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Greedy Randomized Adaptive Search Procedures
, 2002
"... GRASP is a multistart metaheuristic for combinatorial problems, in which each iteration consists basically of two phases: construction and local search. The construction phase builds a feasible solution, whose neighborhood is investigated until a local minimum is found during the local search phas ..."
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Cited by 647 (82 self)
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GRASP is a multistart metaheuristic for combinatorial problems, in which each iteration consists basically of two phases: construction and local search. The construction phase builds a feasible solution, whose neighborhood is investigated until a local minimum is found during the local search phase. The best overall solution is kept as the result. In this chapter, we first describe the basic components of GRASP. Successful implementation techniques and parameter tuning strategies are discussed and illustrated by numerical results obtained for different applications. Enhanced or alternative solution construction mechanisms and techniques to speed up the search are also described: Reactive GRASP, cost perturbations, bias functions, memory and learning, local search on partially constructed solutions, hashing, and filtering. We also discuss in detail implementation strategies of memorybased intensification and postoptimization techniques using pathrelinking. Hybridizations with other metaheuristics, parallelization strategies, and applications are also reviewed.
The Quadratic Assignment Problem: A Survey and Recent Developments
 In Proceedings of the DIMACS Workshop on Quadratic Assignment Problems, volume 16 of DIMACS Series in Discrete Mathematics and Theoretical Computer Science
, 1994
"... . Quadratic Assignment Problems model many applications in diverse areas such as operations research, parallel and distributed computing, and combinatorial data analysis. In this paper we survey some of the most important techniques, applications, and methods regarding the quadratic assignment probl ..."
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Cited by 109 (16 self)
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. Quadratic Assignment Problems model many applications in diverse areas such as operations research, parallel and distributed computing, and combinatorial data analysis. In this paper we survey some of the most important techniques, applications, and methods regarding the quadratic assignment problem. We focus our attention on recent developments. 1. Introduction Given a set N = f1; 2; : : : ; ng and n \Theta n matrices F = (f ij ) and D = (d kl ), the quadratic assignment problem (QAP) can be stated as follows: min p2\Pi N n X i=1 n X j=1 f ij d p(i)p(j) + n X i=1 c ip(i) ; where \Pi N is the set of all permutations of N . One of the major applications of the QAP is in location theory where the matrix F = (f ij ) is the flow matrix, i.e. f ij is the flow of materials from facility i to facility j, and D = (d kl ) is the distance matrix, i.e. d kl represents the distance from location k to location l [62, 67, 137]. The cost of simultaneously assigning facility i to locat...
RealTime Optimization of Containers and Flatcars for Intermodal Operations
 TRANSPORTATION SCIENCE
, 1998
"... We propose a dynamic model for optimizing the flows of flatcars that considers explicitly the broad range of complex constraints that govern the assignment of trailers and containers to a flatcar. The problem is formulated as a logistics queueing network which can handle a wide range of equipmenttyp ..."
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Cited by 15 (0 self)
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We propose a dynamic model for optimizing the flows of flatcars that considers explicitly the broad range of complex constraints that govern the assignment of trailers and containers to a flatcar. The problem is formulated as a logistics queueing network which can handle a wide range of equipmenttypes and complex operating rules. The complexity of the problem prevents a practical implementation of a global network optimization model. Instead, we formulate a global model with the specific goal of providing network information to local decision makers, regardless of whether they are using optimization models at the yard level. Thus, our approach should be relatively easy to implement given current rail operations. Initial experiments suggest that a flatcar fleet that is managed locally, without the benefit of our network information, can achieve the same demand coverage as a fleet that is 10 percent smaller, but which is managed locally with our network information.
Planning problems in intermodal freight transport: Accomplishments and prospects
 TRANSPORTATION PLANNING AND TECHNOLOGY
, 2008
"... Intermodal freight transport has received an increased attention due to problems of road congestion, environmental concerns and traffic safety. A growing recognition of the strategic importance of speed and agility in the supply chain is forcing firms to reconsider traditional logistic services. A ..."
