Method for loading cargo trucks using two-dimensional packing algorithms

The paper describes the method for optimization of loading cargo trucks using two-dimensional packing algorithms. The point of this method is to reduce loading cargo problem to two-dimensional packing problem. This problem can be solved by using of various algorithms. There is analysis of several algorithms that are most often used in practical calculations of objects distribution in 2D space in this paper. The object of this study is transport of the metal processing company and its products (cargo). PHP programming language, MySQL database, and Apache web server are used to create client application. The interface developed using HTML5, CSS and javascript. © 2019 IOP Publishing Ltd. All rights reserved.The work was supported by Act 211 Government of the Russian Federation, contract № 02.A03.21.0006

Models and algorithms for berth allocation problems in port terminals

Seaports play a key role in maritime commerce and the global market economy. Goods of different kinds are carried in specialized vessels whose handling requires ad hoc port facilities. Port terminals comprise the quays, infrastructures, and services dedicated to handling the inbound and outbound cargo carried on vessels. Increasing seaborne trade and ever-greater competition between port terminals to attract more traffic have prompted new studies aimed at improving their quality of service while reducing costs. Most terminals implement operational planning to achieve more efficient usage of resources, and this poses new combinatorial optimization problems which have attracted increasing attention from the Operations Research community. One of the most important problems confronted at the quayside is the efficient allocation of quay space to the vessels calling at the terminal over time, also known as the Berth Allocation Problem. A closely related problem arising in terminals that specialize in container handling concerns the efficient assignment of quay cranes to vessels, which, together with quay space planning, leads to the Berth Allocation and Quay Crane Assignment Problem. These problems are known to be especially hard to solve, and therefore require designing methods capable of attaining good solutions in reasonable computation times. This thesis studies different variants of these problems considering well-known and new real-world aspects, such as terminals with multiple quays or irregular layouts. Mathematical programming and metaheuristics techniques are extensively used to devise tailored solution methods. In particular, new integer linear models and heuristic algorithms are developed to deal with problem instances of a broad range of sizes representing real situations. These methods are evaluated and compared with other state-of-the-art proposals through various computational experiments on different benchmark sets of instances. The results obtained show that the integer models proposed lead to optimal solutions on small instances in short computation times, while the heuristic algorithms obtain good solutions to both small and large instances. Therefore, this study proves to be an effective contribution to the efforts aimed at improving port efficiency and provides useful insights to better tackle similar combinatorial optimization problems

Solving a large multicontainer loading problem in the car manufacturing industry

Renault, a large car manufacturer with factories all over the world, has a production system in which not every factory produces all the parts required to assemble a vehicle. Every day, large quantities of car parts are sent from one factory to another, defining very large truck/container transportation problems. The main challenge faced by the Renault logistics platforms is to load the items into trucks and containers as efficiently as possible so as to minimize the number of vehicles sent. Therefore, the problem to be solved is a multicontainer loading problem in which, besides the usual geometric constraints preventing items from overlapping and exceeding the dimensions of the container, there are many other constraints, concerning the way in which items are put into layers, layers into stacks and stacks into containers, limiting the total weight and the weight supported by the items. In this paper we propose a GRASP algorithm, including constructive procedures to build solutions satisfying all the constraints, randomization strategies to produce diversity of solutions, and improvement moves to obtain high-quality solutions in short computing times. The algorithm has been tested on a set of real instances provided by the company and the results are competitive with the best results known, including some new improved solutions