A evolutionary algorithm for dynamically optimisation of drayage operations

Proper planning of drayage operations is fundamental in the quest for the economic viability of intermodal freight transport. The work we present here is a dynamic optimization model which uses real-time knowledge of the fleet's position, permanently enabling the planner to reallocate tasks as the problem conditions change. Stochastic trip times are considered, both in the completion of each task and between tasks

A Genetic Algorithm for Real-Time Optimisation of Drayage Operations

Proper planning of drayage operations is fundamental in the quest for the economic viability of intermodal freight transport. The work we present here is a dynamic optimization model which uses real-time knowledge of the fleet’s position, permanently enabling the planner to reallocate tasks as the problem conditions change. Stochastic trip times are considered, both in the completion of each task and between tasks

A satellite navigation system to improve the management of intermodal drayage

The intermodal transport chain can become more efficient by means of a good organization of the drayage movements. Drayage in intermodal container terminals involves the pick up or delivery of containers at customer locations, and the main objective is normally the assignment of transportation tasks to the different vehicles, often with the presence of time windows. The literature shows some works on centralised drayage management, but most of them consider the problem only from a static and deterministic perspective, whereas the work we present here incorporates the knowledge of the real-time position of the vehicles, which permanently enables the planner to reassign tasks in case the problem conditions change. This exact knowledge of position of the vehicles is possible thanks to a geographic positioning system by satellite (GPS, Galileo, Glonass), and the results show that this additional data can be used to dynamically improve the solution

Knowledge of Real Time Position of Vehicles and Its Impact on the Improvement of Intermodal Drayage Operations

Libro de abstracts en la web del congreso: http://www.civil.ist.utl.pt/wctr12_lisboa/int_04_program_conference.htmThe intermodal transport chain can become more efficient by means of a good organization of the drayage movements. Drayage in intermodal container terminals involves the pick up or delivery of containers at customer locations. There are some works on centralised drayage management, but most of them consider the problem only from a static and deterministic perspective. The main objective is normally the assignment of transportation tasks to the different vehicles, often with the presence of time windows. The work we present here considers the knowledge of the vehicles’ real-time position, which permanently enables the planner to reassign tasks in case the problem conditions change. This exact knowledge of position of the vehicles is possible thanks to a geographic positioning system by satellite (GPS, Galileo, Glonass). This additional data are used to dynamically improve the solution

Integrating Strategic and Tactical Rolling Stock Models with Cyclical Demand

In the transportation industry, companies position rolling stock where it is likely to be needed in the face of a pronounced weekly cyclical demand pattern in orders. Strategic policies based on assumptions of repetition of cyclical weekly patterns set rolling stock targets; during tactical execution, a myriad dynamic influences cause deviations from strategically set targets. We find that optimal strategic plans do not agree with results of tactical modeling; strategic results are in fact suboptimal in many tactical situations. We discuss managerial implications of this finding and how the two modeling paradigms can be reconciled

Comparativa de técnicas metaheurísticas para la optimización del problema del acarreo terrestre

