Optimization of the Transportation Problem in the Covid Pandemic with Time-Window Vehicle Routing Problem
- DOI
- 10.2991/aebmr.k.211119.024How to use a DOI?
- Keywords
- Vehicle Routing Problem - VRP; Vehicle Routing Problem with Time Window - VRPTW; Genetic Algorithm; Optimization
- Abstract
Logistics is one of the most important factors in any country and it has become a crucial topic due to the significant effect of Covid 19 on the world economics. Since the beginning of time, logistics is a critical link for products circulation throughout the world. When the Covid 19 pandemic came, logistics is becoming an essential role in preventing supply chain disruption. Meanwhile, transportation activity accounts for the highest portion in logistics. Therefore, improving the transportation system and optimizing the delivery method of goods are essential not only in the pandemic but also in any normal business situation. This paper focuses on optimizing the vehicle routing problem with the time window (VRPTW) by Genetic algorithm (GA) since the receiving time of the customer has some boundaries and restrictions. Furthermore, our VRPTW model include vehicle selection algorithm, which will automatically select the most efficient fleet from the set of vehicles with different capacity. This paper analyzes the application of the algorithm to GK logistics – a Vietnamese logistics company, compared to common direct transportation. The result proves that VRPTW is more efficient in term of distance, travel time and vehicle used.
Research purpose:
There are many transportation problems such as time constraints, capacity constraints, pick-up, and delivery constraints, which create many difficulties for transport routers. Finding the vehicle routing is always big issue of any logistics company and manufactures not only in the pandemic but also in any normal business situation. Many enterprises should determine vehicles route allocation to minimize costs by considering capacity and time windows of customers’ requirements.
Therefore, this paper aims to develop the vehicle routing problem with time windows and selecting the appropriate fleet of trucks to minimize the total distribution cost through finding optimal routes and satisfy the customer demand at specific of receiving time. In this article, the genetic algorithm is applied to solve and optimize vehicle routing problem with hard-time window. The algorithm will be tested and compared with direct transportation method in experiment section.
Vehicle routing problem is based on integrating vehicles that deliver goods in the transport network affected by the time. The time window refers to time slot when the fleet reaches the customer’s node. Two types of time windows impose each vehicle delivering the goods to the customers within a specific time interval. The vehicle may not arrive at the customer’s node closing time and before node open time. The best route must be on time and follow disciplined capacity as well as minimum distance and cost.
Research motivation:
In recent years, E-commerce is developing rapidly in all over the world. Transportation plays an important role in delivering goods to customer and has a close relationship with E-commerce. Vietnam is among Asian countries having fastest growth in E-commerce. E-commerce has been developed sharply in recent years, since more and more people using the internet to purchase goods. Especially, the percentage of people doing online shopping has increased much more in the Covid 19 pandemic. When customers place orders from supermarkets and E-commerce companies, the supermarkets or E-commerce companies make a plan for delivery. They might manage the transportation activities or outsource to a 3PL provider. Furthermore, Logistics plays an important role in preventing broken supply chain. Transportation should manage the operation not only to meet the customer requirements at the right place, right time, and provide the right quality and but also to minimize the operation costs. As a result, providing cost-effective and efficient service is the top target of any logistics enterprise. In addition, the required receiving time should be strict during the pandemic thus the time window of receiving order also should be included in the research. Therefore, the motivation of this paper is developing the transportation model that minimize transportation distance, optimize capacity but still can meet the customers’ requirements in term of time windows and transportation quantity.
Research design, approach and method:
This research develops the optimization model for vehicle routing problem with added constraints of time windows and selection of fleets. The problem of vehicle routing with time window constraint and fleet selection is non -linear programming, hence it is difficult to solve. As a result, we develop genetic algorithm to solve the problem faster and address more complicated distribution network. A downside of the classic vehicle routing model is the initial fixed capacity fleet condition. This problem causes reducing of flexibility when choosing vehicles to deliver to customers. This research has tried with actual data, the fixed capacity fleet has not as high fill rate as expected. Therefore, the research paper improves this problem with a vehicle selection model. Basically, the model helps us to choose the trucks with capacity approximately satisfying to the customer’s requirements, thereby helping to reduce cost, increase the occupancy rate, simplify the transport management process. This research uses real data from GK Logistics company to test the model and derive applicable results in term of traveling distance, fleet selection and time control.
Main findings:
From the result of research, it reveals that the complex transportation issue during the pandemic can be solved with vehicle routing problem with time window control. Since the labor resources for transportation is limited and each route requires extra cost for testing virus, then the minimum vehicles used with minimum waiting time and late time can be solve and optimized. The model has been tested with data from GK Logistics company, the result shows that total travel distance has been reduced by 34.77% while the demand of customer still meet. Although direct transportation eliminates the waiting time and late time, the total time in VRPTW is still smaller than direct transportation, which decreases 10.09%. Quantity of vehicles mobilized also significantly reduced. From the implementation of research, it is evident that direct transportation will cost GK logistics more time to transport, especially for time-sensitive goods, leading to a drop in supply chain performance and customer service. On the other hand, VRPTW with GA, which surpass direct transportation in all important metrics, could be a good solution not only for GK Logistics Company but also for other Logistics companies and other manufacturers that have transportation activity.
Practical/managerial implications:
The pandemic has been affected much to whole Vietnam especially in Ho Chi Minh, Binh Duong, Dong Nai, Ha Noi, Bac Ninh and Bac Giang since the beginning of the pandemic. The supply chain has been broken and delayed especially in terms of transportation. As a result, logistics becomes a critical link for products circulation in the whole country and becoming an essential role in preventing supply chain disruption. In this paper, we optimize the vehicle routing problem with the time window (VRPTW) by Genetic algorithm (GA) to solve the transportation issue with some boundaries and restrictions on receiving time. Furthermore, our VRPTW model include vehicle selection algorithm, which will automatically select the most efficient fleet from the set of vehicles having different capacity. This paper analyzes application of the algorithm to GK logistics company. The result proved that VRPTW is more efficient in term of distance, travel time and vehicle used that could be applied both during and beyond the pandemic. The study shows the effectiveness of the genetic algorithm in optimization problems, especially solving the complex transportation issue. During the pandemic, the labor resources for transportation is limited and each route requires extra cost for testing Covid-19, then the minimum vehicles used with minimum waiting time and late time need to be solve and optimized. The research team has established optimization model of VRPTW using GA and demonstrated the effectiveness of the program, which could be applied widely to other field of transportation management. Some of the most useful applications of the VRPTW include postal deliveries, national franchise restaurant services, school bus routing, security patrol services, and JIT (just in time) manufacturing.
- Copyright
- © 2021 The Authors. Published by Atlantis Press International B.V.
- Open Access
- This is an open access article under the CC BY-NC license.
Cite this article
TY - CONF AU - Nguyen Thi Xuan HOA AU - Vu Hai ANH AU - Nguyen Quang ANH AU - Nguyen Dac Viet HA PY - 2021 DA - 2021/12/07 TI - Optimization of the Transportation Problem in the Covid Pandemic with Time-Window Vehicle Routing Problem BT - Proceedings of the International Conference on Emerging Challenges: Business Transformation and Circular Economy (ICECH 2021) PB - Atlantis Press SP - 237 EP - 245 SN - 2352-5428 UR - https://doi.org/10.2991/aebmr.k.211119.024 DO - 10.2991/aebmr.k.211119.024 ID - HOA2021 ER -