Dushyant Sharma - Academia.edu (original) (raw)

Papers by Dushyant Sharma

The capacitated minimum spanning tree (CMST) problem is to ÿnd a minimum cost spanning tree in a ... more The capacitated minimum spanning tree (CMST) problem is to ÿnd a minimum cost spanning tree in a network where nodes have speciÿed demands, with an additional capacity constraints on the subtrees incident to a given source node s. The capacitated minimum spanning tree problem arises as an important subproblem in many telecommunication network design problems. In a recent paper, Ahuja et al. (Math. Program. 91 proposed two very large-scale neighborhood search algorithms for the capacitated minimum spanning tree problem. Their ÿrst node-based neighborhood structure is obtained by performing multi-exchanges involving several trees where each tree contributes a single node. Their second tree-based neighborhood structure is obtained by performing multi-exchanges where each tree contributes a subtree. The computational investigations found that node-based multi-exchange neighborhood gives the best performance for the homogenous demand case (when all nodes have the same demand), and the tree-based multi-exchange neighborhood gives the best performance for the heterogeneous demand case (when nodes may have di erent demands). In this paper, we propose a composite neighborhood structure that subsumes both the node-based and tree-based neighborhoods, and outperforms both the previous neighborhood search algorithms for solving the capacitated minimum spanning tree problem on standard benchmark instances. We also develop improved dynamic programming based exact algorithms for searching the composite neighborhood.

SSRN Electronic Journal, 2002

generalizing the swap-based neighborhood search approach of for FAM which proceeds by swapping th... more generalizing the swap-based neighborhood search approach of for FAM which proceeds by swapping the fleet assignment of two flight paths flown by two different plane types that originate and terminate at the same stations and the same times. An important feature of our approach is that the size of our neighborhood is very large; hence the suggested algorithm is in the category of very large-scale neighborhood (VLSN) search algorithms. Another important feature of our approach is that we use integer programming to identify improved neighbors. We provide computational results that indicate that the neighborhood search approach for ctFAM provides substantial savings over the sequential approach of solving FAM and TAM.

Operations Research, 2010

We present a Simplex-type algorithm, that is, an algorithm that moves from one extreme point of t... more We present a Simplex-type algorithm, that is, an algorithm that moves from one extreme point of the infinite-dimensional feasible region to another not necessarily adjacent extreme point, for solving a class of linear programs with countably infinite variables and constraints. Each iteration of this method can be implemented in finite time, while the solution values converge to the optimal value as the number of iterations increases. This Simplex-type algorithm moves to an adjacent extreme point and hence reduces to a true infinite-dimensional Simplex method for the important special cases of non-stationary infinite-horizon deterministic and stochastic dynamic programs. subject to ∞ j=1 a ij x j = b i , i = 1, 2, . . .

Optimal decision making in manufacturing requires integrated consideration of cap- ital investmen... more Optimal decision making in manufacturing requires integrated consideration of cap- ital investment, revenue management, production scheduling and sales planning. Tra- ditionally these decisions are made independently at difierent times and organizational levels, which may lead to sub-optimal results. In this paper, we develop a joint op- timization model that considers these interactions and present a simplifled example involving a single dedicated-production line. We employ a novel game-theoretic al- gorithm called Sampled Fictitious Play (SFP) to solve this example. Although this instance can be solved via traditional optimization approaches such as dynamic pro- gramming, we demonstrate that SFP is exceedingly faster than dynamic programming at flnding near-optimal solutions, flnding a solution within 1% of optimal value in less than one thousandth the computation time.

