scholarly journals Genetic algorithm for the cargo shunting cooperation between two hub-and-spoke logistics networks

2019 ◽  
Vol 12 (2) ◽  
pp. 356
Author(s):  
Jingjing Hu ◽  
Youfang Huang
Keyword(s):  
Genetic Algorithm ◽  
Programming Model ◽  
Northern China ◽  
Third Party ◽  
Utilization Rate ◽  
Network Resources ◽  
Hub And Spoke ◽  
Logistics Network ◽  
Logistics Networks ◽  
Hub And Spoke Networks

Purpose: The overstocked goods flow in the hub of hub-and-spoke logistics network should be disposed of in time, to reduce delay loss and improve the utilization rate of logistics network resources. The problem we need to solve is to let logistics network cooperate by sharing network resources to shunt goods from one hub-and-spoke network to another hub-and-spoke network.Design/methodology/approach: This paper proposes the hub shunting cooperation between two hub-and-spoke networks. Firstly, a hybrid integer programming model was established to describe the problem, and then a multi-layer genetic algorithm was designed to solve it and two hub-and-spoke networks are expressed by different gene segments encoded by genes. The network data of two third-party logistics companies in southern and northern China are used for example analysis at the last step. Findings: The hub-and-spoke networks of the two companies were constructed simultaneously. The transfer cost coefficient between two networks and the volume of cargo flow in the network have an impact on the computation of hubs that needed to be shunt and the corresponding cooperation hubs in the other network.Originality/value: Previous researches on hub-and-spoke logistics network focus on one logistics network, while we study the cooperation and interaction between two hub-and-spoke networks. It shows that two hub-and-spoke network can cooperate across the network to shunt the goods in the hub and improve the operation efficiency of the logistics network. 

An Efficient Genetic Algorithm for the Design of Hub-and-Spoke Networks

2013 ◽  
Vol 17 (4) ◽  
pp. 793-796 ◽  
Author(s):  
Bruno N. Gomes ◽  
Alexandre X. Martins ◽  
Ricardo S. de Camargo ◽  
Jaime A. Ramirez
Keyword(s):  
Genetic Algorithm ◽  
Hub And Spoke ◽  
Hub And Spoke Networks

scholarly journals Reverse Logistics Network Optimizing by Genetic Algorithm: A Case Study of Automotive Wiring Harnesses

2014 ◽  
Vol 564 ◽  
pp. 740-746 ◽  
Author(s):  
Abdolhossein Sadrnia ◽  
N. Ismail ◽  
M.K.A.M. Ariffin ◽  
Zulkifli Norzima ◽  
Omid Boyer
Keyword(s):  
Genetic Algorithm ◽  
Reverse Logistics ◽  
Programming Model ◽  
Mixed Integer ◽  
Profit Model ◽  
Logistics Network ◽  
Wiring Harness ◽  
Reverse Logistics Network ◽  
Optimum Solution

The shortage of material and environmental legislations have encouraged car manufacturers to recycle used material in end of life vehicles (ELVs), reverse logistics are essential to the concerns of the automotive supply chain. In this research, a profit model multi-echelon reverse logistics network including collection center, shredder center and recycling center is developed to recycle automotive parts. The work was continued by illustrating empirical application in wiring harness manufacturer that would like to recycle wire harnesses and extract copper. With regards to the complexity of the reverse logistics network, traditional method cannot be implemented for solving them. Thus, an evolutionary algorithm based genetic algorithm (GA) is applied as a solution methodology to solve mixed integer linear programming model and find the optimum solution. The results emphasize the efficiency of the modeling and solving method so that in the case study the company gained more than 27 thousand dollars through the establishment of reverse logistics for recycling copper.

scholarly journals Two-Echelon Multidepot Logistics Network Design with Resource Sharing

2021 ◽  
Vol 2021 ◽  
pp. 1-28
Author(s):  
Siyu Luo ◽  
Yong Wang ◽  
Jinjun Tang ◽  
Xiangyang Guan ◽  
Maozeng Xu
Keyword(s):  
Genetic Algorithm ◽  
Hybrid Method ◽  
Resource Sharing ◽  
Clustering Algorithm ◽  
Operating Cost ◽  
Pickup And Delivery ◽  
Nsga Ii ◽  
Logistics Network ◽  
Logistics Networks

