Research on Logistics Distribution Route Based on Multi-objective Sorting Genetic Algorithm

2020 ◽  
Vol 29 (07n08) ◽  
pp. 2040020
Author(s):  
Jun Zhao ◽  
Hui Xiang ◽  
Jinbao Li ◽  
Jie Liu ◽  
Luyao Guo
Keyword(s):  
Genetic Algorithm ◽  
Programming Model ◽  
Optimal Path ◽  
Reference Value ◽  
Theoretical Research ◽  
Logistics System ◽  
Logistics Distribution ◽  
Multi Objective ◽  
Elite Strategy ◽  
Low Dimensional

With the continuous development of society, the social division of labor is further improved, and social production tends to be highly specialized and industrialized. Moreover, enterprise production is increasingly internationalized, and sales are gradually expanding. Therefore, the multi-objective sequencing in logistics distribution is incorporated into the path optimization of the logistics system, and a multi-objective bi-level programming model of time and cost is established. What is more, considering the limitations of the traditional algorithm in solving multi-objective problems, the low-dimensional multi-objective problem is selected, and according to the actual situation, the inheritance strategy of genetic factors is adopted to solve the more targeted rapid dominating sorting genetic problem. Besides, the specific conditions and characteristics of the model determine the encoding method, which is brought into the operation of the cross-mutation law and the interruption of individual populations, so that the building foundation of the model is improved. Based on the further theoretical research on the distribution efficiency of logistics system, the corresponding mathematical model is constructed by using the planning method, and the single cost target is transformed into the time and cost double objective, and the improved fast non dominated sorting genetic algorithm with elite strategy is used to solve the problem, which has certain theoretical innovation. Through simulation, the optimal or near optimal path of distribution vehicles in a certain area is given, which has certain practicality and reference value for the optimization of actual logistics distribution path.

scholarly journals A new model for logistic and transportation of fashion goods in the presence of stochastic market demands considering restricted retailers capacity

2019 ◽  
Author(s):  
Aidin Delgoshaei ◽  
Hengameh Norozi ◽  
Abolfazl Mirzazadeh ◽  
Maryam Farhadi ◽  
Golnaz Hooshmand Pakdel ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Case Studies ◽  
Programming Model ◽  
Real Data ◽  
Objective Functions ◽  
Mathematical Programming Model ◽  
Multi Objective ◽  
Scheduling Method ◽  
Stochastic Market

In today’s world, using fashion goods is a vital of human. In this research, we focused on developing a scheduling method for distributing and selling fashion goods in a multi-market/multi-retailer supply chain while the product demands in markets are stochastic. For this purpose, a new multi-objective mathematical programming model is developed where maximizing the profit of selling fashion goods and minimizing delivering time and customer’s dissatisfaction are considered as objective functions. In continue due to the complexity of the problem, a number of metaheuristics are compared and a hybrid of Non-dominated Sorting Genetic Algorithm II (NSGAII) and simulated annealing is selected for solving the case studies. Then, in order to find the best values for input parameters of the algorithm, a Taguchi method is applied. In continue, a number of case studies are selected from literature review and solved by the algorithm. The outcomes are analyzed and it is found that using multi-objective models can find more realistic solutions. Then, the model is applied for a case study with real data from industry and outcomes showed that the proposed algorithm can be successfully applied in practice.

scholarly journals Multi-objective optimization model for planning metro-based underground logistics system network: Nanjing case study

2021 ◽  
Vol 0 (0) ◽  
pp. 0
Author(s):  
Xiliang Sun ◽  
Wanjie Hu ◽  
Xiaolong Xue ◽  
Jianjun Dong
Keyword(s):  
Programming Model ◽  
Freight Transport ◽  
Optimization Techniques ◽  
Economic Feasibility ◽  
Environmental Cost ◽  
Mixed Integer ◽  
Logistics System ◽  
Transit System ◽  
Multi Objective ◽  
Good Ability