SSRN Electronic Journal, 2002

The locomotive scheduling problem (or the locomotive assignment problem) is to assign a consist (... more The locomotive scheduling problem (or the locomotive assignment problem) is to assign a consist (a set of locomotives) to each train in a pre-planned train schedule so as to provide them sufficient power to pull them from their origins to their destinations. Locomotive scheduling problems are among the most important problems in railroad scheduling. In this paper, we report the results of a study of the locomotive scheduling problem faced by CSX Transportation, a major US railroad company. We consider the planning version of the locomotive scheduling model (LSM), where there are multiple types of locomotives and we need to decide the set of locomotives to be assigned to each train. We present an integrated model that determines the set of active and deadheaded locomotives for each train, light traveling locomotives from power sources to power sinks, and train-to-train connections (specifying which inbound train and outbound trains can directly connect). An important feature of our model is that we explicitly consider consist-bustings and consistency. A consist is said to be busted when the set of locomotives coming on an inbound train is broken into subsets to be reassigned to two or more outbound trains. A solution is said to be consistent over a week with respect to a train, if the train gets the same locomotive assignment each day it runs. We give a mixed integer programming (MIP) formulation of the problem that contains about 197 thousand integer variables and 67 thousand constraints. An MIP of this size cannot be solved to optimality or near-optimality in acceptable running times using commercially available software. Using problem decomposition, integer programming, and very large-scale neighborhood search, we developed a solution technique to solve this problem within 30 minutes of computation time on a Pentium III computer. When we compared our solution with the solution obtained by the software in-house developed by CSX, we obtained a savings of over 400 locomotives, which translates into savings of over one hundred million dollars annually.

generalizing the swap-based neighborhood search approach of for FAM which proceeds by swapping th... more generalizing the swap-based neighborhood search approach of for FAM which proceeds by swapping the fleet assignment of two flight paths flown by two different plane types that originate and terminate at the same stations and the same times. An important feature of our approach is that the size of our neighborhood is very large; hence the suggested algorithm is in the category of very large-scale neighborhood (VLSN) search algorithms. Another important feature of our approach is that we use integer programming to identify improved neighbors. We provide computational results that indicate that the neighborhood search approach for ctFAM provides substantial savings over the sequential approach of solving FAM and TAM.

Mathematical Programming, 2001

The capacitated minimum spanning tree (CMST) problem is to find a minimum cost spanning tree with... more The capacitated minimum spanning tree (CMST) problem is to find a minimum cost spanning tree with an additional cardinality constraint on the sizes of the subtrees incident to a given root node. The CMST problem is an NP-complete problem, and existing exact algorithms can solve only small size problems. Currently, the best available heuristic procedures for the CMST problem are tabu search algorithms due to Amberg et al. and Sharaiha et al. These algorithms use two-exchange neighborhood structures that are based on exchanging a single node or a set of nodes between two subtrees. In this paper, we generalize their neighborhood structures to allow exchanges of nodes among multiple subtrees simultaneously; we refer to such neighborhood structures as multi-exchange neighborhood structures. Our first multi-exchange neighborhood structure allows exchanges of single nodes among several subtrees. Our second multi-exchange neighborhood structure allows exchanges that involve multiple subtrees. The size of each of these neighborhood structures grows exponentially with the problem size without any substantial increase in the computational times needed to find improved neighbors. Our approach, which is based on the cyclic transfer neighborhood structure due to Thompson and Psaraftis and Thompson and Orlin transforms a profitable exchange into a negative cost subset-disjoint cycle in a graph, called an improvement graph, and identifies these cycles using variants of shortest path label-correcting algorithms. Our computational results with GRASP and tabu search algorithms based on these neighborhood structures reveal that (i) for the unit demand case our algorithms obtained the best available solutions for all benchmark instances and improved some; and (ii) for the heterogeneous demand case our algorithms improved the best available solutions for most of the benchmark instances with improvements by as much as 18%. The running times our multi-exchange neighborhood search algorithms are comparable to those taken by two-exchange neighborhood search algorithms.

