Jacksonville, FL, United States
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Peng F.,CSX Transportation Inc | Ouyang Y.,University of Illinois at Urbana - Champaign
Computer-Aided Civil and Infrastructure Engineering | Year: 2014

Railroad job clustering is an important part of railroad track maintenance planning. It focuses on clustering track maintenance jobs into projects, so that the projects can be assigned to the production teams and scheduled in the planning horizon. The real-world instances of job-clustering problem usually have a very large scale, involving thousands of jobs per year. Various difficult side constraints such as mutual exclusion constraints and rounding constraints further increase the difficulty in solving the problem. In this article, we develop a mixed-integer mathematical programming model in the form of vehicle routing problem with side constraints, and propose a set of integrated heuristic algorithms to solve the problem. The proposed model and algorithms are shown to be effective and have been adopted by a Class-I railroad to help their practical operations for a few years. © 2013 Computer-Aided Civil and Infrastructure Engineering.


Hajibabai L.,University of Illinois at Urbana - Champaign | Nourbakhsh S.M.,University of Illinois at Urbana - Champaign | Ouyang Y.,University of Illinois at Urbana - Champaign | Peng F.,CSX Transportation. Inc.
Transportation Research Record | Year: 2014

The routing of snowplow trucks in urban und regional areas encompasses a variety of complex decisions, especially for jurisdictions with heavy snowfall. The main activities involve dispatching a fleet of snowplow trucks from u central depot or satellite facility to clean and spread salt and chemicals on the network links (i.e., snow routes). In this paper, a mixed integer linear program model Is proposed to minimize the total operation time of all snowplow trucks needed to complete a given set of snow routes with multiple plowing priorities and to reduce the longest individual truck operation time. Customized construction and local search solution algorithms arc developed and used to design snow routes for an empirical application. The computational results show that the proposed solution approach Is able to solve the problem effectively and the model result outperforms the current solution in practice. The proposed models and algorithms are also incorporated into the development of a state-of-the-art snowplow routing software that can help planners optimize snow routes and evaluate options for resource allocation.


Peng F.,CSX Transportation Inc. | Ouyang Y.,University of Illinois at Urbana - Champaign | Somani K.,CSX Transportation Inc.
Journal of Rail Transport Planning and Management | Year: 2013

Railroads use a set of rail inspection teams to periodically examine the status of rail tracks across the railroad network. The rail inspection scheduling problem (RISP) is a large-scale routing and scheduling problem where thousands of inspection tasks are to be scheduled subject to many complex constraints. This paper proposes a vehicle routing problem formulation for RISP and develops a customized heuristic algorithm to effectively solve the problem. Real-world case studies show that the proposed approach significantly outperforms commercial solvers and the state-of-art manual solution approach. The proposed approach has been adopted by a Class I railroad to enhance safety and operational efficiency. © 2014 Elsevier Ltd.


Peng F.,CSX Transportation Inc. | Hwang T.,University of Illinois at Urbana - Champaign | Ouyang Y.,University of Illinois at Urbana - Champaign
Transportation Research Record | Year: 2013

Formulation and solution algorithms are proposed for a discrete group assembly problem in which a number of arbitrarily located objects in a network need to travel to some assembly points so that the subgraph induced by them contains a spanning tree whose edge lengths are all less than a predetermined distance. The objective of this problem is to find the optimal assembly location for each object so as to minimize the total travel distance of all objects from their initial locations to assembly points. This problem was motivated by several real-world applications in a range of contexts. The problem was formulated into a mixed-integer mathematical program, and effective algorithms such as neighborhood search were developed to obtain near-optimum solutions. Computational results for a number of experimental problem instances show that the proposed algorithms are able to give good solutions in a short amount of time.


Bai Y.,Rutgers University | Li X.,Mississippi State University | Peng F.,CSX Transportation Inc. | Wang X.,University of Illinois at Urbana - Champaign | Ouyang Y.,University of Illinois at Urbana - Champaign
Energies | Year: 2015

While ever-growing bio-ethanol production poses considerable challenges to the bioenergy supply chain, the risk of refinery operation disruptions further compromises the efficiency and reliability of the energy supply system. This paper applies discrete and continuous reliable facility location models to the design of reliable bio-ethanol supply chains so that the system can hedge against potential operational disruptions. The discrete model is shown to be suitable for obtaining the exact optimality for small or moderate instances, while the continuous model has superior computational tractability for large-scale applications. The impacts of both site-independent and dependent disruptions (i.e., due to flooding) are analyzed in empirical case study for the State of Illinois (one of the main biomass supply states in the U.S.). The reliable solution is compared with a deterministic solution under the same setting. It is found that refinery disruptions, especially those site-dependent ones, affect both optimal refinery deployment and the supply chain cost. Sensitivity analysis is also conducted to show how refinery failure probability and fixed cost (for building biorefineries) affect optimal supply chain configuration and the total expected system cost. © 2015 by the authors.


Erenay B.,Ohio University | Suer G.A.,Ohio University | Huang J.,CSX Transportation Inc. | Maddisetty S.,Ohio University
Computers and Industrial Engineering | Year: 2015

In this study, a mathematical programming approach is proposed to design a layered cellular manufacturing system in highly fluctuated demand environment. A mathematical model is developed to create dedicated, shared and remainder cells with the objective of minimizing the number of cells. In contrast with classical cellular manufacturing systems, in layered cellular systems, some cells can serve to multiple part families. A five-step hierarchical methodology is employed: (1) formation of part families, (2) calculation of expected cell utilizations and demand coverage probabilities, (3) specification cell types as dedicated, shared, and remainder cells, (4) simulation of proposed layered systems to evaluate their performance with respect to average flowtime and work-in-process inventory, and (5) statistical analysis to find the best layered cellular design among alternatives. It is found that designs with higher number of part families tend to have less number of machines. Similar results are also observed with respect to average flowtime and work-in-process inventory measures. The results are also compared with a heuristic approach from the literature. None of the approaches is dominant with respect to all of the performance measures. Mathematical modeling approach performs better in terms of number of machines for most of the alternative designs. However, heuristic approach yields better average flowtime and work-in-process inventory for most of the designs. ©2015 Elsevier Ltd. All rights reserved.

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