A messy genetic algorithm for the vehicle routing problem with time window constraints

2002 ◽  
Author(s):  
K.C. Tan ◽  
T.H. Lee ◽  
K. Ou ◽  
L.H. Lee
Keyword(s):  
Genetic Algorithm ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Time Window ◽  
Routing Problem ◽  
Time Window Constraints

Recent progress of local search in handling the time window constraints of the vehicle routing problem

4OR ◽  
2010 ◽  
Vol 8 (3) ◽  
pp. 221-238 ◽  
Author(s):  
Hideki Hashimoto ◽  
Mutsunori Yagiura ◽  
Shinji Imahori ◽  
Toshihide Ibaraki
Keyword(s):  
Local Search ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Recent Progress ◽  
Time Window ◽  
Routing Problem ◽  
Time Window Constraints

scholarly journals VEHICLE ROUTING PROBLEM WITH TIME WINDOWS USING HYBRID ENCODING GENETIC ALGORITHM

2014 ◽  
Vol 12 (10) ◽  
pp. 3945-3951
Author(s):  
Dr P.K Chenniappan ◽  
Mrs.S.Aruna Devi
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Time Window ◽  
Time Windows ◽  
Minimal Cost ◽  
Total Size ◽  
Routing Problem ◽  
Time Window Constraints ◽  
Vehicle Capacity ◽  
Vehicle Routes

The vehicle routing problem is to determine K vehicle routes, where a route is a tour that begins at the depot, traverses a subset of the customers in a specified sequence and returns to the depot. Each customer must be assigned to exactly one of the K vehicle routes and total size of deliveries for customers assigned to each vehicle must not exceed the vehicle capacity. The routes should be chosen to minimize total travel cost. Thispapergivesasolutiontofindanoptimumrouteforvehicle routingproblem using Hybrid Encoding GeneticAlgorithm (HEGA)technique tested on c++ programming.The objective is to find routes for the vehicles to service all the customers at a minimal cost and time without violating the capacity, travel time constraints and time window constraints

scholarly journals A Bucket Graph–Based Labeling Algorithm with Application to Vehicle Routing

2020 ◽  
Author(s):  
Ruslan Sadykov ◽  
Eduardo Uchoa ◽  
Artur Pessoa
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Resource Constraints ◽  
Time Window ◽  
Time Windows ◽  
Routing Problem ◽  
Constrained Vehicle Routing ◽  
Time Window Constraints ◽  
Vehicle Capacity ◽  
First Time

We consider the shortest path problem with resource constraints arising as a subproblem in state-of-the-art branch-cut-and-price algorithms for vehicle routing problems. We propose a variant of the bidirectional label-correcting algorithm in which the labels are stored and extended according to the so-called bucket graph. This organization of labels helps to significantly decrease the number of dominance checks and the running time of the algorithm. We also show how the forward/backward route symmetry can be exploited and how to eliminate arcs from the bucket graph using reduced costs. The proposed algorithm can be especially beneficial for vehicle routing instances with large vehicle capacity and/or with time window constraints. Computational experiments were performed on instances from the distance-constrained vehicle routing problem, including multidepot and site-dependent variants, on the vehicle routing problem with time windows, and on the “nightmare” instances of the heterogeneous fleet vehicle routing problem. Significant improvements over the best algorithms in the literature were achieved, and many instances could be solved for the first time.

A hybrid genetic algorithm for multi-depot vehicle routing problem with considering time window repair and pick-up

2018 ◽  
Vol 13 (3) ◽  
pp. 698-717 ◽  
Author(s):  
Masoud Rabbani ◽  
Pooya Pourreza ◽  
Hamed Farrokhi-Asl ◽  
Narjes Nouri
Keyword(s):  
Genetic Algorithm ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Heuristic Algorithms ◽  
Time Window ◽  
Hybrid Genetic Algorithm ◽  
Variable Cost ◽  
Routing Problem ◽  
Content Type ◽  
Simple Genetic Algorithm

Purpose This paper, considers the multi-depot vehicle routing problem with time window considering two repair and pickup vehicles (CMDVRPTW). Design/methodology/approach The objective of this problem is minimization of the total traveling cost and the time window violations. Two meta-heuristic algorithms, namely, simple genetic algorithm (GA) and hybrid genetic algorithm (HGA) are used to find the best solution for this problem. A comparison on the results of these two algorithms has been done and based on the outcome, it has been proved that HGA has better performance than GA. Findings A comparison on the results of these two algorithms has been done and based on the outcome, it has been proved that HGA has better performance than GA. Originality/value This paper, considers the multi-depot vehicle routing problem with time window considering two repair and pickup vehicles (CMDVRPTW). The defined problem is a practical problem in the supply management and logistic. The repair vehicle services the customers who have goods, while the pickup vehicle visits the customer with nonrepaired goods. All the vehicles belong to an internal fleet of a company and have different capacities and fixed/variable cost. Moreover, vehicles have different limitations in their time of traveling. The objective of this problem is minimization of the total traveling cost and the time window violations. Two meta-heuristic algorithms (simple genetic algorithm and hybrid one) are used to find the best solution for this problem.

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