scholarly journals A SOLUTION FOR THE BI-OBJECTIVE VEHICLE ROUTING PROBLEM WITH TIME WINDOWS USING LOCAL SEARCH AND GENETIC ALGORITHMS

2016 ◽  
Vol 6 (2) ◽  
pp. 149-158 ◽  
Author(s):  
Anan Mungwattana ◽  
Tharinee Manisri ◽  
Kanjanaporn Charoenpol ◽  
Gerrit K. Janssens
Keyword(s):  
Genetic Algorithms ◽  
Local Search ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Time Windows ◽  
Routing Problem

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.

A Fast Evolutionary Metaheuristic for the Vehicle Routing Problem with Time Windows

2003 ◽  
Vol 12 (02) ◽  
pp. 153-172 ◽  
Author(s):  
Olli Bräysy ◽  
Wout Dullaert
Keyword(s):  
Simulated Annealing ◽  
Local Search ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Time Windows ◽  
Computation Time ◽  
Routing Problem ◽  
Time Consumption ◽  
Evolutionary Metaheuristic ◽  
Made In

This paper presents a new evolutionary metaheuristic for the vehicle routing problem with time windows. Ideas on multi-start local search, ejection chains, simulated annealing and evolutionary computation are combined in a heuristic that is both robust and efficient. The proposed method produces results that are on par with the current best solutions, at a fraction of the computation time needed by previous best approaches. Given the significantly lower time consumption over methods proposed previously in the literature, we consider our approach especially useful in dynamic routing problems, where decisions must be made in real-time.

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