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 A Modified Harmony Search Algorithm for Solving the Dynamic Vehicle Routing Problem with Time Windows

2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Shifeng Chen ◽  
Rong Chen ◽  
Jian Gao
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Search Algorithm ◽  
Time Windows ◽  
Harmony Search ◽  
Population Diversity ◽  
Benchmark Problems ◽  
Dynamic Vehicle Routing ◽  
Dynamic Vehicle Routing Problem ◽  
Routing Problem

The Vehicle Routing Problem (VRP) is a classical combinatorial optimization problem. It is usually modelled in a static fashion; however, in practice, new requests by customers arrive after the initial workday plan is in progress. In this case, routes must be replanned dynamically. This paper investigates the Dynamic Vehicle Routing Problem with Time Windows (DVRPTW) in which customers’ requests either can be known at the beginning of working day or occur dynamically over time. We propose a hybrid heuristic algorithm that combines the harmony search (HS) algorithm and the Variable Neighbourhood Descent (VND) algorithm. It uses the HS to provide global exploration capabilities and uses the VND for its local search capability. In order to prevent premature convergence of the solution, we evaluate the population diversity by using entropy. Computational results on the Lackner benchmark problems show that the proposed algorithm is competitive with the best existing algorithms from the literature.

scholarly journals Solving Practical Vehicle Routing Problem with Time Windows Using Metaheuristic Algorithms

1970 ◽  
Vol 24 (4) ◽  
pp. 343-351 ◽  
Author(s):  
Filip Taner ◽  
Ante Galić ◽  
Tonči Carić
Keyword(s):  
Vehicle Routing ◽  
Real World ◽  
Vehicle Routing Problem ◽  
Time Windows ◽  
Iterated Local Search ◽  
Metaheuristic Algorithms ◽  
Benchmark Problems ◽  
Routing Problem ◽  
Constructive Heuristics ◽  
Initial Feasible Solution

This paper addresses the Vehicle Routing Problem with Time Windows (VRPTW) and shows that implementing algorithms for solving various instances of VRPs can significantly reduce transportation costs that occur during the delivery process. Two metaheuristic algorithms were developed for solving VRPTW: Simulated Annealing and Iterated Local Search. Both algorithms generate initial feasible solution using constructive heuristics and use operators and various strategies for an iterative improvement. The algorithms were tested on Solomon’s benchmark problems and real world vehicle routing problems with time windows. In total, 44 real world problems were optimized in the case study using described algorithms. Obtained results showed that the same distribution task can be accomplished with savings up to 40% in the total travelled distance and that manually constructed routes are very ineffective.

Tabu Search heuristics for the Vehicle Routing Problem with Time Windows

Top ◽  
2002 ◽  
Vol 10 (2) ◽  
pp. 211-237 ◽  
Author(s):  
Olli Bräysy ◽  
Michel Gendreau
Keyword(s):  
Tabu Search ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Time Windows ◽  
Routing Problem ◽  
Search Heuristics

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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