We consider neighborhood search defined on combinatorial optimization problems. Suppose that N is... more We consider neighborhood search defined on combinatorial optimization problems. Suppose that N is a neighborhood for combinatorial optimization problem X. We say that N′ is LO-equivalent (locally optimal) to N if for any instance of X, the set of locally optimal solutions with respect to N and N′ are the same. The union of two LO-equivalent neighborhoods is itself LO-equivalent to the neighborhoods. The largest neighborhood that is LO-equivalent to N is called the extended neighborhood of N, and denoted as N * . We analyze some basic properties of the extended neighborhood. We provide a geometric characterization of the extended neighborhood N * when the instances have linear costs defined over a cone. For the TSP, we consider 2-opt * , the extended neighborhood for the 2-opt (i.e., 2-exchange) neighborhood structure. We show that number of neighbors of each tour T in 2-opt * is at least (n/2 -2)!. We show that finding the best tour in the 2-opt * neighborhood is NP-hard. We also show that the extended neighborhood for the graph partition problem is the same as the original neighborhood, regardless of the neighborhood defined. This result extends to the quadratic assignment problem as well. This result on extended neighborhoods relies on a proof that the convex hull of solutions for the graph partition problem has a diameter of 1, that is, every two corner points of this polytope are adjacent.

Journal of Hepatology, 2000

Neighborhood search algorithms are often the most effective approaches available for solving part... more Neighborhood search algorithms are often the most effective approaches available for solving partitioning problems, a difficult class of combinatorial optimization problems arising in many application domains including vehicle routing, telecommunications network design, parallel machine scheduling, location theory, and clustering. A critical issue in the design of a neighborhood search algorithm is the choice of the neighborhood structure, that is, the manner in which the neighborhood is defined. Currently, the two-exchange neighborhood is the most widely used neighborhood for solving partitioning problems. The paper describes the cyclic exchange uneighborhood, which is a generalization of the two-exchange neighborhood in which a neighbor is obtained by performing a cyclic exchange. The cyclic exchange neighborhood has substantially more neighbors compared to the two-exchange neighborhood. This paper outlines a network optimization based methodology to search the neighborhood efficiently and presents a proof of concept by applying it to the capacitated minimum spanning tree problem, an important problem in telecommunications network design.

Networks, 2006

We study capacitated network flow problems with demands defined on a countably infinite collectio... more We study capacitated network flow problems with demands defined on a countably infinite collection of nodes having finite degree. This class of network flow models includes, for example, all infinite horizon deterministic dynamic programs with finite action sets, because these are equivalent to the problem of finding a shortest path in an infinite directed network. We derive necessary and sufficient conditions for flows to be extreme points of the set of feasible flows. Under an additional regularity condition met by all such problems with integer data, we show that a feasible solution is an extreme point if and only if it contains neither a cycle nor a doubly-infinite path consisting of free arcs (an arc is free if its flow is strictly between its upper and lower bounds). We employ this result to show that the extreme points can be characterized by specifying a basis. Moreover, we establish the integrality of extreme point flows whenever node demands and arc capacities are integer valued. We illustrate our results with an application to an infinite horizon economic lot-sizing problem. © 2006 Wiley Periodicals, Inc. NETWORKS, Vol. 48(4), 209–222 2006

SSRN Electronic Journal, 2002

The Quadratic Assignment Problem (QAP) consists of assigning n facilities to n locations so as to... more The Quadratic Assignment Problem (QAP) consists of assigning n facilities to n locations so as to minimize the total weighted cost of interactions between facilities. The QAP arises in many diverse settings, is known to be NP-hard, and can be solved to optimality only for fairly small size instances (typically, n ≤ 25). Neighborhood search algorithms are the most popular heuristic algorithms to solve larger size instances of the QAP. The most extensively used neighborhood structure for the QAP is the 2-exchange neighborhood. This neighborhood is obtained by swapping the locations of two facilities and thus has size O(n 2 ). Previous efforts to explore larger size neighborhoods (such as 3-exchange or 4-exchange neighborhoods) were not very successful, as it took too long to evaluate the larger set of neighbors. In this paper, we propose very largescale neighborhood (VLSN) search algorithms where the size of the neighborhood is very large and we propose a novel search procedure to heuristically enumerate good neighbors. Our search procedure relies on the concept of improvement graph which allows us to evaluate neighbors much faster than the existing methods. We present extensive computational results of our algorithms on standard benchmark instances. These investigations reveal that very large-scale neighborhood search algorithms give consistently better solutions compared the popular 2exchange neighborhood algorithms considering both the solution time and solution accuracy.

