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eBook Localized Genetic Algorithm for the Vehicle Routing Problem: Standardization of Divide and Conquer Strategies, Computational Comparison Rules and Complexity  Estimation ePub

eBook Localized Genetic Algorithm for the Vehicle Routing Problem: Standardization of Divide and Conquer Strategies, Computational Comparison Rules and Complexity Estimation ePub

by Ziauddin Ursani

  • ISBN: 383836807X
  • Subcategory: Computers
  • Author: Ziauddin Ursani
  • Language: English
  • Publisher: LAP LAMBERT Academic Publishing (May 26, 2010)
  • Pages: 328
  • ePub book: 1844 kb
  • Fb2 book: 1518 kb
  • Other: lrf mobi lit txt
  • Rating: 4.2
  • Votes: 505

Description

This book identifies some problems, the genetic algorithm GA is facing in the area of vehicle routing problem VRP and proposes various methods to address those problems.

This book identifies some problems, the genetic algorithm GA is facing in the area of vehicle routing problem VRP and proposes various methods to address those problems. Those problems arise from unavailability of suitable chromosomal representation and evaluation schemes of the GA for the VRP. They have problems of high computational cost, illegal chromosomes and wrong fitness assignment

In the late 1990s, large vehicle routing problem instances with nearly 500 customers were generated and solved using metaheuristics. In this paper, we focus on very large vehicle routing problems. Our contributions are threefold.

Ursani, Ziauddin, Daryl Essam, David Cornforth and Robert Stocker. The Vehicle Routing Problem (VRP) is an optimization problem that seeks to provide a solution to vehicle scheduling while minimizing (for example) cost or total travel times. Enhancements to the Localized Genetic Algorithm for Large Scale Capacitated Vehicle Routing Problems. IJAEC . (2013): 17-38.

In computer science, divide and conquer is an algorithm design paradigm based on multi-branched recursion

In computer science, divide and conquer is an algorithm design paradigm based on multi-branched recursion. A divide-and-conquer algorithm works by recursively breaking down a problem into two or more sub-problems of the same or related type, until these become simple enough to be solved directly. The solutions to the sub-problems are then combined to give a solution to the original problem.

cle{tsTT, title {Enhancements to the Localized Genetic Algorithm .

cle{tsTT, title {Enhancements to the Localized Genetic Algorithm for Large Scale Capacitated Vehicle Routing Problems}, author {Ziauddin Ursani and Daryl Essam and David Cornforth and Rob Stocker}, journal {IJAEC}, year {2013}, volume {4}, pages {17-38} .

PROBLEM DEFINITION The Vehicle Routing Problem (VRP) is a. .Two evolution strategies for solving the VRPTW were proposed by Homberger.

PROBLEM DEFINITION The Vehicle Routing Problem (VRP) is a generalization of the Traveling Salesman Problem (TSP) and consists in determining a set of vehicle routes of minimum total cost, such that each vehicle starts and ends at the depot (warehouse), each customer is visited exactly once, and the total demand handled by any vehicle does not exceed its capacity The heuristic techniques for the VRPTW can be divided into three groups: improvement heuristics, construction heuristics and metaheuristics.

A divide and conquer algorithm tries to break a problem down into as many little chunks as possible since it is easier to solve . It typically does this with recursion.

A divide and conquer algorithm tries to break a problem down into as many little chunks as possible since it is easier to solve with little chunks. Examples of divide and conquer include merge sort, fibonacci number calculations. This is the psuedocode for the Fibonacci number calculations: algorithm f(n) if n 0 or n 1 then return 1 else f(n-1) + f(n+1).

The main types of evolutionary algorithms for the VRPTW are genetic algorithms and evolution strategies. In addition to describing the basic features of each method, experimental results for the benchmark test problems of Solomon (1987) and Gehring and Homberger (1999) are presented and analyzed. evolutionary algorithms genetic algorithms vehicle routing transportation.

The vehicle routing problem (VRP) is one of the most famous combinatorial optimization problems

The vehicle routing problem (VRP) is one of the most famous combinatorial optimization problems. In simple terms, the goal is to determine a set of routes with overall minimum cost that can satisfy several geographical scattered demands. Biological inspired computation is a field devoted to the development of computational tools modeled after principles that exist in natural systems. The adoption of such design principles enables the production of problem solving techniques with enhanced robustness and flexibility, able to tackle complex optimization situations.

When you use the divide and conquer strategy, what you do is you break up the problem into many smaller problems and then you combine the solutions for the small problems to get the solution for the main problem. How to solve the smaller problems: By breaking them up further. This process of breaking up continues until you reach a level where the problem is small enough to be handled directly. How to compute time complexity: Assume the time taken by your algo is T(n). n. Now, notice what you are doing.

This book identifies some problems, the genetic algorithm GA is facing in the area of vehicle routing problem VRP and proposes various methods to address those problems. Those problems arise from unavailability of suitable chromosomal representation and evaluation schemes of the GA for the VRP. They have problems of high computational cost, illegal chromosomes and wrong fitness assignment. These problems are addressed by several new proposed schemes namely Self Imposed Constraints evaluation scheme, Contour and Reverse Contour evaluation scheme and Order Skiping evaluation scheme, which are specifically tailored for various problems, objectives and situations. Apart from this Localized Optimization Framework is introduced,which standardizes divide and conquer strategies. Genetic Algorithm is used in this framework to be called as Localized Genetic Algorithm. Lastly, a standard unit for computational comparison i.e., Bellman's Evaluation Units BEUs, is also introduced to facilitate computational comparisons for future researchers. Furthermore computational complexity estimation is also modelled to estimate computational time without actual experimentation.