Routing and scheduling temporal and heterogeneous freight car traffic on rail networks

Kwon, Oh Kyoung; Martland, C D; Sussman, Joseph M.

doi:10.1016/s1366-5545(97)00022-7

Cited by 56 publications

(32 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They developed a procedure that involves iteratively solving a linear programming relaxation and rounding some of the resulting fractional values until a feasible integral solution is found. Another paper by Kwon et al [24] deals with the algorithm to improve a given blocking plan and block-to-train assignment. They formulate the problem as a linear multicommodity flow problem and use the column generation technique as a solution approach.…”

Section: The Block-to-train Assignment Problemmentioning

confidence: 99%

New approaches for solving the block‐to‐train assignment problem

2007

View full text Add to dashboard Cite

Railroad planning involves solving two optimization problems: (i) the blocking problem, which determines what blocks to make and how to route traffic over these blocks; and (ii) the train schedule design problem, which determines train origins, destinations, and routes. Once the blocking plan and train schedule have been obtained, the next step is to determine which trains should carry which blocks. This problem, known as the block-to-train assignment problem, is considered in this paper. We provide two formulations for this problem: an arc-based formulation and a path-based formulation. The latter is generally smaller than the former, and it can better handle practical constraints. We also propose exact and heuristic algorithms based on the path-based formulation. Our exact algorithm solves an integer programming formulation with CPLEX using both a priori generation and dynamic generation of paths. Our heuristic algorithms include a Lagrangian relaxation-based method as well as a greedy construction method. We present computational results of our algorithms using the data provided by a major US railroad. We show that we can obtain an optimal solution of the block-to-train assignment problem within a few minutes of computational time, and can obtain heuristic solutions with 1-2% deviations from the optimal solutions within a few seconds.

show abstract

Section: The Block-to-train Assignment Problemmentioning

confidence: 99%

New approaches for solving the block‐to‐train assignment problem

2007

View full text Add to dashboard Cite

show abstract

“…The MCNF mathematical model was involved in many real-world OR applications. The most recent applications of MCNF models are in telecommunication network routing [51][52][53][54], routing and scheduling in transportation and logistics [55][56][57][58][59], production scheduling and planning [60,61], VLSI design [62][63][64][65], traffic equilibrium [66,67], graph partitioning [68][69][70][71][72], and network design [73][74][75][76][77]. Wang [46] provides reviews on some of these applications.…”

Section: Mcnf Problem and Solution Algorithmsmentioning

confidence: 99%

Dynamic Decision Making for Less-Than-Truckload Trucking Operations

Hejazi

Haghani

2007

Transportation Research Record

View full text Add to dashboard Cite

On a typical day, more than 53 million tons of goods valued at about $36 million are moved on the US multimodal transportation network. An efficient freight transportation industry is the key in facilitating the required movement of raw materials and finished products. Among different modes of transportation, trucking remains the shipping choice for many businesses and is increasing its market share.Less-than-truckload (LTL) trucking companies provide a transportation service in which several customers are served simultaneously by using the same truck and shipments need to be consolidated at some terminals to build economical loads.Intelligent transportation system (ITS) technologies increase the flow of available data, and offer opportunities to control the transportation operations in realtime. Some research efforts have considered real-time acceptance/rejection of shipping requests, but they are mostly focused on truckload trucking operations. This study tries to use real-time information in decision making for LTL carriers in a dynamically changing environment.The dissertation begins with an introduction of LTL trucking operations and different levels of planning for this type of motor carriers, followed by the review of literature that are related to tactical and operational planning. Following a brief discussion on multi commodity network flow problems and their solution algorithm, a mathematical model is proposed to deal with the combined shipment and routing problem.Furthermore, a decision making procedure as well as a decision support application are developed and are presented in this dissertation. The main step in the decision making procedure is to solve the proposed mathematical problem. Three heuristic solution algorithms are proposed and the quality of the solutions is evaluated using a set of benchmark solutions.Three levels of numerical experiments are conducted considering an auto carrier that operates on a hub-and-spoke network. The accuracy of the mathematical model and the behavior of the system under different demand/supply situations are examined. Also, the performance of the solutions provided by the proposed heuristic algorithms is compared and the best solution method is selected. The study suggests that significant reductions in operational costs are expected as the result of using the proposed decision making procedure.

show abstract

“…If k is greater than 1, the train departs from station S 1 for the kth time as soon as it has arrived in station S 1 for the (k − 1)th time, the passengers have gotten the time to get out of the train and the earliest departure time indicated in the timetable has passed. Note that under nominal operations the kth train on track T 1 (e.g., the one that departs from station S 1 at, e.g., 10.00 a.m.) gives connection to the (k − 1)th train on track T 4 (which has departed from station S 4 at 9.45 a.m.) and not to the kth train on track T 4 (which will depart from station S 4 at 10.45 a.m.). This implies that the train arrives in station S 1 for the (k − 1)th time at time instant x 4 (k − 1) + a 4,1 (k).…”

Section: Worked Examplementioning

confidence: 99%

“…Work in connection with the modeling and control of railway networks in a systems and control context can be found in [2,3,7,12]. We also refer the interested reader to [9,10,11,13] and the references therein.…”

Section: Introductionmentioning

confidence: 99%

Model Predictive Control Approach for Recovery from Delays in Railway Systems

Schutter

Boom

Hegyi

2002

Transportation Research Record

View full text Add to dashboard Cite

We extend the model predictive control (MPC) framework, which is a very popular controller design method in the process industry, to transfer coordination in railway systems. In fact, the proposed approach can also be used for other systems with both hard and soft synchronization constraints, such as logistic operations. The main aim of the control is to recover from delays in an optimal way by breaking connections (at a cost). In general, the MPC control design problem for railway systems leads to a nonlinear non-convex optimization problem. We show that the optimal MPC strategy can also be computed using an extended linear complementarity problem. Furthermore, we present an extension with an extra degree of freedom to recover from delays by letting some trains run faster than usual (again at a cost). The resulting extended MPC railway problem can also be solved using an extended linear complementarity problem.

show abstract

Routing and scheduling temporal and heterogeneous freight car traffic on rail networks

Cited by 56 publications

References 6 publications

New approaches for solving the block‐to‐train assignment problem

New approaches for solving the block‐to‐train assignment problem

Dynamic Decision Making for Less-Than-Truckload Trucking Operations

Model Predictive Control Approach for Recovery from Delays in Railway Systems

Contact Info

Product

Resources

About