design-of-a-selective-parallel-heuristic-algorithm-for-the-vehicle-routing

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Overview

Supply Chain Management could be defined as the practice of analyzing all aspects of acquiring, storing, moving, and delivering materials from the time they are acquired through any conversion or production processes through to the time final products are used or sold. A company‟s supply chain may consists of geographically dispersed facilities where raw materials, intermediate products, or finished products are acquired, transformed, stored, or sold, and transportation links connecting the facilities along which products flow.
Logistics management is that part of the supply chain which plans, implements and controls the efficient, effective forward and reverse flow and storage of goods, services and related information between the point of origin and the point of consumption in order to meet customers’ requirements. Depending on the industry sector, supply chain logistics costs account from 5% to 50% of a product‟s total landed cost. The Vehicle Routing Problem (VRP) is an important problem occurring in many distribution systems. Many companies are faced with problems regarding the transportation of people, goods or information. This is commonly denoted as routing problems.
Indeed they not only model the problems of collection and delivery of goods, but, more generally, appear as a key ingredient in many transportation systems, such as those for solid waste collection, street cleaning, bus routing, dial-a-ride systems, routing of maintenance units, transports for handicapped. Another area in which very similar problems play a relevant role is modern telecommunication networks, even if here we find « routing » and not « vehicle routing » problems. As the world economy turns more and more global, transportation is becoming more important. And with the current energy and economic crisis, conserving resources is at the utmost priority. Environmental Accounts published by the Office for National Statistics in the UK show that, on a UK resident‟s basis, greenhouse gas emissions fell 1.4 per cent between 2005 and 2006 to 724.5 million tonnes of CO2 equivalent(Office of National Statistics, News Release, 2006). Between 2005 and 2006 greenhouse gas emissions from the non-household sector decreased by 1.1 per cent to 572.8 million tonnes of CO2 equivalent. This was largely driven by a fall in emissions from the transport and communications sector due to changes in the structure of the UK shipping industry. If shipping industry emissions are removed from the data the year on year change in emissions from the non-household sector rose 0.2 per cent.
Greenhouse gas emissions from the non-household sector accounted for 79.1 percent of all emissions in 2006. The transport and communications industries were one of the most significant non-household contributors to greenhouse gas emissions in 2006, responsible for 15.7 per cent (113.8 million tonnes of CO2 equivalent) and 13.3 per cent (96.3 million tonnes of CO2 equivalent) respectively. Emissions from the road transport industry show a small year on year increase of 0.4 per cent but at 190.9 million tonnes of CO2 equivalent this is 17.9 per cent above the 1990 level. In practice, vehicle routing may be the single biggest success story in operations research. For example, each day 103,500 drivers at UPS follow computer-generated routes. The drivers visit 7.9 million customers and handle an average of 15.6 million packages.

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Table of Contents :

