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.

Optimization of Vehicle Routing Problem Based on Multi-Objective Genetic Algorithm

2012 ◽  
Vol 253-255 ◽  
pp. 1356-1359
Author(s):  
Ru Zhong ◽  
Jian Ping Wu ◽  
Yi Man Du
Keyword(s):  
Genetic Algorithm ◽  
Vehicle Routing ◽  
Heuristic Algorithm ◽  
Efficient Method ◽  
Vehicle Routing Problem ◽  
Multiple Objectives ◽  
Routing Problem ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm

When there are multiple objectives co-existent in Vehicle routing problem(VRP), it is difficult to achieve optical status simultaneously. To solve this issue, it introduces a method of improved multi-objective Genetic Algorithm (MOGA). It adopts an approach close to heuristic algorithm to cultivate partial viable chromosomes, route decoding to ensure that all individuals meet constraints and uses relatively efficient method of arena contest to construct non-dominated set. Finally programme to fulfill the multi-objective algorithm and then apply it in the standard example of VRP to verity its effectiveness by comparison with the existing optimal results.

scholarly journals Optimization of Multi Objective Vehicle Routing Problem with Time Windows using Total Time Balance

2020 ◽  
Vol 8 (6) ◽  
pp. 529-537
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Time Window ◽  
Time Windows ◽  
Transportation Systems ◽  
Routing Problem ◽  
Related Literature ◽  
Multi Objective ◽  
Standard Data ◽  
Goods And Services

Vehicle Routing Problem time window (VRPW) used in supply chain management in the physical delivery of goods and services, and the determination of all routes for a fleet of vehicles, starting and ending at a depot and serving customers with known demands by increasing the efficiency and profitability of transportation systems. The main objective of this study is to refutes a multi-objective vehicle routing problem with time windows by minimization of the number of vehicles, the total distance traveled and rout balance not sufficient instead of using 'total time balance'. To test the performance of the objective it considers GA with the fitness Aggregation approach comparing with the related literature based on Solomon’s benchmark instance standard data. The time makespan produced by FAGA and the time makespan mentioned in related literature is compared. From the result, fitness Aggregation approach with genetic algorizim (FAGA) provides better result including the real-time window as compared to previous work .

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.

Tackling the Multi-Objective Vehicle Routing Problem With Uncertain Demands

2020 ◽  
Vol 11 (1) ◽  
pp. 1-22 ◽  
Author(s):  
Méziane Aïder ◽  
Asma Skoudarli
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Time Window ◽  
Epistemic Uncertainty ◽  
Time Windows ◽  
Routing Problem ◽  
Multi Objective ◽  
Dominance Relations ◽  
Epistemic Representation ◽  
Time Window Constraints

In this article, the single capacitated vehicle routing problem with time windows and uncertain demands is studied. Having a set of customers whose actual demand is not known in advance, needs to be serviced. The goal of the problem is to find a set of routes with the lowest total travel distance and tardiness time, subject to vehicle capacity and time window constraints. Two uncertainty types can be distinguished in the literature: random and epistemic uncertainties. Because several studies focalized upon the random aspect of uncertainty, the article proposes to tackle the problem by considering dominance relations to handle epistemic uncertainty in the objective functions. Further, an epistemic multi-objective local search-based approach is proposed for studying the behavior of such a representation of demands on benchmark instances generated following a standard generator available in the literature. Finally, the results achieved by the proposed method using epistemic representation are compared to those reached by a deterministic version. Encouraging results have been obtained.

A New Multi Objective Genetic Algorithm: Fitness Aggregated Genetic Algorithm (FAGA) for Vehicle Routing Problem

2014 ◽  
Vol 984-985 ◽  
pp. 1261-1268 ◽  
Author(s):  
V. Sivaram Kumar ◽  
M.R. Thansekhar ◽  
R. Saravanan
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Routing Problem ◽  
Suggested Algorithm ◽  
Multi Objective ◽  
Total Distance ◽  
Multi Objective Genetic Algorithm ◽  
Assignment Approach

This paper presents multi objective vehicle routing problem in which the total distance travelled by the vehicles and total number of vehicles used are minimized. In general, fitness assignment procedure, as one of the important operators, influences the effectiveness of multi objective genetic algorithms. In this paper genetic algorithm with different fitness assignment approach and specialized crossover called Fitness Aggregated Genetic Algorithm (FAGA) is introduced for solving the problem. The suggested algorithm is investigated on large number of popular benchmarks for vehicle routing problem. It is observed from the results that the suggested new algorithm is very effective and the solutions are competitive with the best known results.

Sign in / Sign up

Export Citation Format

Share Document