El presente ejercicio aborda diferentes técnicas de resolución aplicadas al problema del acarreo terrestre con ventanas temporales. Estas técnicas se encuentran en el campo de la metaheurística y trataran de resolver en mayor o menor medida el problema planteado. El problema del acarreo terrestre se centra en el transporte y entrega de mercadería, recursos, o diferente tipo de mercadería en diferentes nodos dentro de unos horizontes temporales que han sido marcados de antemano. En concreto, se trata de dar respuesta a las necesidades logísticas del mercado. Para lograr dar una buena respuesta, se usan varios tipos de medios como pueden ser medios terrestres, aéreos o marítimos. Por tanto, el ejercicio que se plantea en este trabajo podría denominarse como: una parte de la cadena intermodal de transporte que da cobertura a un conjunto de nodos (diferentes puntos que demandan este servicio) y que, además, se provee con un solo tipo de transporte. En el caso estudiado, el método de transporte estará representado por camiones que realizarán el proceso de provisión entre los nodos. En breves palabras, el objetivo del problema queda definido entonces por la reducción del uso de los camiones en número y cantidad de kilómetros que estos recorren. Atendiendo a la forma de resolver el caso, el ejercicio ha sido enfocado bajo un enfoque metaheurístico. Se han propuesto 3 técnicas diferentes: Búsqueda tabú, Recocido Simulado y Algoritmo genético. Esta manera de resolver el problema logra diferenciar este trabajo de los demás presentados hasta el momento, y así poder ver cómo afecta cada una de esta metaheurísticas a la respuesta final del ejercicio. Para la resolución del ejercicio, se planteará una batería de 12 problemas diferentes que serán resueltos por cada una de las metaheurísticas un número determinado de veces para poder certificar la calidad de estas. Analizando la literatura científica existente, el presente problema ha sido estudiado en diferentes ocasiones. De manera, que lo que se propone en este ejercicio es comprobar la funcionalidad de las tres técnicas metaheurísticas propuestas, comparándolas para cada una de los diferentes escenarios. En cada escenario, el problema será resuelto un número definido de veces por cada método, viéndose así la homogeneidad y calidad que cada uno presenta.Universidad de Sevilla. Máster Universitario en Organización Industrial y Gestión de Empresa

Development Of Models And Solution Methods For Different Drayage Applications

In the last decades, intermodal freight transport is becoming more attractive in the global supply chains and freight transport policy makings. Intermodal freight transport provides a cost-effective, reliable, and efficient movement of freight by utilizing the strengths of different transport modes. The initial and final segment of intermodal freight transport, performed by truck, is known as “drayage.” The scheduling of truck movements in drayage operation within the service area of an intermodal terminal is an operational problem which leads to a truck scheduling problem that determines the efficient schedule of trucks while satisfying all transportation demands and constraints. Drayage accounts for a large percentage of the origin-destination expenses in the intermodal transport. Efficient planning of the drayage operations to improve the economic performance of this operation can increase the efficiency and attractiveness of intermodal transport. The primary objective of this research is to apply operation research techniques to optimize truck movements in drayage operation. The first study in this dissertation considers the drayage problem with time constraints at marine container terminals imposed by the truck appointment system and time-windows at customer locations. A mathematical model is proposed that solve the empty container allocation problem, vehicle routing problem, and appointment booking problem in an integrated manner. This model is an extension of a multiple traveling salesman problem with time windows (m-TSPTW) which is known to be NP-hard (i.e., non-deterministic polynomial-time hard). To solve this model, a reactive tabu search (RTS) algorithm is developed and its accuracy and computational efficiency are evaluated against an industry-established solver IBM ILOG CPLEX. In comparison with the CPLEX, RTS was able to find optimal or near-optimal solution in significantly shorter time. This integrated approach also allows for more accurate evaluation of the effects of the truck appointment system on the drayage operation. The second study extends the drayage literature by incorporating these features in drayage problem: (1) treating tractor, container, and chassis as separate resources which are provided in different locations, (2) ensuring that container and chassis are of the same size and type, (3) considering the possibility that drayage companies can sub-contract the work to owner-operators, and (4) a heterogeneous mix of drayage vehicles (from company fleet and owner-operators) with different start and end locations is considered; drayage company’s trucks start at company’s depot and should return to one of the company’s depots whereas owner-operators’ trucks should return to the same location from where they originated. A mixed-integer quadratic programming model is developed that solves scheduling of tractors, full containers, empty containers, and chassis jointly. A RTS algorithm combined with an insertion heuristic is developed to tackle the problem. The experimental results demonstrated the feasibility of the developed model and solution methodology. The results show that the developed integrated model is capable of finding the optimal solutions and is solvable within a reasonable time for operational problems. This new model allowed us to assess the effectiveness of different chassis supply models on drayage operation time, the percentage of empty movements and air emissions. The fourth work builds on our previous work and extends the integrated drayage scheduling model to consider uncertainty in the (un)packing operation. Recognizing the inherent difficulty in obtaining an accurate probability distribution, this paper develops two new stochastic drayage scheduling models without explicit assumption about the probability distributions of the (un)packing times. The first model assumes that only the mean and variance of the (un)packing times are available, and the second model assumes that the mean as well as the upper and lower bounds of the (un)packing times are available. To demonstrate the feasibility of the developed models, they are tested on problem instances with real-life characteristics. Future work would address the real-time scheduling of drayage problem. It would assume trucks’ locations, travel times, and customer requests are updated throughout the day. We would propose a solution approach for solving such a complex model. The solution approach would be based on re-optimization of the drayage problem and consist of two phases: (1) initial optimization at the beginning of the day, and (2) re-optimization during operation. The third study of this dissertation addresses the impact of a new trend in the North American intermodal terminals in using second-tier facilities on drayage operation. These facilities are located outside the terminals and are used to store loaded containers, empty containers, and chassis. This work builds on our previous work and extends the integrated drayage scheduling model to incorporate these features into drayage problem: (1) trucks do not have to wait at customers’ locations during the packing and unpacking operations, (2) drayage operations include a drop yard (i.e., second-tier facility) for picking up or/and dropping off loaded containers outside the marine container terminal, and (3) the job requests by customers is extended to include empty container pickup, loaded container pickup, empty container delivery, and loaded container delivery. As the mathematical model is an extension of the m-TSPTW, a RTS combined with an insertion heuristic developed by the authors is used to solve the problems

