A Genetic Algorithm for the Vehicle Routing Problem

The purpose of this research was to develop a version of a genetic algorithm (GA ) which would provide near optimal solutions for Vehicle Routing Problems (VRP) with both time and weight constraints. The genetic algorithm used for the experimentation was adapted from a GA which had been developed by James Bean at the University of Michigan to solve machine scheduling problems. The VRP data sets used in this research were obtained from the literature. Various aspects of the GA were experimented with in order to develop a version which would perform consistently well for all the data sets. The results of the final version of the genetic algorithm were then compared to the results presented in the original papers. The results from this research indicated that the genetic algorithm seems to perform relatively well for smaller problems with 50 or fewer customers. However, the results seem to become progressively worse as the problem becomes larger

A genetic algorithm for the vehicle routing problem with time windows

The objective of the vehicle routing problem (VRP) is to deliver a set of customers with known demands on minimum-cost vehicle routes originating and terminating at the same depot. A vehicle routing problem with time windows (VRPTW) requires the delivery be made within a speci¯c time frame given by the customers. Prins (2004) recently proposed a simple and e®ective genetic algorithm (GA) for VRP. In terms of average solution cost, it outperforms most published tabu search results. We implement this hybrid GA to handle VRPTW. Both the implementation and computational results will be discussed

A genetic algorithm for the vehicle routing problem with heterogeneous vehicles from multiple depots, allowing multiple visits

In the thesis, an application of a genetic algorithm (GA) is considered to solve the vehicle routing problem (VRP) which involves heterogeneous vehicles to serve known customer demands from multiple depots achieving the minimum delivery cost, where each customer must be satisfied by one or more visit(s), and each vehicle must make at most one visit to any particular customer. Vehicles can be unused. The problem involves optimizing the routes for all vehicles which are to serve a certain number of customers from multiple depots, allowing multiple visits. These conditions are generalized from the classical VRPs, which only involve one depot and one visit to each customer. The VRP is one of combinatorial optimization problems which are difficult to obtain an optimal solution through the classical optimization methods owing to the high computational complexity. The GA is a randomized global search algorithm to solve problems by imitating processes observed during natural evolution. It has been a widespread application to various combinatorial optimization problems such as traveling salesman problem, scheduling problem and VRP. The performance of GA is subject to the process parameters such as population size, crossover rate, termination condition, and mutation policy. For the generalized VRP under considerations, the influences of the process parameters in the proposed GA are examined by Taguchi method which is known as a robust design tool for optimizing the process parameters. The proposed GA is the first effort to solve the generalized VRP, which allows the multiple depots, multiple visits and heterogeneous vehicles. A real-life example problem of 35 US cities and 3 depots has been proposed to measure the performance of the proposed GA. In addition, 4 benchmark problems from the prior works only allowing one depot, one visit and homogeneous vehicles has been tested. The proposed GA outperforms the prior works by generating the equal to or the better solutions than the best known solutions. The computational results obtained from the performance comparisons show that the proposed GA is an effective and feasible method for solving the VRP with heterogeneous vehicles from multiple depots, allowing multiple visits to customers

Onsite Job Scheduling by Adaptive Genetic Algorithm

Onsite Job Scheduling is a specialized variant of Vehicle Routing Problem (VRP) with multiple depots. The objective of this problem is to execute jobs requested by customers, belonging to different geographic locations by a limited number of technicians, with minimum travel and overtime of technicians. Each job is expected to be completed within a specified time limit according to the service level agreement with customers. Each technician is assumed to start from a base location, serve several customers and return to the starting place. Technicians are allotted jobs based on their skill sets, expertise levels of each skill and availability slots. Although there are considerable number of literatures on VRP we do not see any explicit work related to Onsite Job Scheduling. In this paper we have proposed an Adaptive Genetic Algorithm to solve the scheduling problem. We found an optimized travel route for a substantial number of jobs and technicians, minimizing travel distance, overtime duration as well as meeting constraints related to SLA

A double dynamic fast algorithm to solve multi-vehicle Dial a Ride Problem

Abstract In this work a two level heuristic algorithm is described for a nearly real-time multi-vehicle many-to-many Dial-A-Ride Problem (DARP). This algorithm is ready to support a Demand Responsive Transportation System in which we face the problem of quickly evaluate a good-quality schedule for the vehicles and provide fast response to the users. The insertion heuristic is double dynamic nearly real-time and the objective function is to minimize the variance between the requested and scheduled time of pickup and delivery. In the first level, after a customer web-request, the heuristic returns an answer about the possibility to insert the request into the accepted reservations, and therefore in a vehicle schedule, or reject the request. In the second level, during the time elapsed between a request and the following, and after a reshuffling of the order of the incoming accepted requests, the same heuristic works for the whole set of accepted requests, trying to optimize the solution. We intensively tested the algorithm with a requests-generating software that has allowed us to show the competitive advantage of this web-based architecture

Research on multi-objective emergency logistics vehicle routing problem under constraint conditions

