Freight Train Optimization and Simulation - Research Proposal Example

number Report on freight train optimization and simulation Introduction Transport logistics is among the most important and critical factors that determine the cost and supply rate of nearly all freight. Rail transport which is at the heart of the freight transport is a complex, and busy network. When properly utilized, this system will minimize and manage transportation costs immensely. Freight train scheduling has in the recent past received a lot of attention especially considering the increased volume of goods. The most probable solution of handling the increased freight would have been an extension of the railway systems. This however, is rare as it presents challenges in terms of cost and implementation to the stakeholders. On the same note, the current train system is operating at its optimal with which come numerous challenges and safety concerns (Ballis et al, 2004). This calls for a more efficient and effecting freight train optimisation and simulation capable of mitigating the emerging concerns, and even proceed further to predict possible future concerns. Factors considered in the rail freight transport were crew scheduling and train scheduling. This is because, unlike passenger train schedules which can be planned and allocated a slot well in advance, Freight trains are dynamic and require a shorter planning period. Freight train systems vary depending on availability of goods and distance required to be covered. This paper provides an overview of freight transport and the possible effects optimization of the rail system will cause. Problem Statement In any environment and or country that have a considerable amount of shipment rail transport is always the centre of focus. Freight train has faced an increased attention due to problems related with road congestion and safety concerns. Furthermore, large container shipments are always a problem at major ports. A good organisation and prioritisation of freight flows and validating an expedition and efficient freight train systems will go a long way in ensuring speedy processing and clearing of these shipment containers. Motivation/Applications Railway transports system is uniquely designed to deliver efficiency and reliability as it allows for massive freight transport at low and affordable rates. This reliance and steady growth on rail transport however, cannot be fully matched by an equal growth pace of the rail system and network. The increased traffic therefore relies on a nearly out-dated system that requires urgent revision. The hampered growth pace is caused by several factors the most obvious being the high costs involved in such an investment (Rizzoli, 2002). Additionally, due to the bulk of both the locomotive trains and its related infrastructure, expansion space required is also a concern especially when it comes to residential and ragged terrain surroundings. Railway companies therefore, are left out with limited options the most significant of which is exploitation of the available resources. This can be done through the implementation of better systems that will facilitate better more efficient time management. Furthermore, additional test environments can be developed to study and understand the various challenges involved on the ground during freight train movement to mitigate risks involved. Objectives The objective of this report is to provide an in depth understanding of the freight train optimization and simulation system. Current conditions will be exploited with the objective of coming up with an optimal train scheduling and optimization technique under an array of realistic and probable conditions. These conditions include; infrastructure I place, optimal average speed done by each train, and the inflow of freight goods. In addition, this report seeks to alleviate the efficiency of freight train transportation by highlighting all the impacts it has on the transport system, and further proposing ways to better improve and handle safety measures already in place. These will be accomplished by first, proposing a freight simulation model and an intelligent database that can handle current and future logistics. This model should be able to emulate networks already in place so as to improve the current freight optimization, identify loopholes and provide a better managed system capable of handling unforeseen complications. Finally, this report aims at presenting the pros and cons of freight optimization and simulation with the hope of coming up with a model that best suits the freight rail transportation. Basic Assumptions/Limitations For the purposes of this research the following assumptions have been made in order to fully understand the application and utilisation of proposed measures. Firstly, the current train system operates a single rail channel as such allows only one train at a given time to be on the track. This essentially creates a deadlock in that two trains travelling in opposite directions cannot under any circumstance be online at the same time. Not only does this make their timing and management crucial, but also means that several delays are bound to occur even under the most efficient systems. Secondly, all shipments are transferred between involved trains at minimum number of stations. On the same note, a shipment is allocated time, minimum and maximum, to practicably make a connection or be transferred between trains. Strategic planning In their research Crainic and laporte discuss significant models that can be used to strategically plan on proper optimization and simulation. These models are location models, network design, and regional multimodal models which are indeed suitable for efficiently optimising the current system. Location models can be used in determining the ideal location of new terminals that will best serve both economical and logical implementation. Network design models are mainly concerned with the set up requirements of the network, how best they can be placed to avoid collisions and accident while at the same time economizing on available space. Finally regional models encompass the entire rail system in a given region, the main products that are in use as well as the freight flows both inbound and outbound. Literature Review A general overview of the freight train transport system is well described by Zografos and Regan and by Vrenken et al. They bring forth the idea of intermodal freight transport and the various challenges that are involved if the system is not optimized. Furthermore, in their research Macharis and Bontekoning reveal the various opportunities that arise in the incorporation and optimization of rail transport system. The authors give a detailed review of research models that currently are in use, and define the challenges that require urgent resolution to further make the freight system efficient. Considering this is a relatively new and wide field of research, most research papers and literature on optimization of freight train and simulation do not cover all possible angles. This report therefore will encompass a significant content from the research and further provide relevant updates on left out areas. Various models have been proposed for freight train schedule optimization and can be categorised into three. There is integer Linear Program formulation, which encompasses a lot of variables therefore has scalability issues. Despite its limitation this still is the most popular approach. The second classification of models is time-indexed column generation. This class offers optimal results but is periodic and supports mostly small coverage systems. The third and final classification corresponds to semi-infinite linear programs. This is a relatively unpopular and new class that has not been extensively documented. Optimization and simulation Background In freight train optimization, it is crucial that the departure and arrival time of the trains at exact station is determined. A set of linear inequalities are used to determine the arrival and departure times which are defined by corresponding decision variables (Zografos. Et. Al, 2004). The solution thereafter is chosen from a set of possible solutions arrived at based on a given objective or selected criterion. Often in less complicated scenarios, an algorithm gives desired results within a given period of time if all conditions hold still (Vrenken et al, 2005). However, this does not hold water for complex situations that involve multiple stations, trains and a long period of time where many variables are bound to change. On the other hand, a simpler algorithm can be used to give one time or short term solutions. A heuristic algorithm is easily designed and can offer the desired results when required. This algorithm however does not guarantee an optimal solution and is based on several presumptions. In addition, it is trivial to develop heuristic algorithms for all possible cases as such it is not popular. A simulation model enables developers and decision makers of these algorithms to test and evaluate their ideas before actual implementation. Simulation has the capacity to integrate several related factors into the model under near similar and realistic conditions. The algorithm models presented in this report determines the arrival and departure time of every train at each location without creating any conflict at all times (Ferreira, 1995). On the other hand, simulation models present first hand realistic decisions on the ground which means therefore, arrival and departure times are not computed at once and variables can be later introduced to note the changes and adjustments made. While simulation strives to achieve given objectives, it does not guarantee optimality of the schedules and therefore is more of a planning than in is a scheduling tool. Conclusion In summary, the future plans of freight management and rail transport are in the automation of the system. This should be handled by a fast and reliable loading system, railcar compartments capable of simultaneous transportation of multiple containers, and capable track that will handle the heavy load and high speed of the trains. Works Cited A. Ballis, and J. Golias, Towards the improvement of a combined transport chain performance. European Journal of Operational Research, (2004). A.E. Rizzoli, N. Fornara and L.M. Gambardella, A simulation tool for combined rail/road transport in intermodal terminals. Mathematics and Computers in Simulation, (2002). H. Vrenken, C. Macharis and P. Wolters, Intermodal Transport in Europe. European Intermodal Association, Brussels (2005). K.G. Zografos and A.C. Regan, Current Challenges for Intermodal Freight Transport and Logistics in Europe and the United States. Transportation Research Record 1873, (2004). L. Ferreira and J. Sigut, Modelling intermodal freight terminal operations. Road and Transport Research Journal 4, (1995). Read More