<p style='text-indent:20px;'>Utilizing rail transit system for collaborative passenger-and-freight transport is a sustainable option to conquer urban congestion. This study proposes effective modeling and optimization techniques for planning a city-wide metro-based underground logistics system (M-ULS) network. Firstly, a novel metro prototype integrating retrofitted underground stations and newly-built capsule pipelines is designed to support automated inbound delivery from urban logistics gateways to in-city destinations. Based on four indicators (i.e. unity of freight flows, regional accessibility, environmental cost-saving, and order priority), an entropy-based fuzzy TOPSIS evaluation model is proposed to select appropriate origin-destination flows for underground freight transport. Then, a mixed integer programming model, with a well-matched solution framework combining multi-objective PSO algorithm and A* algorithm, are developed to optimize the location-allocation-routing (LAR) decisions of M-ULS network. Finally, real-world simulation based on Nanjing metro case is conducted for validation. The best facility configurations and flow assignments of the three-tier M-ULS network are reported in details. Results confirm that the proposed algorithm has good ability in providing high-quality Pareto-optimal LAR decisions. Moreover, the Nanjing M-ULS project shows strong economic feasibility while bringing millions of Yuan of annual external benefit to the society and environment.</p>

scholarly journals Multi-Objective Assembly Line Balancing Problem with Setup Times Using Fuzzy Goal Programming and Genetic Algorithm

Symmetry ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 333
Author(s):  
Amy H. I. Lee ◽  
He-Yau Kang ◽  
Chong-Lin Chen
Keyword(s):  
Genetic Algorithm ◽  
Linear Programming ◽  
Assembly Line ◽  
Programming Model ◽  
Assembly Line Balancing ◽  
Linear Programming Model ◽  
Line Balancing ◽  
Multi Objective ◽  
Assembly Line Balancing Problem ◽  
Genetic Algorithm Model

Assembly lines are often indispensable in factories, and in order to attain a certain level of assembly line productivity, multiple goals must be considered at the same time. However, these multiple goals may conflict with each other, and this is a multi-objective assembly line balancing problem. This study considers four objectives, namely minimizing the cycle time, minimizing the number of workstations, minimizing the workload variance, and minimizing the workstation idle time. Since the objectives conflict with each other, for example, minimizing the cycle time may increase the number of workstations, the fuzzy multi-objective linear programming model is used to maximize the satisfaction level. When the problem becomes too complicated, it may not be solved by the fuzzy multi-objective linear programming model using a mathematical software package. Therefore, a genetic algorithm model is proposed to solve the problem efficiently. By studying practical cases of an automobile manufacturer, the results show that the proposed fuzzy multi-objective linear programming model and the genetic algorithm model can solve small-scale multi-objective assembly line balancing problems efficiently, and the genetic algorithm model can obtain good solutions for large-scale problems in a short computational time. Datasets from previous works are adopted to examine the applicability of the proposed models. The results show that both the fuzzy multi-objective linear programming model and the genetic algorithm model can solve the smaller problem cases and that the genetic algorithm model can solve larger problems. The proposed models can be applied by practitioners in managing a multi-objective assembly line balancing problem.

Emergency Logistics System Based on Bilevel Optimization Model

2013 ◽  
Vol 694-697 ◽  
pp. 3605-3609
Author(s):  
Bo Liu ◽  
Bo Li ◽  
Yan Li
Keyword(s):  
Genetic Algorithm ◽  
Bilevel Programming ◽  
Programming Model ◽  
Hybrid Genetic Algorithm ◽  
Optimal Solution ◽  
Bilevel Optimization ◽  
Logistics System ◽  
Emergency Logistics ◽  
Proposed Model ◽  
Bilevel Programming Model

A bilevel programming model is established to determine the emergency storage centers location and the resource supply plan of the provincial and municipal levels by the collaborative mode of the vertical supply and lateral transfer for the emergency logistics system in the unusual emergencies. And the optimal solution is obtained by the hybrid genetic algorithm. Finally, the case shows the effectiveness of the proposed model and its algorithm.

scholarly journals Research on E-commerce Logistics Distribution Path Planning Based on Improved Genetic Algorithm