Resource sharing within a logistics network offers an effective way to solve problems resulting from inefficient and costly operations of individual logistics facilities. However, the existing analysis of resource sharing and profit allocation is still limited. Therefore, this study aims to model resource sharing in two-echelon delivery and pickup logistics networks to improve the overall efficiency and decrease the total network operating cost. A bi-objective integer programming model is first proposed for two-echelon collaborative multidepot pickup and delivery problems with time windows (2E-CMDPDTW) to seek the minimization of operating costs and number of vehicles. Integrating a customer clustering algorithm, a greedy algorithm, and an improved nondominated sorting genetic algorithm-II (Im-NSGA-II), a hybrid method is then designed to handle the 2E-CMDPDTW model. The customer clustering and the greedy algorithms are employed to generate locally optimized initial solutions to accelerate the calculating velocity and guarantee the diversity of feasible solutions. The Im-NSGA-II combines the order crossover operation and the polynomial mutation process to find the optimal solution of the 2E-CMDPDTW. The comparative results show that the proposed hybrid method outperforms the NSGA-II and the multiobjective genetic algorithm. Furthermore, a Shapley value method is used for allocating total profits of established alliances and finding an optimal coalition sequence of the logistics facilities joining alliances based on the strictly monotonic path strategy. Finally, a case study of 2E-CMDPDTW in Chongqing China is conducted to validate the feasibility. Results indicate that this study contributes to long-term partnerships between logistics facilities within multi-echelon logistics networks in practice and contributes to the long-term sustainability of urban logistics pickup and delivery networks’ development.

Enterprise Logistics Network Optimization Based on Genetic Algorithm

2013 ◽  
Vol 744 ◽  
pp. 595-600 ◽  
Author(s):  
De Peng Liu ◽  
Chuan Jun Zhu ◽  
Wei Zhou
Keyword(s):  
Genetic Algorithm ◽  
Network Optimization ◽  
Distribution Center ◽  
Logistics System ◽  
Logistics Network ◽  
Logistics Networks ◽  
Tobacco Production ◽  
Logistic Cost ◽  
Enterprise Logistics ◽  
Geographical Characteristics

After the enterprise has developed to a certain scale, its logistics and supply chains will become more complex. For example,tobacco logistics network optimization of construction can effectively connect all websites of tobacco production, processing, and management.It can also promote lowest logistic cost and the highest efficiency. For building the best tobacco logistics networks, we need to set a scientific logistics nodes .This article established a mathematical model of a logistics network optimization according to existing logistics network system of a tobacco company and geographical characteristics. With the goal of total cost least in logistics system, the models builded up mathematical models which are based on the distance between each distribution center and each point of sales, unit freight, and shipment of each distribution center, in addition to the delivery requirements quantity of each point of sales.It discusses the design and implementation about the logistics network optimization based on genetic algorithm. Finally, it depthly analysised and verified the model and the solutions through cases.

scholarly journals Designing a Resilient and Sustainable Logistics Network under Epidemic Disruptions and Demand Uncertainty

2021 ◽  
Vol 13 (24) ◽  
pp. 14053
Author(s):  
Aymen Aloui ◽  
Nadia Hamani ◽  
Laurent Delahoche
Keyword(s):  
Demand Uncertainty ◽  
Programming Model ◽  
Mixed Integer ◽  
Design Strategies ◽  
Logistics Network ◽  
Location Allocation ◽  
Logistics Networks ◽  
Sustainable Logistics ◽  
Stochastic Mixed Integer Programming ◽  
Demand Fluctuations

To face the new challenges caused by modern industry, logistics operations managers need to focus more on integrating sustainability goals, adapt to unexpected disruptions and find new strategies and models for logistics management. The COVID-19 pandemic has proven that unforeseen fragilities, negatively affecting the supply chain performance, can arise rapidly, and logistics systems may confront unprecedented vulnerabilities regarding network structure disruption and high demand fluctuations. The existing studies on a resilient logistics network design did not sufficiently consider sustainability aspects. In fact, they mainly addressed the independent planning of decision-making problems with economic objectives. To fill this research gap, this paper concentrates on the design of resilient and sustainable logistics networks under epidemic disruption and demand uncertainty. A two-stage stochastic mixed integer programming model is proposed to integrate key decisions of location–allocation, inventory and routing planning. Moreover, epidemic disruptions and demand uncertainty are incorporated through plausible scenarios using a Monte Carlo simulation. In addition, two resiliency strategies, namely, capacity augmentation and logistics collaboration, are included into the basic model in order to improve the resilience and the sustainability of a logistics chain network. Finally, numerical examples are presented to validate the proposed approach, evaluate the performance of the different design models and provide managerial insights. The obtained results show that the integration of two design strategies improves resilience and sustainability.

Research on Optimizing Reverse Logistics Network of Waste Copper Recycling

2013 ◽  
Vol 798-799 ◽  
pp. 967-970
Author(s):  
Qi Hao ◽  
Xin Sheng Xu ◽  
Qing Liu ◽  
Xi Ze Liu
Keyword(s):  
Reverse Logistics ◽  
Network Models ◽  
Mathematic Model ◽  
Third Party ◽  
Third Party Logistics ◽  
Logistics Network ◽  
Logistics Networks ◽  
Reverse Logistics Network ◽  
Scrap Recycling

Based on third-party logistics, we propose a linear programming mathematic model for reverse logistics networks of waste copper recycling. And, a reverse logistics network models is established to deal with returned products, which will decrease costs and allow optimal decisions on distributing methods so as to increase efficiency and profit levels. A case study is also developed to illustrating the value of this model and network. Whats more, according to the model, we put forward to sincere suggestions and better measures of copper scrap recycling.

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