  • Table of Figures
  • Table of Tables
  • Table of Equations
  • Table of Algorithms
  • Glossary
  • 1 Inroduction
  • 1.1 Overview
  • 1.2 Background on VRP
  • 1.2.1 VRP variants
  • 1.3 Input Data
  • 1.4 Algorithms
  • 1.4.1 Artificial Intelligence
  • 1.4.1.1 Symbolic Processing
  • 1.4.1.2 Heuristics
  • 1.4.1.3 Inferencing
  • 1.4.1.4 Pattern Matching
  • 1.4.2 Heuristic
  • 1.5 Parallel Algorithms
  • 1.6 Adaptive Object Modelling
  • 1.7 Summary
  • 2 VRP: An overview
  • 2.1 Introduction
  • 2.2 Travelling Salesman Problem
  • 2.3 Background on VRP
  • 2.3.1 Evolutionary algorithms
  • 2.3.2 Initial Solutions
  • 2.3.3 VRP and Clustering
  • 2.3.4 Tabu Search
  • 2.3.5 Ant Optimisation
  • 2.3.6 Hyper-Heuristics
  • 2.3.7 Memetic Algorithms
  • 2.4 Problem Overview
  • 2.5 Formulation: Vehicle Routing Problem
  • 2.6 VRP variants
  • 2.6.1 Multiple Depots
  • 2.7 Problem Statement
  • 2.8 Adaptive Objects
  • 2.8.1 Data source
  • 2.8.2 Object layer
  • 2.8.3 Base classes
  • 2.8.4 Cost functions
  • 2.8.5 Optimization algorithm
  • 2.9 Algorithm
  • 2.9.1 Initial Solution
  • 2.9.2 Meta-Heuristics
  • 2.9.3 Tabu Search
  • 2.9.4 Ant Algorithms
  • 2.9.5 Memetic Algorithms
  • 2.10 Parallel
  • 2.11 Summary
  • 3 Adaptive Object Modeling
  • 3.1 Overview
  • 3.2 Supply chain domain
  • 3.3 Components
  • 3.3.1 Data source
  • 3.3.2 Domain Objects
  • 3.3.3 Base classes (Solution Workspace)
  • 3.3.4 Cost and constraint functions
  • 3.3.5 Optimization algorithm
  • 3.4 Implementation
  • 3.4.1 Constraints
  • 3.4.2 Interfaces
  • 3.5 Base classes
  • 3.6 Data objects
  • 3.7 Cost and constraint functions
  • 3.7.1.1 Solomon Function
  • 3.7.1.2 Peak and off-peak travel times
  • 3.8 Optimization algorithm
  • 3.9 Summary
  • 4 Ant System on Adaptive objects Algorithm
  • 4.1 Approach
  • 4.2 Compatibility and Cost Matrices
  • 4.2.1 The cost matrix
  • 4.2.2 The compatibility matrix
  • 4.2.2.1 Time Window Compatibility
  • 4.3 Clustering assistance in probability
  • 4.3.1 Review of clustering methods
  • 4.3.1.1 Partitioning methods
  • 4.3.1.2 Hierarchical Method
  • 4.3.1.3 Density Methods
  • 4.3.1.4 Model Methods
  • 4.3.2 Clusters and data environment
  • 4.3.3 Cluster Methods
  • 4.3.3.1 DBSCAN
  • 4.3.3.2 Shared Nearest Neighbour algorithm
  • 4.3.3.3 k-Means
  • 4.3.3.4 k-Medoids
  • 4.3.4 Cluster implementation
  • 4.4 Construction Heuristic
  • 4.5 Tabu Search
  • 4.5.1 Move Operators
  • 4.5.1.1 Insert Operator
  • 4.5.1.2 Tour depletion operator
  • 4.5.1.3 Relocate operator
  • 4.5.1.4 Exchange Operator
  • 4.5.1.5 Cross operator
  • 4.5.1.6 Vehicle Fit
  • 4.5.1.7 Operator probability
  • 4.6 Simulated Annealing
  • 4.7 Ant Algorithms
  • 4.8 Solution Algorithm
  • 4.9 Summary
  • 5 Environment Analysis – Results
  • 5.1 Overview
  • 5.2 Solomon Functions
  • 5.3 Probability matrix
  • 5.4 Density cluster results
  • 5.5 Partition cluster results
  • 5.6 Benchmark Results
  • 5.6.2 C
  • 5.6.3 R
  • 5.6.4 R
  • 5.6.5 RC
  • 5.6.6 RC
  • 5.7 Summary
  • 6 Parallel implementation
  • 6.1 Overview
  • 6.2 Single thread environment
  • 6.3 Partitioning
  • 6.4 Communication Analysis
  • 6.5 Granularity Control
  • 6.6 Mapping
  • 6.7 Parallel Ant System on Adaptive Objects
  • 6.8 Results
  • 6.9 Summary
  • 7 Conlusion
  • 7.1 Overview
  • 7.2 Problem approach
  • 7.3 Results
  • 7.4 Summary
  • 8 Bibliography

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