Operations Research Letters, 2003

The capacitated minimum spanning tree (CMST) problem is to ÿnd a minimum cost spanning tree in a ... more The capacitated minimum spanning tree (CMST) problem is to ÿnd a minimum cost spanning tree in a network where nodes have speciÿed demands, with an additional capacity constraints on the subtrees incident to a given source node s. The capacitated minimum spanning tree problem arises as an important subproblem in many telecommunication network design problems. In a recent paper, Ahuja et al. (Math. Program. 91 proposed two very large-scale neighborhood search algorithms for the capacitated minimum spanning tree problem. Their ÿrst node-based neighborhood structure is obtained by performing multi-exchanges involving several trees where each tree contributes a single node. Their second tree-based neighborhood structure is obtained by performing multi-exchanges where each tree contributes a subtree. The computational investigations found that node-based multi-exchange neighborhood gives the best performance for the homogenous demand case (when all nodes have the same demand), and the tree-based multi-exchange neighborhood gives the best performance for the heterogeneous demand case (when nodes may have di erent demands). In this paper, we propose a composite neighborhood structure that subsumes both the node-based and tree-based neighborhoods, and outperforms both the previous neighborhood search algorithms for solving the capacitated minimum spanning tree problem on standard benchmark instances. We also develop improved dynamic programming based exact algorithms for searching the composite neighborhood.

Mathematical Programming, 2004

We consider neighborhood search defined on combinatorial optimization problems. Suppose that N is... more We consider neighborhood search defined on combinatorial optimization problems. Suppose that N is a Neighborhood for combinatorial optimization problem X. We say that N ′ is LO-equivalent (locally optimal) to N if for any instance of X, the set of locally optimal solutions with respect to N and N ′ are the same. The union of two LO-equivalent neighborhoods is itself LO-equivalent to the neighborhoods. The largest neighborhood that is LO-equivalent to N is called the extended neighborhood of N, and denoted as N *. We analyze some basic properties of the extended neighborhood. We provide a geometric characterization of the extended neighborhood N * when the instances have linear costs defined over a cone. For the TSP, we consider 2-opt*, the extended neighborhood for the 2-opt (i.e., 2-exchange) neighborhood structure. We show that number of neighbors of each tour T in 2-opt* is at least (n /2 -2)!. We show that finding the best tour in the 2-opt* neighborhood is NP-hard. We also show that the extended neighborhood for the graph partition problem is the same as the original neighborhood, regardless of the neighborhood defined. This result extends to the quadratic assignment problem as well. This result on extended neighborhoods relies on a proof that the convex hull of solutions for the graph partition problem has a diameter of 1, that is, every two corner points of this polytope are adjacent.

The locomotive scheduling problem (or the locomotive assignment problem) is to assign a consist (... more The locomotive scheduling problem (or the locomotive assignment problem) is to assign a consist (a set of locomotives) to each train in a pre-planned train schedule so as to provide them sufficient power to pull them from their origins to their destinations. Locomotive scheduling problems are among the most important problems in railroad scheduling. In this paper, we report the results of a study of the locomotive scheduling problem faced by CSX Transportation, a major US railroad company. We consider the planning version of the locomotive scheduling model (LSM), where there are multiple types of locomotives and we need to decide the set of locomotives to be assigned to each train. We present an integrated model that determines the set of active and deadheaded locomotives for each train, light traveling locomotives from power sources to power sinks, and train-to-train connections (specifying which inbound train and outbound trains can directly connect). An important feature of our model is that we explicitly consider consist-bustings and consistency. A consist is said to be busted when the set of locomotives coming on an inbound train is broken into subsets to be reassigned to two or more outbound trains. A solution is said to be consistent over a week with respect to a train, if the train gets the same locomotive assignment each day it runs. We give a mixed integer programming (MIP) formulation of the problem that contains about 197 thousand integer variables and 67 thousand constraints. An MIP of this size cannot be solved to optimality or near-optimality in acceptable running times using commercially available software. Using problem decomposition, integer programming, and very large-scale neighborhood search, we developed a solution technique to solve this problem within 30 minutes of computation time on a Pentium III computer. When we compared our solution with the solution obtained by the software in-house developed by CSX, we obtained a savings of over 400 locomotives, which translates into savings of over one hundred million dollars annually.

