scholarly journals Integrated Tomato Picking and Distribution Scheduling Based on Maturity

2020 ◽  
Vol 12 (19) ◽  
pp. 7934
Author(s):  
Anqi Zhu ◽  
Bei Bian ◽  
Yiping Jiang ◽  
Jiaxiang Hu
Keyword(s):  
Genetic Algorithm ◽  
Time Windows ◽  
Joint Optimization ◽  
Adaptive Genetic Algorithm ◽  
Maturity Model ◽  
Improved Genetic Algorithm ◽  
Routing Problem ◽  
Order Processing ◽  
Supply Chain Efficiency ◽  
Distribution Scheduling

Agriproducts have the characteristics of short lifespan and quality decay due to the maturity factor. With the development of e-commerce, high timelines and quality have become a new pursuit for agriproduct online retailing. To satisfy the new demands of customers, reducing the time from receiving orders to distribution and improving agriproduct quality are significantly needed advancements. In this study, we focus on the joint optimization of the fulfillment of online tomato orders that integrates picking and distribution simultaneously within the context of the farm-to-door model. A tomato maturity model with a firmness indicator is proposed firstly. Then, we incorporate the tomato maturity model function into the integrated picking and distribution schedule and formulate a multiple-vehicle routing problem with time windows. Next, to solve the model, an improved genetic algorithm (the sweep-adaptive genetic algorithm, S-AGA) is addressed. Finally, we prove the validity of the proposed model and the superiority of S-AGA with different numerical experiments. The results show that significant improvements are obtained in the overall tomato supply chain efficiency and quality. For instance, tomato quality and customer satisfaction increased by 5% when considering the joint optimization, and the order processing speed increased over 90% compared with traditional GA. This study could provide scientific tomato picking and distribution scheduling to satisfy the multiple requirements of consumers and improve agricultural and logistics sustainability.

scholarly journals An Improved Genetic Algorithm for Vehicle Routing Problem Pick-up and Delivery with Time Windows

2021 ◽  
Vol 22 (1) ◽  
pp. 1-17
Author(s):  
Muhammad Faisal Ibrahim ◽  
M.M Putri ◽  
D Farista ◽  
Dana Marsetiya Utama
Keyword(s):  
Genetic Algorithm ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Time Windows ◽  
Optimal Solution ◽  
Improved Genetic Algorithm ◽  
Closing Time ◽  
Routing Problem ◽  
Logistics Company ◽  
Fitness Value

Vehicle Routing Problem (VRP) has many applications in real systems, especially in distribution and transportation. The optimal determination of vehicle routes impacts increasing economic interests. This research aims to find the optimal solution in Vehicle Routing Problem Pick-up and Delivery with Time Windows (VRPPDTW).  Targets of this problem included reducing distance travel and penalties. Three penalties that were considered are a capacity penalty, opening time capacity, and closing time capacity. An improved genetic algorithm was developed and used to determine the vehicle route.  There were one main depot and 42 customers. This research raised the problem of a shipping and logistics company. Analysis of the results showed that the proposed route obtained from improved genetic algorithms (GA) was better than the existing route and previous algorithm. Besides, this research was carried out an analysis on the effect of the number of iterations on distance traveled, the number of penalties, and the fitness value. This algorithm could be applied in VRPPDTW and produces an optimal solution.

scholarly journals Vehicle Routing Problem with Soft Time Windows Based on Improved Genetic Algorithm for Fruits and Vegetables Distribution

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Peiqing Li ◽  
Jie He ◽  
Dunyong Zheng ◽  
Yongsheng Huang ◽  
Chenhao Fan
Keyword(s):  
Genetic Algorithm ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Fruits And Vegetables ◽  
Time Windows ◽  
Improved Genetic Algorithm ◽  
Routing Problem ◽  
Total Cost ◽  
Soft Time Windows ◽  
Distribution Process

Fresh fruits and vegetables, perishable by nature, are subject to additional deterioration and bruising in the distribution process due to vibration and shock caused by road irregularities. A nonlinear mathematical model was developed that considered not only the vehicle routing problem with time windows but also the effect of road irregularities on the bruising of fresh fruits and vegetables. The main objective of this work was to obtain the optimal distribution routes for fresh fruits and vegetables considering different road classes with the least amount of logistics costs. An improved genetic algorithm was used to solve the problem. A fruit delivery route among the 13 cities in Jiangsu Province was used as a real analysis case. The simulation results showed that the vehicle routing problem with time windows, considering road irregularities and different classes of toll roads, can significantly influence total delivery costs compared with traditional VRP models. The comparison between four models to predict the total cost and actual total cost in distribution showed that the improved genetic algorithm is superior to the Group-based pattern, CW pattern, and O-X type cross pattern.

