A viral system to optimise the daily drayage problem

The intermodal transport chain can become more efficient by means of a good organisation 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. This paper focuses on a new approach to tackle the daily drayage problem by the use of viral system (VS). VS is a novel bio-inspired approach that makes use of a virus-infection biological analogy that is producing very satisfactory results when dealing with complex problems with huge feasibility region.Unión Europea TEC2013-47286-C3-3-

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

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

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

Off-peak truck deliveries at container terminals: the 'Good Night' program in Israel

Purpose – Avoiding truck congestion and peaks in landside activity is one of the challenges to container terminal managers. The spreading of truck arrivals at terminals can be facilitated by widening the opening hours of terminals at the landside. Israel’s Ministry of Transport has instituted the “Good Night Program”, involving monetary incentives for importers and exporters who deliver containers to ports at night. Design/methodology/approach – This paper aims to quantitatively examine the market utility resulting from shifting traffic from daytime to nighttime, and analyzes customer considerations regarding nighttime transportation. Findings – The external utility found in the traffic-economics model is quite similar to the economic incentive given to customers. Therefore, a significant increase of the incentive is not feasible. Originality/value – Furthermore, it seems that an incentive method by itself is not effective enough, and does not motivate customers to act and find creative solutions to the obstacles they face. To achieve a considerable change in nighttime transport to Israeli ports, more effective methods should be examined

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

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

An efficient mixed integer programming model for pairing containers in inland transportation based on the assignment of orders

The inland transportation takes a significant portion of the total cost that arises from intermodal transportation. In addition, there are many parties (shipping lines, haulage companies, customers) who share this operation as well as many restrictions that increase the complexity of this problem and make it NP-hard. Therefore, it is important to create an efficient strategy to manage this process in a way to ensure all parties are satisfied. This paper investigates the pairing of containers/orders in drayage transportation from the perspective of delivering paired containers on 40-ft truck and/or individual containers on 20-ft truck, between a single port and a list of customer locations. An assignment mixed integer linear programming model is formulated, which solves the problem of how to combine orders in delivery to save the total transportation cost when orders with both single and multiple destinations exist. In opposition to the traditional models relying on the vehicle routing problem with simultaneous pickups and deliveries and time windows formulation, this model falls into the assignment problem category which is more efficient to solve on large size instances. Another merit for the proposed model is that it can be implemented on different variants of the container drayage problem: import only, import–inland and import–inland–export. Results show that in all cases the pairing of containers yields less cost compared to the individual delivery and decreases empty tours. The proposed model can be solved to optimality efficiently (within half hour) for over 300 orders