scholarly journals Enhanced Genetic Algorithm Optimization Models for Vehicular Routing Problems

2019 ◽  
Vol 9 (2) ◽  
pp. 3140-3149
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problems ◽  
Optimization Models ◽  
Optimization Approach ◽  
Algorithm Optimization ◽  
Routing Problem ◽  
Performance Factors ◽  
Fine Grained ◽  
Combinatorial Optimization Problems ◽  
Routing Strategy

The Vehicle Routing Problem (VRP) is one of the most studied combinatorial optimization problems because of its practical relevance and complexity. Though there are several techniques have been proposed to solve the VRPs and its variants effectively, each technique has its own tradeoff values in terms of the performance factors. From this perspective, the work presented in this paper proposed an intelligent routing strategy for VRP based on distance values between the cities. The proposed strategy uses an enhanced model of Genetic Algorithm to find the optimal tour paths among the cities under distance based optimized tour path estimation scenarios. For distance-based optimization approach, experiments were performed on the standard benchmark TSP instances obtained from TSPLIB. A set of fine-grained result analyses demonstrated that the proposed model of routing strategies performed comparatively better w.r.t. the existing relevant approaches. By considering this problem as the base, a distinct model was developed as a set of assistive modules for Genetic Algorithms (GA), which are aimed at improving the overall efficiency of the typical GA, particularly for optimization problems. The capability of the proposed optimization models for VRP is demonstrated at various levels, particularly at the population initialization stage, using a set of well-defined experiments.

A New Multi-Criteria Solving Procedure for Multi-Depot FSM-VRP with Time Window

2017 ◽  
Vol 4 (1) ◽  
pp. 1-18 ◽  
Author(s):  
Lahcene Guezouli ◽  
Samir Abdelhamid
Keyword(s):  
Genetic Algorithm ◽  
Combinatorial Optimization ◽  
Vehicle Routing Problem ◽  
Processing Time ◽  
Optimization Problems ◽  
Time Window ◽  
Routing Problem ◽  
Combinatorial Optimization Problems ◽  
Acceptable Quality ◽  
Capacitated Vehicle

One of the most important combinatorial optimization problems is the transport problem, which has been associated with many variants such as the HVRP and dynamic problem. The authors propose in this study a decision support system which aims to optimize the classical Capacitated Vehicle Routing Problem by considering the existence of different vehicle types (with distinct capacities and costs) and multiple available depots, that the authors call the Multi-Depot HVRPTW by respecting a set of criteria including: schedules requests from clients, the heterogeneous capacity of vehicles..., and the authors solve this problem by proposing a new scheme based on a genetic algorithm heuristics that they will specify later. Computational experiments with the benchmark test instances confirm that their approach produces acceptable quality solutions compared with previous results in similar problems in terms of generated solutions and processing time. Experimental results prove that the method of genetic algorithm heuristics is effective in solving the MDHVRPTW problem and hence has a great potential.

Enterprise distribution routing optimization with soft time windows based on genetic algorithm

2021 ◽  
pp. 1-11
Author(s):  
Kaixian Gao ◽  
Guohua Yang ◽  
Xiaobo Sun
Keyword(s):  
Genetic Algorithm ◽  
Customer Satisfaction ◽  
Optimization Problems ◽  
Rapid Development ◽  
Time Windows ◽  
Theory And Practice ◽  
Routing Problem ◽  
Logistics Industry ◽  
Routing Optimization ◽  
A Company

With the rapid development of the logistics industry, the demand of customer become higher and higher. The timeliness of distribution becomes one of the important factors that directly affect the profit and customer satisfaction of the enterprise. If the distribution route is planned rationally, the cost can be greatly reduced and the customer satisfaction can be improved. Aiming at the routing problem of A company’s vehicle distribution link, we establish mathematical models based on theory and practice. According to the characteristics of the model, genetic algorithm is selected as the algorithm of path optimization. At the same time, we simulate the actual situation of a company, and use genetic algorithm to plan the calculus. By contrast, the genetic algorithm suitable for solving complex optimization problems, the practicability of genetic algorithm in this design is highlighted. It solves the problem of unreasonable transportation of A company, so as to get faster efficiency and lower cost.

The Simulation Optimization for Job-Shop Scheduling Based on Plant Simulation Using Genetic Algorithm

2012 ◽  
Vol 217-219 ◽  
pp. 1444-1448
Author(s):  
Xiang Ke Tian ◽  
Jian Wang
Keyword(s):  
Genetic Algorithm ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Optimization Problems ◽  
Scheduling Problem ◽  
Scheduling Problems ◽  
Job Shop Scheduling Problem ◽  
Shop Scheduling ◽  
Combinatorial Optimization Problems ◽  
Plant Simulation

The job-shop scheduling problem (JSP), which is one of the best-known machine scheduling problems, is among the hardest combinatorial optimization problems. In this paper, the key technology of building simulation model in Plant Simulation is researched and also the build-in genetic algorithm of optimizing module is used to optimize job-shop scheduling, which can assure the scientific decision. At last, an example is used to illustrate the optimization process of the Job-Shop scheduling problem with Plant Simulation genetic algorithm modules.