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Cited by 14 (0 self)
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Intermodal freight transport has received an increased attention due to problems of road congestion, environmental concerns and traffic safety. A growing recognition of the strategic importance of speed and agility in the supply chain is forcing firms to reconsider traditional logistic services. As a consequence, research interest in intermodal freight transportation problems is growing. This paper provides an overview of planning decisions in intermodal freight transport and solution methods proposed in scientific literature. Planning problems are classified according to type of decision maker and decision level. General conclusions are given and subjects for further research are identified.
Optimal Communications Systems and Network Design for Cargo Monitoring,” To appear
 in Proc. Tenth Workshop Mobile Computing Systems and Applications (HOTMOBILE
, 2009
"... In the United States there is an emerging trend to ship goods by rail directly from ports to inland intermodal traffic terminals. However, for this trend to succeed shippers must have “visibility ” into rail shipments. In this research we seek to provide visibility into shipments through optimal pla ..."
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Cited by 4 (4 self)
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In the United States there is an emerging trend to ship goods by rail directly from ports to inland intermodal traffic terminals. However, for this trend to succeed shippers must have “visibility ” into rail shipments. In this research we seek to provide visibility into shipments through optimal placement of sensor and communication technology. We formally define the notion of visibility and then highlight the objectives of our study. We also provide a generalized description of an optimization problem that has been developed to determine optimal sensor locations. Several problems must be solved to enable costeffective visibility into rail shipments. We break down these problems into tasks and discuss how they can be addressed. The expected result of the proposed research includes a model (or models) that predicts the system cost given an assignment of sensors to railbased containers. This model can be used to determine costeffective scenarios for deploying sensors to containers on a train, as well as the system tradeoffs.
SOLVING COMBINATORIAL OPTIMISATION PROBLEMS USING NEURAL NETWORKS
, 1996
"... Combinatorial optimisation problems (COP's) arise naturally when mathematically modelling many practical optimisation problems from science and engineering. Due to the NPhard nature of many COP's, heuristics are often used to provide rapid and nearoptimal solutions. Neural networks are a ..."
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Cited by 1 (0 self)
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Combinatorial optimisation problems (COP's) arise naturally when mathematically modelling many practical optimisation problems from science and engineering. Due to the NPhard nature of many COP's, heuristics are often used to provide rapid and nearoptimal solutions. Neural networks are a novel and potentially powerful alternative approach to solving such problems. They are also intrinsically parallel, with much potential for rapid hardware implementation. Unfortunately, existing neural techniques are widely considered to be unsuited to optimisation due to their tendency to produce infeasible or poor quality solutions. Over the last decade or so, two main types of neural networks have been proposed for solving COP's in particular, the Travelling Salesman Problem (TSP). The first of these neural approaches is the Hopfield neural network which evolves in such away asto minimise a system energy function. In its original form, the Hopfield energy function involves many parameters which need to be tuned, and constructing a suitable energy function which enables the network to arrive at feasible nearoptimal solutions is a difficult
DYNAMIC CONTAINER TRAIN PLANNING
"... This is the author’s version of a work that was submitted/accepted for publication in the following source: ..."
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Cited by 1 (0 self)
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This is the author’s version of a work that was submitted/accepted for publication in the following source:
In Press: Transportation Research Part E: Logistics and Transportation Review
"... Optimizing the aerodynamic efficiency of intermodal freight trains ..."
unknown title
, 2007
"... Intermodal (IM) freight is the largest segment of the US railroad freight transportation business, and is the fastest growing portion (nearly 80 % in the past 15 years) (Gallamore, 1998; Association of American Railroads (AAR), 2005). However, IM trains are generally the least fuel efficient trains ..."
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Intermodal (IM) freight is the largest segment of the US railroad freight transportation business, and is the fastest growing portion (nearly 80 % in the past 15 years) (Gallamore, 1998; Association of American Railroads (AAR), 2005). However, IM trains are generally the least fuel efficient trains. This inefficiency is due to the physical constraints imposed by the combination of loads and the railcar design (Engdahl et al.,