Πλοήγηση ανά Επιβλέπων "Nikolopoulou, Amalia"

A B C D E F G H I J K L M N O P Q R S T U V W X Y Z

Α Β Γ Δ Ε Ζ Η Θ Ι Κ Λ Μ Ν Ξ Ο Π Ρ Σ Τ Υ Φ Χ Ψ Ω

Τώρα δείχνει 1 - 2 από 2

The capacitated vehicle routing problem. A comparison between integer linear programming and metaheuristic search methods
(2021) Chiotis, Minas A.; Χιώτης, Μηνάς; Athens University of Economics and Business, Department of Business Administration; Kouretas, Georgios; Agoraki, Maria-Eleni; Nikolopoulou, Amalia
This thesis presents a comparison between two ways of solving a Capacitated Vehicle Routing Problem (CVRP) - a special variation of the classic Vehicle Routing Problem (VRP) - with Linear Integer Programming versus metaheuristic methods from Operating Research Google developers (OR-Tools). This problem is traditionally as one of the most important issues for companies delivering goods and services and in general in the sector of Logistics and Supply chain. The scope of this thesis is to deliver the best solution between two different methodologies and propose the best use of them at any case they face.In the first part we can find the literature about the Supply Chain Management and the VRP problem in general and the variations of it. There are mentions about the creators and researchers of the sector and highlighting the milestones of their life which was dedicated in their work.We analyze that in the CVRP case we examine the scope of the problem is to serve as many as possible customers and to minimize the total distance of each truck aiming to minimize the total cost of transportation and maximizing the company’s profitability and competitive advantage against other providers. The constraint is the capacity of each vehicle which is not tolerated to be exceeded. The comparison between them focuses on which of the two methods is the most effective in a manner of time, precision, and efficiency.After that we analyze the methodologies we are going to use and based on we are going to implement the experiment at each case. Here we use the linear integer programming to solve the problem and on the other hand a bunch of metaheuristics which come from OR Tools. Details about the integration is listed in the proportionate chapter.Then implementation and conclusion take place and are starting to represent the whole mindset of the problem, the issues, the measurements and finally the results based on how we make our comparison. All in all, the outcome is different and at any case the user (manager) is called to make a decision that has to choose between these two methodologies.
Cargo shipping and scheduling optimization for crude oil transportation
(02/19/2021) Margaritis, Athanasios; Μαργαρίτης, Αθανάσιος; Peppas, Konstantinos-Marios; Πέππας, Κωνσταντίνος-Μάριος; Athens University of Economics and Business, Department of Accounting and Finance; Kavousanos, Emmanuel; Androutsopoulos, Konstantinos; Nikolopoulou, Amalia
This thesis aims to apply mathematical techniques to facilitate decision making for shipping companies providing crude oil transportation. Focus is given on the development of a mathematical model solving a challenging problem for the shipping industry with practical operational constraints and characteristics. In particular we study a cargo shipping and scheduling problem for crude oil transportation taking into consideration time window constraints and operational synchronization constraints. At first, we examine the significance of crude oil transportation worldwide. To do so, the largest importers of crude oil worldwide are considered. Next, we review research on ship routing and scheduling and related problems during the new millennium and present main methods for solving them. From the Literature review it is revealed that the routing and scheduling problems for shipping companies are of great importance. Our research studies a routing and scheduling problem in which a maritime company needs to transport crude oil between three loading ports and nine discharge ports. Capacity and time window constraints are considered. The problem is formulated as a Mixed Integer Linear Programming Model and Open-solver is used to obtain the optimal solution.