A Hybrid Genetic Algorithm-Simulated Annealing Approach for the Multi-Objective Vehicle Routing Problem with Time Windows

2011 ◽  
pp. 79-97
Author(s):  
Gülfem Tuzkaya ◽  
Bahadir Gülsün ◽  
Ender Bildik ◽  
E. Gözde Çaglar
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Time Windows ◽  
Hybrid Genetic Algorithm ◽  
Benchmark Problems ◽  
Routing Problem ◽  
Solution Approach ◽  
Multi Objective

In this study, the vehicle routing problem with time windows (VRPTW) is investigated and formulated as a multi-objective model. As a solution approach, a hybrid meta-heuristic algorithm is proposed. Proposed algorithm consists of two meta-heuristics: Genetic Algorithm (GA) and Simulated Annealing (SA). In this algorithm, SA is used as an improvement operator in GA. Besides, a hypothetical application is presented to foster the better understanding of the proposed model and algorithm. The validity of the algorithm is tested via some well-known benchmark problems from the literature.

scholarly journals Hybrid Genetic Algorithms and Simulated Annealing for Multi-trip Vehicle Routing Problem with Time Windows

2018 ◽  
Vol 8 (6) ◽  
pp. 4713 ◽  
Author(s):  
Amalia Kartika Ariyani ◽  
Wayan Firdaus Mahmudy ◽  
Yusuf Priyo Anggodo
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Computational Experiment ◽  
Time Windows ◽  
Hard Problem ◽  
Routing Problem ◽  
Search Area ◽  
Np Hard Problem

Vehicle routing problem with time windows (VRPTW) is one of NP-hard problem. Multi-trip is approach to solve the VRPTW that looking trip scheduling for gets best result. Even though there are various algorithms for the problem, there is opportunity to improve the existing algorithms in order gaining a better result. In this research, genetic algoritm is hybridized with simulated annealing algoritm to solve the problem. Genetic algoritm is employed to explore global search area and simulated annealing is employed to exploit local search area. Four combination types of genetic algorithm and simulated annealing (GA-SA) are tested to get the best solution. The computational experiment shows that GA-SA1 and GA-SA4 can produced the most optimal fitness average values with each value was 1.0888 and 1.0887. However GA-SA4 can found the best fitness chromosome faster than GA-SA1.

scholarly journals A Hybrid Multi-objective Genetic Algorithm for Bi-objective Time Window Assignment Vehicle Routing Problem

2019 ◽  
Vol 31 (5) ◽  
pp. 513-525
Author(s):  
Manman Li ◽  
Jian Lu ◽  
Wenxin Ma
Keyword(s):  
Genetic Algorithm ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Time Window ◽  
Time Windows ◽  
Routing Problem ◽  
Multi Objective ◽  
Objective Time ◽  
Multi Objective Genetic Algorithm ◽  
Positive Effect

Providing a satisfying delivery service is an important way to maintain the customers’ loyalty and further expand profits for manufacturers and logistics providers. Considering customers’ preferences for time windows, a bi-objective time window assignment vehicle routing problem has been introduced to maximize the total customers’ satisfaction level for assigned time windows and minimize the expected delivery cost. The paper designs a hybrid multi-objective genetic algorithm for the problem that incorporates modified stochastic nearest neighbour and insertion-based local search. Computational results show the positive effect of the hybridization and satisfactory performance of the metaheuristics. Moreover, the impacts of three characteristics are analysed including customer distribution, the number of preferred time windows per customer and customers’ preference type for time windows. Finally, one of its extended problems, the bi-objective time window assignment vehicle routing problem with time-dependent travel times has been primarily studied.

scholarly journals Tabu Search for the Vehicle Routing Problem with Multiple Trips, Time Windows, and Simultaneous Delivery-Pickup

2018 ◽  
Vol 19 (2) ◽  
pp. 75
Author(s):  
Suprayogi Suprayogi ◽  
Yusuf Priyandari
Keyword(s):  
Genetic Algorithm ◽  
Tabu Search ◽  
Local Search ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Objective Function Value ◽  
Time Windows ◽  
Routing Problem ◽  
Solution Approach ◽  
Simultaneous Delivery

This paper discusses a vehicle routing problem with multiple trips, time windows, and simultaneous delivery-pickup (VRPMTTWSDP). This problem is a variant of the basic vehicle routing problem (VRP) including the following characteristics: multiple trips, time windows, and simultaneous delivery-pickup.  In this paper, a solution approach based on tabu search (TS) is proposed. In the proposed TS, the sequential insertion (SI) algorithm is used to construct an initial solution. A neighbor structure is generated by applying an operator order consisting of eleven operators of relocation, exchange, and crossover operators. A tabu solution code (TSC) method is applied as a tabu restriction mechanism. Computational experiments are carried out to examine the performance of the proposed TS using hypothetical instances. The performance of the proposed TS is compared to the local search (LS) and the genetic algorithm (GA). The comparison shows that the proposed TS is better in terms of the objective function value.

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