Find Optimal Routes for Vehicles: Big Data Analytic Approach - Essay Sample

Introduction

The Vehicle Routing problem goal is to find optimal routes for multiple vehicles which are going to a set of different locations (Capelle et al., 2019). In the transportation industry, sound business value mostly relies on the way the transport route designed.

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Article : Solving Vehicle Routing Problem: A Big Data Analytic Approach

A logical routing plan will enable the vehicles to transport the required tasks at minimum time and also cost while covering the shortest time possible order for one to develop a satisfactory routing plan one must improve the efficiency of the fleet of vehicles to traverse in order to be able to deliver to some of the customers (Zheng, 2019).

Usefulness

Article: A Column Generation Approach For Location-Routing Problems With Pickup And Delivery

The value of Dynamic VRP model cannot fix but changes. The relationship between the vehicle and the customer, together with the road route changes. The VRP can serve customers who can either be at the same place or located at different horizons (Capelle et al., 2019). Each vehicle delivers the items to the customers by one visit, and it later returns to the source after delivering the goods.

Article : Solving Vehicle Routing Problem: A Big Data Analytic Approach

It states, customers visited through a route of networks, and the transport cost I, also minimized under the limitations on time travel, the load on the vehicle and also the time frame. In standard VRP where it allows time window and travelling limitations treated as cost functions, and when all is generalized, the problem becomes general (Zheng, 2019). The transport cost is friendly to the customer since based on the total number of vehicles and also the number of miles covered

Criticisms

Article: A Column Generation Approach For Location-Routing Problems With Pickup And Delivery

The Dynamic VRP Model is time-consuming and expensive since the vehicles used to deliver is one, and it tends just to pick up the item and take it to its owner instead of using one vehicle to carry all the items and supply to its various owners at different grounds (Zheng, 2019).

Article : Solving Vehicle Routing Problem: A Big Data Analytic Approach

The Standard VRP runs on a loss since it reduces the cost of transporting the items to various owners of which the mileage incurred may be far away, and at this point, the company`s fuel consumed (Zheng, 2019).

Ways to improve

Article: A Column Generation Approach For Location-Routing Problems With Pickup And Delivery

In order to improve the Location Routing Problem, one has to allow flexibility in delivering the amount required. It proposes a new algorithm that can combine and solve the problem effectively this is by formulating a problem that integrates the PDP, and the optimum depot location and the solution is proposed based on the column scheme within its official frameworks, which recognizes as the B& P (Capelle et al., 2019).

Article : Solving Vehicle Routing Problem: A Big Data Analytic Approach

In VRP, the path problems are the most recognized, in order to improve it, is by modified versions of algorithms that involves including a label to each route and extending it (Zheng, 2019). To indicate the practicality of the resources that include time and also the vehicle, one has to avoid calculating a set of routes to end the procedure when a set of columns with negative reduced costs is determined.

Criticisms

Article: A Column Generation Approach For Location-Routing Problems With Pickup And Delivery

When the VRP includes a label to each route, will not help reduce the cost of using the resources in order to improve on the cost-effectiveness of the business and also Location Routing Problem should invent ways of curbing the problems at once by use of proper and practical algorithms.

Article : Solving Vehicle Routing Problem: A Big Data Analytic Approach

The VRP should also have cost-friendly to the customers, and it will help increase the cost-effectiveness of the company since the consumers will be many (Zheng, 2019).

References

Capelle, T., Cortés, C. E., Gendreau, M., Rey, P. A., & Rousseau, L.-M. (2019). A column generation approach for location-routing problems with pickup and delivery. European Journal of Operational Research, 272(1), 121–131. https://doi-org.proxygsu-gamc.galileo.usg.edu/10.1016/j.ejor.2018.05.055

Zheng, S. (2019). Solving Vehicle Routing Problem: A Big Data Analytic Approach. IEEE Access, 7, 169565-169570. https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8910558