The Real-Time Vehicle Routing Problem

2008 ◽  
pp. 141-146 ◽  
Author(s):  
Irena Okhrin ◽  
Knut Richter
Keyword(s):  
Vehicle Routing ◽  
Real Time ◽  
Vehicle Routing Problem ◽  
Routing Problem ◽  
The Real

On-Line Vehicle Routing with Time Windows: Optimization-Based Heuristics Approach for Freight Demands Requested in Real-Time

1998 ◽  
Vol 1617 (1) ◽  
pp. 171-178 ◽  
Author(s):  
How-Ming Shieh ◽  
Ming-Der May
Keyword(s):  
Local Search ◽  
Vehicle Routing ◽  
Real Time ◽  
Vehicle Routing Problem ◽  
Time Windows ◽  
Benchmark Problems ◽  
Routing Problem ◽  
Anytime Algorithm ◽  
Search Heuristics ◽  
On Line

The problem of the on-line version of the vehicle routing problem with time windows (VRPTW) differs from the traditional off-line problem in the dynamical arrival of requests and the execution of the partial tour during the run time. The study develops an on-line optimization-based heuristic that combined the concepts of the “on-line algorithm,” “anytime algorithm,” and local search heuristics to solve the on-line version of VRPTW. The solution heuristic is evaluated with modified Solomon’s problems. By comparing with these benchmark problems, the different results between on-line and off-line algorithms are indicated.

scholarly journals Evolving heuristics for Dynamic Vehicle Routing with Time Windows using genetic programming

2021 ◽  
Author(s):  
Josiah Jacobsen-Grocott ◽  
Yi Mei ◽  
Gang Chen ◽  
Mengjie Zhang
Keyword(s):  
Genetic Programming ◽  
Vehicle Routing ◽  
Real Time ◽  
Vehicle Routing Problem ◽  
Time Windows ◽  
Dynamic Vehicle Routing ◽  
Routing Problem ◽  
Real World Applications ◽  
Personal Use ◽  
Optimisation Methods

Dynamic vehicle routing problem with time windows is an important combinatorial optimisation problem in many real-world applications. The most challenging part of the problem is to make real-time decisions (i.e. whether to accept the newly arrived service requests or not) during the execution of the routes. It is hardly applicable to use the optimisation methods such as mathematical programming and evolutionary algorithms that are competitive for static problems, since they are usually time-consuming, and cannot give real-time responses. In this paper, we consider solving this problem using heuristics. A heuristic gradually builds a solution by adding the requests to the end of the route one by one. This way, it can take advantage of the latest information when making the next decision, and give immediate response. In this paper, we propose a meta-algorithm to generate a solution given any heuristic. The meta-algorithm maintains a set of routes throughout the scheduling horizon. Whenever a new request arrives, it tries to re-generate new routes to include the new request by the heuristic. It accepts the new request if successful, and reject otherwise. Then we manually designed several heuristics, and proposed a genetic programming-based hyper-heuristic to automatically evolve heuristics. The results showed that the heuristics evolved by genetic programming significantly outperformed the manually designed heuristics. © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

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