On green routing and scheduling problem

The vehicle routing and scheduling problem has been studied with much interest within the last four decades. In this paper, some of the existing literature dealing with routing and scheduling problems with environmental issues is reviewed, and a description is provided of the problems that have been investigated and how they are treated using combinatorial optimization tools

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

Integrated Intermodal Network Design with Nonlinear Inter-Hub Movement Costs

In this research, transportation mode and load route selection problems are integrated with the hub location problem in a single mathematical formulation to find the optimal design of intermodal transportation networks. Economies of scale are modeled utilizing a stepwise function that relates the per container transportation cost to the amount of flow between two nodes. A heuristic method combining a genetic algorithm and the shortest path algorithm was developed to solve this integrated planning problem. Computational experiments were completed to evaluate the performance of the proposed heuristic for different problem instances. At the end, conclusions are presented and future research directions are discussed

Booking limits and bid price based revenue management policies in rail freight transportation

In this paper, the possibility and potential benefits of implementing discriminatory policies in rail freight transportation are analyzed, with the aim of revenue maximization. A regular, cyclic, single train service with fixed composition and capacity is studied. The problem is decomposed into discrete time periods. Transportation requests arise randomly over time, and the decision of either accept or reject a certain request has to be made. The problem is formulated via dynamic programming, and the deterministic approximations of the problem are used in order to formulate booking limits and bid price policies. Results obtained are compared with those of standard first come – first served policy, which is implemented by the Serbian railways. Although not acceptable in all contexts, the proposed aggressive policies demonstrated promising benefits

Anticipatory freight selection in intermodal long-haul round-trips

We consider the planning problem faced by Logistic Service Providers (LSPs) transporting freights periodically, using long-haul round-trips. In each round-trip, freights are delivered and picked up at different locations within one region. Freights have time-windows and become known gradually over time. Using probabilistic knowledge about future freights, the LSP’s objective is to minimize costs over a multi-period horizon. We propose a look-ahead planning method using Approximate Dynamic Programming. Experiments show that our approach reduces costs up to 25.5% compared to a single-period optimization approach. We provide managerial insights for several intermodal long-haul round-trips settings and provide directions for further research

Modeling the Multicommodity Multimodal Routing Problem with Schedule-Based Services and Carbon Dioxide Emission Costs

We explore a freight routing problem wherein the aim is to assign optimal routes to move commodities through a multimodal transportation network. This problem belongs to the operational level of service network planning. The following formulation characteristics will be comprehensively considered: (1) multicommodity flow routing; (2) a capacitated multimodal transportation network with schedule-based rail services and time-flexible road services; (3) carbon dioxide emissions consideration; and (4) a generalized costs optimum oriented to customer demands. The specific planning of freight routing is thus defined as a capacitated time-sensitive multicommodity multimodal generalized shortest path problem. To solve this problem systematically, we first establish a node-arc-based mixed integer nonlinear programming model that combines the above formulation characteristics in a comprehensive manner. Then, we develop a linearization method to transform the proposed model into a linear one. Finally, a computational experiment from the Chinese inland container export business is presented to demonstrate the feasibility of the model and linearization method. The computational results indicate that implementing the proposed model and linearization method in the mathematical programming software Lingo can effectively solve the large-scale practical multicommodity multimodal transportation routing problem