto Optimal Solutions ◽  
Optimal Solutions ◽  
Multi Objective ◽  
Mutation Operators ◽  
Multi Objective Genetic Algorithm

The problem of allocating real-time tasks to cloud computing resources minimizing the makespan is defined as a NP-hard problem. This work studies the same problem with two realistic multi-objective criteria; the makespan and the total cost of execution and communication between tasks. A mathematical model including objective functions and constraints is proposed. In addition, a theoretical lower bound for the makespan which served later as a baseline to benchmark the experimental results is theoretically determined and proven. To solve the studied problem, a multi-objective genetic algorithm is proposed in which new crossover and mutation operators are proposed. Pareto-optimal solutions are retrieved using the genetic algorithm. The experimental results show that genetic algorithm provides efficient solutions in term of makespan for different-size problem instances with reference to the lower bound. Moreover, the proposed genetic algorithm produces many Pareto optimal solutions that dominate the solution given by greedy algorithm for both criteria.

Multi-objective Genetic Algorithm for De Novo Drug Design

2020 ◽  
Vol 16 ◽  
Author(s):  
R. Vasundhara Devi ◽  
S. Siva Sathya ◽  
S. Mohane Coumar
Keyword(s):  
Genetic Algorithm ◽  
Drug Design ◽  
De Novo ◽  
New Drugs ◽  
Weighted Sum ◽  
De Novo Drug Design ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Fragment Library ◽  
Novel Drug

Background: Genetic algorithm being a famous evolutionary algorithm, multiple objectives of drug design are solved using weighted sum approach. Objective: To design a computational tool for the de novo design of novel drug-like molecules to aid in the discovery of new drugs using Genetic algorithm and chemical fragment library and reference molecules. Method: Multi-objective optimization using genetic algorithm and weighted sum approach. Results: The drug-like molecules for the reference molecules such as Lidocaine, Furano-pyrimidine, Imatinib, Atorvastatin and Glipizide. Conclusion: The performance of the MOGADdrug tool is evaluated using 5 reference molecules and the designed molecules are compared with Zinc and Pubchem databases along with their docking investigations.

Multi-Objective Optimization for a Novel Electrostatic-Feedback Micro-Sensor Based on Genetic Algorithm

2007 ◽  
Author(s):  
Yongquan Wang ◽  
Hualing Chen ◽  
Zhiying Ou ◽  
Xueming He
Keyword(s):  
Genetic Algorithm ◽  
Multidisciplinary Design Optimization ◽  
Optimal Solution ◽  
Optimization Approach ◽  
Pareto Optimal Solutions ◽  
Optimal Solutions ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Micro Sensor ◽  
Energy Relations

In this paper, we present the multi-objective optimization for an entire microsystem, a novel capacitive electrostatic feedback accelerometer. From the energy relations of the coupled electrostatic-field, the dynamic model of the system is constructed. Aiming at the global performance, a multi-objective optimization model, where sensitivity, resolution and damping resonant frequency are selected as objectives, is established based on the concept of multidisciplinary design optimization (MDO). Genetic algorithm (GA) is used to solve this problem, and compared with a traditional optimization approach, sequence quadratic programming (SQP). Both the two algorithms can achieve our aim commendably, and the optimal solution given by GA is more satisfied. The research provides us a good foundation to develop the stochastic and implicit parallel properties of GA to obtain Pareto optimal solutions.

scholarly journals Application of multi-objective optimization based on genetic algorithm for sustainable strategic supplier selection under fuzzy environment

2017 ◽  
Vol 10 (2) ◽  
pp. 188 ◽  
Author(s):  
Muhammad Hashim ◽  
Muhammad Nazam ◽  
Liming Yao ◽  
Sajjad Ahmad Baig ◽  
Muhammad Abrar ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Supply Chain ◽  
Supplier Selection ◽  
Decision Makers ◽  
Quality Level ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Fuzzy Environment ◽  
Proposed Model ◽  
Textile Sector

Purpose:  The incorporation of environmental objective into the conventional supplier selection practices is crucial for corporations seeking to promote green supply chain management (GSCM). Challenges and risks associated with green supplier selection have been broadly recognized by procurement and supplier management professionals. This paper aims to solve a Tetra “S” (SSSS) problem based on a fuzzy multi-objective optimization with genetic algorithm in a holistic supply chain environment. In this empirical study, a mathematical model with fuzzy coefficients is considered for sustainable strategic supplier selection (SSSS) problem and a corresponding model is developed to tackle this problem.Design/methodology/approach: Sustainable strategic supplier selection (SSSS) decisions are typically multi-objectives in nature and it is an important part of green production and supply chain management for many firms. The proposed uncertain model is transferred into deterministic model by applying the expected value mesurement (EVM) and genetic algorithm with weighted sum approach for solving the multi-objective problem. This research focus on a multi-objective optimization model for minimizing lean cost, maximizing sustainable service and greener product quality level. Finally, a mathematical case of textile sector is presented to exemplify the effectiveness of the proposed model with a sensitivity analysis.Findings: This study makes a certain contribution by introducing the Tetra ‘S’ concept in both the theoretical and practical research related to multi-objective optimization as well as in the study of sustainable strategic supplier selection (SSSS) under uncertain environment. Our results suggest that decision makers tend to select strategic supplier first then enhance the sustainability.Research limitations/implications: Although the fuzzy expected value model (EVM) with fuzzy coefficients constructed in present research should be helpful for solving real world problems. A detailed comparative analysis by using other algorithms is necessary for solving similar problems of agriculture, pharmaceutical, chemicals and services sectors in future.Practical implications: It can help the decision makers for ordering to different supplier for managing supply chain performance in efficient and effective manner. From the procurement and engineering perspectives, minimizing cost, sustaining the quality level and meeting production time line is the main consideration for selecting the supplier. Empirically, this can facilitate engineers to reduce production costs and at the same time improve the product quality.Originality/value: In this paper, we developed a novel multi-objective programming model based on genetic algorithm to select sustainable strategic supplier (SSSS) under fuzzy environment. The algorithm was tested and applied to solve a real case of textile sector in Pakistan. The experimental results and comparative sensitivity analysis illustrate the effectiveness of our proposed model.

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