2022 ◽  
Vol 16 ◽  
pp. 202-208
Author(s):  
Lan Lan
Keyword(s):  
Genetic Algorithm ◽  
Path Planning ◽  
Rapid Development ◽  
Optimal Path ◽  
Route Planning ◽  
Planning Method ◽  
Improved Genetic Algorithm ◽  
Logistics Distribution ◽  
Centralized Planning ◽  
Distribution Efficiency

With the rapid development of the Internet, e-commerce business has gradually emerged. However, its logistics distribution route planning method has problems such as redundancy of logistics data, which cannot achieve centralized planning of distribution paths, resulting in low e-commerce logistics distribution efficiency and long distribution distances, higher cost. Therefore, in order to improve the ability of logistics distribution path planning, this paper designs an e-commerce logistics distribution path planning method based on improved genetic algorithm. Optimize the analysis of e-commerce logistics distribution nodes, establish a modern logistics distribution system, and optimize the total transportation time and transportation cost under the location model of the logistics distribution center. Using hybrid search algorithm and improved genetic algorithm parameters, an improved genetic algorithm distribution path planning model is established to select the optimal path of logistics distribution, and realize e-commerce logistics distribution path with high accuracy, low error and good convergence. planning. According to the experimental results, the method in this paper can effectively shorten the distance of e-commerce logistics distribution path, reduce the number of distribution vehicles, reduce distribution costs, improve distribution efficiency, and effectively achieve centralized planning of logistics distribution. Therefore, the e-commerce logistics distribution route planning method based on improved genetic algorithm has high practical application value.

Pembangunan Model Pengoptimuman Pengeluaran Minyak Sawit dan Isirong Sawit Menggunakan Pendekatan Genetik Kabur

2012 ◽  
Author(s):  
Lily Amelia ◽  
Dzuraidah Abd. Wahab ◽  
Azmi Hassan
Keyword(s):  
Genetic Algorithm ◽  
Expert System ◽  
Palm Oil ◽  
Programming Model ◽  
Palm Kernel ◽  
Fuzzy Expert System ◽  
Crude Palm Oil ◽  
Multi Objective ◽  
Multi Objective Programming ◽  
Palm Oil Mill

Pengeluaran minyak sawit dan isirong sawit sering kali menghadapi masalah, antara lain kadar kehilangan minyak sawit dan isirong sawit yang tinggi semasa pemprosesan dan penggunaan sumber-sumber yang tidak optimum. Model pengoptimuman pengeluaran minyak sawit dan isirong sawit perlu direka bentuk untuk menyelesaikan permasalahan tersebut sehingga dapat memaksimumkan hasil, meminimumkan kos pengeluaran serta meminimumkan kehilangan minyak sawit dan isirong sawit. Model yang dibangunkan adalah gabungan antara model sistem pakar kabur dengan model pengaturcaraan pelbagai objektif. Pengoptimuman model dilakukan dengan menggunakan kaedah algoritma genetik. Model disimulasikan menggunakan data daripada sebuah kilang minyak sawit di Indonesia dan hasil kajian membuktikan pencapaian pengeluaran yang lebih baik serta kehilangan minyak sawit dan isirong sawit yang lebih kecil berbanding keadaan pengeluaran sedia ada di kilang minyak sawit tersebut. Kata kunci: Minyak sawit mentah, pengoptimuman pengeluaran, sistem pakar kabur, model pengaturcaraan pelbagai objektif, algoritma genetik The production of crude palm oil and palm kernel are frequented by problems, among others the high loss of crude palm oil and palm kernel during processing and the consumption of resources that are not optimised. An optimisation model for crude palm oil and palm kernel production has to be developed to solve these problems so as to maximise revenue, minimise production costs as well as to minimise palm oil and palm kernel losses. The developed model is an integration between fuzzy expert system models and multi objective programming model. Model optimisation is performed using the genetic algorithm method. The model was simulated using data from a palm oil mill in Indonesia and results from the study show that the model produces an optimum quantity of production and capable of reducing palm oil and palm kernel losses compared with the existing production conditions in the palm oil mill. Key words: Crude palm oil, production optimisation, fuzzy expert system, multi objective programming model, genetic algorithm

Sign in / Sign up

Export Citation Format

Share Document