Efficient Golden-Ball Algorithm Based Clustering to solve the Multi-Depot VRP With Time Windows

2018 ◽  
Vol 9 (1) ◽  
pp. 1-16 ◽  
Author(s):  
Lahcene Guezouli ◽  
Mohamed Bensakhria ◽  
Samir Abdelhamid
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Clustering Algorithm ◽  
Optimization Problems ◽  
Time Window ◽  
Time Windows ◽  
Routing Problem ◽  
Combinatorial Optimization Problems ◽  
Acceptable Quality ◽  
Golden Ball

In this article, the authors propose a decision support system which aims to optimize the classical Capacitated Vehicle Routing Problem by considering the existence of multiple available depots and a time window which must not be violated, that they call the Multi-Depot Vehicle Routing Problem with Time Window (MDVRPTW), and with respecting a set of criteria including: schedules requests from clients, the capacity of vehicles. The authors solve this problem by proposing a recently published technique based on soccer concepts, called Golden Ball (GB), with different solution representation from the original one, this technique was designed to solve combinatorial optimization problems, and by embedding a clustering algorithm. Computational results have shown that the approach produces acceptable quality solutions compared to the best previous results in similar problem in terms of generated solutions and processing time. Experimental results prove that the proposed Golden Ball algorithm is efficient and effective to solve the MDVRPTW problem.

The Traveling Salesman Problem, the Vehicle Routing Problem, and Their Impact on Combinatorial Optimization

2010 ◽  
Vol 1 (2) ◽  
pp. 82-92 ◽  
Author(s):  
Gilbert Laporte
Keyword(s):  
Combinatorial Optimization ◽  
Vehicle Routing ◽  
Traveling Salesman Problem ◽  
Vehicle Routing Problem ◽  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Traveling Salesman ◽  
Routing Problem ◽  
Combinatorial Optimization Problems ◽  
The Traveling Salesman Problem

The Traveling Salesman Problem (TSP) and the Vehicle Routing Problem (VRP) are two of the most popular problems in the field of combinatorial optimization. Due to the study of these two problems, there has been a significant growth in families of exact and heuristic algorithms being used today. The purpose of this paper is to show how their study has fostered developments of the most popular algorithms now applied to the solution of combinatorial optimization problems. These include exact algorithms, classical heuristics and metaheuristics.

scholarly journals Improving Genetic Algorithm with Fine-Tuned Crossover and Scaled Architecture

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Ajay Shrestha ◽  
Ausif Mahmood
Keyword(s):  
Genetic Algorithm ◽  
Evolutionary Biology ◽  
Optimization Problems ◽  
Search Space ◽  
Island Model ◽  
Fine Tuning ◽  
Small Subset ◽  
Combinatorial Optimization Problems ◽  
Mutation Operators ◽  
New Concepts

Genetic Algorithm (GA) is a metaheuristic used in solving combinatorial optimization problems. Inspired by evolutionary biology, GA uses selection, crossover, and mutation operators to efficiently traverse the solution search space. This paper proposes nature inspired fine-tuning to the crossover operator using the untapped idea of Mitochondrial DNA (mtDNA). mtDNA is a small subset of the overall DNA. It differentiates itself by inheriting entirely from the female, while the rest of the DNA is inherited equally from both parents. This unique characteristic of mtDNA can be an effective mechanism to identify members with similar genes and restrict crossover between them. It can reduce the rate of dilution of diversity and result in delayed convergence. In addition, we scale the well-known Island Model, where instances of GA are run independently and population members exchanged periodically, to a Continental Model. In this model, multiple web services are executed with each web service running an island model. We applied the concept of mtDNA in solving Traveling Salesman Problem and to train Neural Network for function approximation. Our implementation tests show that leveraging these new concepts of mtDNA and Continental Model results in relative improvement of the optimization quality of GA.

scholarly journals Using a Goal-Switching Selection Operator in Multi-Objective Genetic Algorithm Optimization Problems

2013 ◽  
Author(s):  
Daniel Shaefer ◽  
Scott Ferguson
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problems ◽  
Pareto Frontier ◽  
Product Line ◽  
Algorithm Optimization ◽  
Nsga Ii ◽  
Solution Quality ◽  
Design Variables ◽  
Mp3 Player ◽  
Selection Operator

This paper demonstrates how solution quality for multiobjective optimization problems can be improved by altering the selection phase of a multiobjective genetic algorithm. Rather than the traditional roulette selection used in algorithms like NSGA-II, this paper adds a goal switching technique to the selection operator. Goal switching in this context represents the rotation of the selection operator among a problem’s various objective functions to increase search diversity. This rotation can be specified over a set period of generations, evaluations, CPU time, or other factors defined by the designer. This technique is tested using a set period of generations before switching occurs, with only one objective considered at a time. Two test cases are explored, the first as identified in the Congress on Evolutionary Computation (CEC) 2009 special session and the second a case study concerning the market-driven design of a MP3 player product line. These problems were chosen because the first test case’s Pareto frontier is continuous and concave while being relatively easy to find. The second Pareto frontier is more difficult to obtain and the problem’s design space is significantly more complex. Selection operators of roulette and roulette with goal switching were tested with 3 to 7 design variables for the CEC 09 problem, and 81 design variables for the MP3 player problem. Results show that goal switching improves the number of Pareto frontier points found and can also lead to improvements in hypervolume and/or mean time to convergence.

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