PYXIDA Institutional Repository
and Digital Library
Collections :

Title :Models and solution algorithms for inventory routing problems
Creator :Lappas, Pantelis
Contributor :Athens University of Economics and Business, Department of Management Science and Technology (Degree granting institution)
Kritikos, Emmanouil (Επιβλέπων καθηγητής)
Type :Text
Extent :85 σ.
Language :en
Abstract :Στόχος της παρούσας διατριβής είναι η παρουσίαση αλγοριθμικών προσεγγίσεων για την επίλυση του Προβλήματος Δρομολόγησης Αποθεμάτων (Inventory Routing Problem, IRP) και του Προβλήματος Δρομολόγησης Αποθεμάτων με Χρονικά Παράθυρα (Inventory Routing Problem with Time Windows, IRPTW). Τα ανωτέρω προβλήματα πηγάζουν από την προσέγγιση της Διαχείρισης Αποθεμάτων από τον Προμηθευτή/Πωλητή (Vendor Managed Inventory, VMI) που διαδόθηκε ιδιαίτερα κατά τα τέλη της δεκαετίας του ’80 από τις Wal-Mart και Procter & Gamble και στη συνέχεια υιοθετήθηκε από πολλές εταιρίες όπως οι Johnson & Johnson, Black & Decker κ.ά. Σύμφωνα με το VMI, ο προμηθευτής διανέμει προϊόντα σε έναν αριθμό από γεωγραφικά διάσπαρτους πελάτες αποφασίζοντας ταυτόχρονα για τα ακόλουθα: (1) τους χρόνους εξυπηρέτησης πελατών, (2) τις ποσότητες διανομής και (3) τις διαδρομές που πρέπει να ακολουθηθούν. Οι πρώτες δύο αποφάσεις, σχετίζονται με το Πρόβλημα Ελέγχου Αποθεμάτων (Inventory Control Problem, ICP), ενώ η τρίτη με το Πρόβλημα της Δρομολόγησης Οχημάτων (Vehicle Routing Problem, VRP).The main objective of this thesis is to propose a hybrid evolutionary optimization algorithm for solving the Inventory Routing Problem (IRP). The IRP arises from the application of the Vendor Managed Inventory (VMI) concept, where the supplier (vendor) has to make inventory and routing decisions simultaneously for a given planning horizon. This thesis focuses on a scenario where a single-product type has to be delivered by a fleet of capacitated homogenous vehicles and housed at a depot over a finite and discrete planning horizon. The demand is fully available to the decision maker (supplier) at the beginning of the planning horizon, stock-outs are not allowed, and transportation costs and inventory holding costs of customers are taken into account in the objective function. Due to the NP-hard nature of the IRP, it is very difficult to develop an exact algorithm that can solve large-scale problems within a reasonable computation time. As an alternative, a hybrid evolutionary optimization algorithm based on two well-known meta-heuristics, the Genetic Algorithm and the Simulated Annealing Algorithm, is presented to handle the IRP. Namely, the Genetic Algorithm is related to the planning phase, while the Simulated Annealing Algorithm is associated with the routing phase. A repetitive procedure, containing characteristics from both referred meta-heuristics, is applied to obtain a near-optimal feasible solution. Testing instances with different properties are established to investigate algorithmic performance, and the computational results are then reported.
Subject :Inventory Routing Problem (IRP)
Vendor Managed Inventory (VMI)
Solution algorithms
Date Issued :23-05-2017
Licence :

File: Lappas_2017.pdf

Type: application/pdf