The Eco-Friendly Intermodal Delivery Network

The design of the distribution process is a strategic issue for almost every company. As the use of advanced technology and automation increases in manufacturing and logistics, the implementation of autonomous and electrical transportation, such as driverless vehicles and electric trucks, has become an interesting topic of study within the last few years, with the main objective of minimizing distribution costs and delivery times. The purpose of this research is to prove that intermodal delivery networks, which may combine a train and several electric vehicles, are more efficient and environmentally friendly than unimodal networks for high volume and long haul transportation, regardless of the customers’ distribution. This is only applicable if demand does not fall within the capacity restriction of road transportation vehicles. To do so, this paper utilizes an optimization algorithm that consists of a feedback mechanism between K-means and a genetic algorithm, which finds the optimal routes between distribution centers and surrounding customers as a multiple traveling salesman problem (mTSP)

Heuristics for dynamic and stochastic routing in industrial shipping

Maritime transportation plays a central role in international trade, being responsible for the majority of long-distance shipments in terms of volume. One of the key aspects in the planning of maritime transportation systems is the routing of ships. While static and deterministic vehicle routing problems have been extensively studied in the last decades and can now be solved effectively with metaheuristics, many industrial applications are both dynamic and stochastic. In this spirit, this paper addresses a dynamic and stochastic maritime transportation problem arising in industrial shipping. Three heuristics adapted to this problem are considered and their performance in minimizing transportation costs is assessed. Extensive computational experiments show that the use of stochastic information within the proposed solution methods yields average cost savings of 2.5% on a set of realistic test instances

Analysis of Gas Turbine Operation before and after Major Maintenance

This paper presents an analysis of the gas turbine real process (with all losses included) before and after a major maintenance. The analysis of both gas turbine operating regimes is based on data measured during its exploitation. Contrary to authors’ expectations, the major maintenance process did not result either in any decrease in losses or increase in efficiencies for the majority of the gas turbine components. However, the major maintenance influenced positively the gas turbine combustion chambers (reduction in losses and increase in the combustion chambers efficiency). After the major maintenance, the overall process efficiency decreased from 43.796% to 41.319% due to a significant decrease in the air mass flow rate and to an increase in the fuel mass flow rate in combustion chambers. A decrease in the gas turbine produced cumulative and useful power after a major maintenance also increased the specific fuel consumption

Improvement Opportunities in Commodity Trucks Delivery in Globalized Markets

Market globalization has posed the problem of finding economical routes of product delivery one of which is via a system of intermodal motor traction. Currently, the automotive manufacturing plant KAMAZ supplies its products and accompanying servicing to countries in Europe, Asia, Africa and Latin America, and intends to extend the market. This study deals with peculiarities of the organization of spare parts delivery to the dealer-service network abroad. Risks of water haul are considered; methods for improving of transportation planning by developing a decision support system are proposed

An artificial neural network based decision support system for energy efficient ship operations

Reducing fuel consumption of ships against volatile fuel prices and greenhouse gas emissions resulted from international shipping are the challenges that the industry faces today. The potential for fuel savings is possible for new builds, as well as for existing ships through increased energy efficiency measures; technical and operational respectively. The limitations of implementing technical measures increase the potential of operational measures for energy efficient ship operations. Ship owners and operators need to rationalise their energy use and produce energy efficient solutions. Reducing the speed of the ship is the most efficient method in terms of fuel economy and environmental impact. The aim of this paper is twofold: (i) predict ship fuel consumption for various operational conditions through an inexact method, Artificial Neural Network ANN; (ii) develop a decision support system (DSS) employing ANN based fuel prediction model to be used on-board ships on a real time basis for energy efficient ship operations. The fuel prediction model uses operating data -‘Noon Data’ - which provides information on a ship’s daily fuel consumption. The parameters considered for fuel prediction are ship speed, revolutions per minute (RPM), mean draft, trim, cargo quantity on board, wind and sea effects, in which output data of ANN is fuel consumption. The performance of the ANN is compared with multiple regression analysis (MR), a widely used surface fitting method, and its superiority is confirmed. The developed DSS is exemplified with two scenarios, and it can be concluded that it has a promising potential to provide strategic approach when ship operators have to make their decisions at an operational level considering both the economic and environmental aspects