scholarly journals MULTI-OBJECTIVE DISTRIBUTION ROUTING OPTIMIZATION WITH TIME WINDOW BASED ON IMPROVED GENETIC ALGORITHM

2018 ◽  
Vol 48 (3) ◽  
pp. 151-156
Author(s):  
S. WU ◽  
C. CHEN
Keyword(s):  
Genetic Algorithm ◽  
Time Window ◽  
Time Windows ◽  
Optimal Solution ◽  
Transportation Cost ◽  
Population Diversity ◽  
Improved Genetic Algorithm ◽  
Routing Problem ◽  
Logistics Distribution ◽  
Routing Optimization

In order to solve the shortcomings of the traditional genetic algorithm in solving the problem of logistics distribution path, a modified genetic algorithm is proposed to solve the Vehicle Routing Problem with Time Windows (VRPTW) under the condition of vehicle load and time window. In the crossover process, the best genes can be preserved to reduce the inferior individuals resulting from the crossover, thus improving the convergence speed of the algorithm. A mutation operation is designed to ensure the population diversity of the algorithm, reduce the generation of infeasible solutions, and improve the global search ability of the algorithm. The algorithm is implemented on Matlab 2016a. The example shows that the improved genetic algorithm reduces the transportation cost by about 10% compared with the traditional genetic algorithm and can jump out of the local convergence and obtain the optimal solution, thus providing a more reasonable vehicle route.

scholarly journals A Joint Decision-Making Approach for Tomato Picking and Distribution Considering Postharvest Maturity

Agronomy ◽  
2020 ◽  
Vol 10 (9) ◽  
pp. 1330
Author(s):  
Zhaotong Zhang ◽  
Bei Bian ◽  
Yiping Jiang
Keyword(s):  
Consumer Satisfaction ◽  
Time Windows ◽  
Solid Content ◽  
Distribution Model ◽  
Soluble Solid Content ◽  
Maturity Model ◽  
Improved Genetic Algorithm ◽  
Joint Decision ◽  
Distribution Scheduling ◽  
Soluble Solid

Fruit maturity is an essential factor for fresh retailers to make economical distribution scheduling and scientific market strategies. In the context of farm-to-door mode, the fresh retailers could incorporate the postharvest maturity time, picking time and distribution time to deliver high-quality fruits to consumers. This study selects climacteric tomato fruits and formulates a postharvest maturity model by capturing the firmness and soluble solid content (SSC) data during maturing. A joint picking and distribution model is proposed to ensure tomatoes could arrive at consumers within expected maturity time windows. To improve the feasibility of proposed model, an improved genetic algorithm (IGA) is designed to obtain solutions. The results demonstrate that the joint model could optimize the distribution routing to improve consumer satisfaction and reduce the order fulfillment costs. The proposed method provides precise guidance for tomato online retailers by taking advantage of postharvest maturity data, which is conducive to sustainable development of fresh e-ecommerce.

An Improved Genetic Algorithm for Multi-Depot Heterogeneous Vehicle Routing Problem with Simultaneous Pickup and Delivery Time Windows

2015 ◽  
Vol 738-739 ◽  
pp. 361-365 ◽  
Author(s):  
Yan Guang Cai ◽  
Ya Lian Tang ◽  
Qi Jiang Yang
Keyword(s):  
Genetic Algorithm ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Time Windows ◽  
Pickup And Delivery ◽  
Improved Genetic Algorithm ◽  
Routing Problem ◽  
Heterogeneous Vehicle Routing Problem ◽  
Simultaneous Pickup And Delivery ◽  
Elite Selection

Multi-depot heterogeneous vehicle routing problem with simultaneous pickup and delivery and time windows (MDHVRPSPDTW) is an extension of vehicle routing problem (VRP), MDHVRPSPDTW mathematical model was established. The improved genetic algorithm (IGA) is proposed for solving the model. Firstly, MDHVRPSPDTW is transferred into different groups by the seed customer selecting method and scanning algorithm (SA).Secondly, IGA based on elite selection and inversion operator is used to solve the model, and then cutting merge strategy based on greedy thought and three kinds of neighborhood search methods is applied to optimize the feasible solutions further. Finally, 3-opt local search is applied to adjust the solution. The proposed IGA has been test on a random new numerical example.The computational results show that IGA is superior to branch and bound algorithm (BBD) by Lingo 9.0 in terms of optimum speed and solution quality, and the model and the proposed approach is effective and feasible.

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