Purpose: Aim at choosing a relative good vehicle routing in emergency conditions under constraint conditions when disaster happens. Rapid response and rescue can save a lot of people. Design/methodology/approach: Modeling analysis: establishing a mathematical model of multi-objective emergency logistics vehicle routing problem. And in end of the paper, we intend to use genetic algorithms to solve the problem. Findings: Considering time requirement and cost limit both while choosing vehicle routing when the disasters happens is meaningful. We can get a relative good result and give a guidance to rescue teams. Originality/value: Consider cost and time objectives and kinds of realistic conditions (such as the road congestion) in the model when solving the problem, having expanded the theory scope.Peer Reviewe

Algoritmus a kapacitáskorlátos, egycentrumos járatszerkesztés megoldására

A tanulmány az elosztási hálózatokon gyakran előforduló problémával, a járatszerkesztéssel foglalkozik. A szerző központi telephelyről indított gyűjtő vagy elosztó járatok minimális költségű útvonalainak számítására közöl egy új algoritmust, amelynek lépéseit egy mintapéldán mutatja be, majd az eredményeket általánosítva összefoglalja az eljárást. A kézi számolásra és számítógépes programozásra egyaránt alkalmas módszer, különböző megszorítások, kapacitás, idő, stb. korlátok egyidejű figyelembevételét is lehetővé teszi

Use of a genetic algorithm for building efficient choice designs

Choice design building based on D-error minimization can be accomplished either by using predefined β values or by assuming probabilistic distributions for them. Several mathematical techniques have been used for both approaches in the past, resulting in algorithms that obtain efficient designs, which guarantee the high quality of the information that will be provided by the respondents. This paper proposes the use of a genetic algorithm for dealing with the problem of building designs with minimum D-error, describing the technique and applying it successfully to several benchmark cases. Design matrices, D-error values, percentages of level overlap and computation times are provided for each case

Pemilihan Kata Benda Bahasa Indonesia Berdasarkan Cakupan Suku Kata Menggunakan Genetic Algoritma untuk Dataset Audio Visual

Dalam pembentukan model Kecerdasan Buatan yang menggunakan pendekatan Deep Learning, dataset memegang peranan yang sangat penting. Memahami dan memilih kumpulan data yang tepat, sangatlah penting untuk memastikan keberhasilan sebuah model Kecerdasan Buatan. Salah satu topik yang cukup baru adalah mempelajari bagaimana pembentukan suara dari hasil pembacaan gerakan bibir manusia, dengan cakupan variasi bunyi dan bentuk bibir yang diharapkan dapat membantu pembelajaran sistem. Mayoritas dataset audio visual, yang biasa digunakan untuk pembangunan model pembentukan suara ataupun pembacaan gerakan bibir tidak memperhatikan keluasan cakupan variasi bunyi yang ada. AVID, salah satu dari dataset audio visual berbahasa Indonesia, mengadopsi susunan kata dalam dataset GRID, yang mengubah setiap kata penyusunnya dari Bahasa Inggris ke bahasa Indonesia. Sedangkan pada Bahasa Indonesia sendiri terdapat banyak ragam bunyi yang dibentuk dari satu atau sederet rangkaian fonem. Penelitian yang dilakukan penulis dengan memanfaatkan Genetic Algorithm untuk mendapatkan susunan kombinasi kata benda guna memperoleh nilai cakupan yang optimal. Dengan cakupan kombinasi suku kata yang lebih baik, maka dapat dihasilkan dataset untuk Deep Learning yang lebih baik lagi. Dalam penelitian ini, kata benda yang diproses, diperoleh dari KBBI edisi 2008, baru kemudian difilter untuk mendapatkan kata benda yang tepat mengandung 3 suku kata, yang bukan nama kota, tokoh maupun lokasi. Dari 39.070 kata benda yang ada, diperoleh 2936 kata benda yang akan digunakan. Ujicoba yang telah dilakukan pada 10.000 hingga 200.000 epoch, diperoleh rata-rata cakupan suku kata 72%-75% dengan batasan 26 variasi kata benda penyusunnya

A comparison of recombination operators for capacitate vehicle routing problem

The Vehicle Routing Problem (VRP) deals with the assignment of a set of transportation orders to a fleet of vehicles, and the sequencing of stops for each vehicle to minimize transportation costs. In this paper we study the Capacitated VRP (CVRP), which is mainly characterized by using vehicles of the same capacity. Taking a basic GA to solve the CVRP, we propose a new problem dependent recombination operator, called Best Route Better Adjustment recombination (BRBAX). A comparison of its performance is carried out with respect to other two classical recombination operators. Also we conduct a study of different mutations in order to determine the best combination of genetic operators for this problem. The results show that the use of our specialized BRBAX recombination outperforms the others more generic on all problem instances used in this work for all the metrics tested.Workshop de Agentes y Sistemas Inteligentes (WASI)Red de Universidades con Carreras en Informática (RedUNCI