Optimización del acarreo terrestre considerando vehículos con capacidad de carga de varios contenedores

Este Trabajo de Fin de Máster trata de resolver un problema de acarreo terrestre considerando vehículos con capacidad de carga de varios contenedores mediante la aplicación del algoritmo tabú y el recocido simulado. El problema aborda tanto el transporte de contenedores de diferentes dimensiones como el manejo de los contenedores vacíos. Además, este se plantea desde tres perspectivas diferentes, en la primera cada camión puede transportar únicamente un contenedor. La segunda versión, permite el acarreo de varios contenedores limitados por su tamaño y la última, añade a esta un límite de carga de peso por camión. Así pues, se analiza el comportamiento de cada una de las perspectivas en función de su modelado y se compara la eficiencia de la búsqueda tabú frente al método del recocido simulado.This aims to solve drayage optimization with multi-size containers problem throughout the taboo algorithm and simulated annealing. The problem addresses both the transport of containers of different dimensions and the handling of empty containers. In addition, this is approached from three different perspectives, in the first one each truck can transport only one container. The second version allows the transport of several containers limited by their size and the last one adds to this a weight limit per truck. Thus, the behavior of each perspective is analyzed according to its modeling and the efficiency of the taboo search is compared with the simulated annealing method.Universidad de Sevilla. Máster Universitario en Ingeniería Industria

Dissecting Drayage: An Examination of Structure, Information, and Control in Drayage Operations

The term dray dates back to the 14th century when it was used commonly to describe a type of very sturdy sideless cart . In the 1700s the word drayage came into use meaning “to transport by a sideless cart”. Today, drayage commonly refers to the transport of containerized cargo to and from port or rail terminals and inland locations. With the phenomenal growth of containerized freight since the container’s introduction in 1956, the drayage industry has also experienced significant growth. In fact, according to the Bureau for Transportation Statistics, the world saw total maritime container traffic grow to approximately 417 million twenty foot equivalent units (TEUs) in 2006. Unfortunately, the drayage portion of a door-to-door container move tends to be the most costly part of the move. There are a variety of reasons for this disproportionate assignment of costs, including a great deal of uncertainty at the interface of modes. For example, trucks moving containers to and from a port terminal are often uncertain as to how long it will take them to pick up a designated container coming from a ship, from the terminal stack, or from customs. This uncertainty leads to much difficulty and inefficiency in planning a profitable routing for multiple containers in one day. We study this problem from three perspectives using both empirical and theoretical techniques