Dynamic approach to solve the daily drayage problem with travel time uncertainty

The intermodal transport chain can become more e cient by means of a good organization of drayage movements. Drayage in intermodal container terminals involves the pick up and delivery of containers at customer locations, and the main objective is normally the assignment of transportation tasks to the di erent vehicles, often with the presence of time windows. This scheduling has traditionally been done once a day and, under these conditions, any unexpected event could cause timetable delays. We propose to use the real-time knowledge about vehicle position to solve this problem, which permanently allows the planner to reassign tasks in case the problem conditions change. This exact knowledge of the position of the vehicles is possible using 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

The one container drayage problem with soft time windows

Intermodal freight transport consists of using different modes of transport without changing the load unit. This results in a significant reduction in the time that goods spend at intermodal terminals, where transshipment takes place. Drayage refers to the transport of freight on trucks among intermodal terminals, depots, customers and suppliers. In spite of the fact that drayage only represents between 5 and 10 percent of total distance, it may amount up to more than 30 percent of the total costs. The aim of this work is to study drayage operations. First, an extensive literature review is undertaken. Since the intermodal transport chain can become more efficient by means of a proper organisation of the drayage movements, the optimization of the daily drayage problem has been identified as one of the main ways of reducing the drayage cost and improving intermodal operations. On this problem, the lack of a common benchmark has hindered reaching further conclusions from all the research carried out. Therefore, this paper proposes a common framework and presents a generalized formulation of the problem, which allows modeling most drayage policies, with the limitation of only considering one-container problems. Results show that flexible tasks in the repositioning of empty containers as well as soft time windows can reduce the operating costs and facilitate the management of drayage companies. This work may help consider adequate policies regarding drayage operations in intermodal terminals

Using Simulated Annealing to Solve the Daily Drayage Problem with Hard Time Windows

Drayage is the stage of the intermodal transport that deals with transport of freight on trucks among the intermodal terminal, and customers and suppliers that are located in its hinterland. This work proposes an algorithm based on simulated annealing heuristics to solve the operations of drayage. This algorithm has been used to solve battery problems, demonstrating the validity and suitability of its results, which were compared with exact method

The Dynamic Improvement of Vehicle Routing: Reoptimization Events

Acta congreso: http://www.insisoc.org/CIO2013/es/documentacion/BOOK%20OF%20PROCEEDINGS%207th%20INTERNATIONAL%20CONFERENCE%20ON%20INDUSTRIAL%20ENGINEERING%20AND%20INDUSTRIAL.pdfThe static environments of optimization are not efficient when there is Uncertainty. The vehicle routing is a common case of it. For example, there usually is uncertainty in the transit time due to the congestion, traffic jam, etc. Dynamic optimization has been more efficient in these environments. To determinate when a reoptimization have to be run is fundamental.Los entornos estáticos de optimización no son todo lo eficientes que se esperaría en situaciones donde existe incertidumbre. El rutado de vehículos es una caso común donde existe incertidumbre, por ejemplo en el tiempo de tránsito, motivado principalmente por los diferentes niveles de congestión existentes. La reoptimización dinámica se ha mostrado más eficaz en este tipo de sistemas. Determinar en que momentos realizar la reoptimización es fundamental en la eficiencia de este tipo de sistemas

The Dynamic Improvement of Vehicle Routing: Reoptimization Events

Acta congreso: http://www.insisoc.org/CIO2013/es/documentacion/BOOK%20OF%20PROCEEDINGS%207th%20INTERNATIONAL%20CONFERENCE%20ON%20INDUSTRIAL%20ENGINEERING%20AND%20INDUSTRIAL.pdfThe static environments of optimization are not efficient when there is Uncertainty. The vehicle routing is a common case of it. For example, there usually is uncertainty in the transit time due to the congestion, traffic jam, etc. Dynamic optimization has been more efficient in these environments. To determinate when a reoptimization have to be run is fundamental.Los entornos estáticos de optimización no son todo lo eficientes que se esperaría en situaciones donde existe incertidumbre. El rutado de vehículos es una caso común donde existe incertidumbre, por ejemplo en el tiempo de tránsito, motivado principalmente por los diferentes niveles de congestión existentes. La reoptimización dinámica se ha mostrado más eficaz en este tipo de sistemas. Determinar en que momentos realizar la reoptimización es fundamental en la eficiencia de este tipo de sistemas

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

A Systematic Literature Review Looking at Digitizing Container Harbors

This article presents a systematic literature on the use of information technology within the field of maritime shipping. First, the review scope, the search terms, the data sources, the search process, the inclusion and exclusion criteria, and the data extraction and analysis procedures are presented. The findings show that RFID is still reported to be in its infancy. Truck appointment system might only work in certain situations as truck drivers might not have a choice of when to pick up its container. There is no centralization of the operation. Creating a digital dashboard to display potential wait-time based on past days truck companies can better plan their day if they have the chance to do so. The benefits of such system are to offer real-time information to its users. Digitalization also allows for predictive analytics to take place this takes the process to another level.publishedVersio