Informs Journal on Computing, 2007

generalizing the swap-based neighborhood search approach of for FAM which proceeds by swapping th... more generalizing the swap-based neighborhood search approach of for FAM which proceeds by swapping the fleet assignment of two flight paths flown by two different plane types that originate and terminate at the same stations and the same times. An important feature of our approach is that the size of our neighborhood is very large; hence the suggested algorithm is in the category of very large-scale neighborhood (VLSN) search algorithms. Another important feature of our approach is that we use integer programming to identify improved neighbors. We provide computational results that indicate that the neighborhood search approach for ctFAM provides substantial savings over the sequential approach of solving FAM and TAM.

Informs Journal on Computing, 2007

The Quadratic Assignment Problem (QAP) consists of assigning n facilities to n locations so as to... more The Quadratic Assignment Problem (QAP) consists of assigning n facilities to n locations so as to minimize the total weighted cost of interactions between facilities. The QAP arises in many diverse settings, is known to be NP-hard, and can be solved to optimality only for fairly small size instances (typically, n ≤ 25). Neighborhood search algorithms are the most popular heuristic algorithms to solve larger size instances of the QAP. The most extensively used neighborhood structure for the QAP is the 2-exchange neighborhood. This neighborhood is obtained by swapping the locations of two facilities and thus has size O(n 2 ). Previous efforts to explore larger size neighborhoods (such as 3-exchange or 4-exchange neighborhoods) were not very successful, as it took too long to evaluate the larger set of neighbors. In this paper, we propose very largescale neighborhood (VLSN) search algorithms where the size of the neighborhood is very large and we propose a novel search procedure to heuristically enumerate good neighbors. Our search procedure relies on the concept of improvement graph which allows us to evaluate neighbors much faster than the existing methods. We present extensive computational results of our algorithms on standard benchmark instances. These investigations reveal that very large-scale neighborhood search algorithms give consistently better solutions compared the popular 2exchange neighborhood algorithms considering both the solution time and solution accuracy.

Transportation Science, 2005

The locomotive scheduling problem (or the locomotive assignment problem) is to assign a consist (... more The locomotive scheduling problem (or the locomotive assignment problem) is to assign a consist (a set of locomotives) to each train in a pre-planned train schedule so as to provide them sufficient power to pull them from their origins to their destinations. Locomotive scheduling problems are among the most important problems in railroad scheduling. In this paper, we report the results of a study of the locomotive scheduling problem faced by CSX Transportation, a major US railroad company. We consider the planning version of the locomotive scheduling model (LSM), where there are multiple types of locomotives and we need to decide the set of locomotives to be assigned to each train. We present an integrated model that determines the set of active and deadheaded locomotives for each train, light traveling locomotives from power sources to power sinks, and train-to-train connections (specifying which inbound train and outbound trains can directly connect). An important feature of our model is that we explicitly consider consist-bustings and consistency. A consist is said to be busted when the set of locomotives coming on an inbound train is broken into subsets to be reassigned to two or more outbound trains. A solution is said to be consistent over a week with respect to a train, if the train gets the same locomotive assignment each day it runs. We give a mixed integer programming (MIP) formulation of the problem that contains about 197 thousand integer variables and 67 thousand constraints. An MIP of this size cannot be solved to optimality or near-optimality in acceptable running times using commercially available software. Using problem decomposition, integer programming, and very large-scale neighborhood search, we developed a solution technique to solve this problem within 30 minutes of computation time on a Pentium III computer. When we compared our solution with the solution obtained by the software in-house developed by CSX, we obtained a savings of over 400 locomotives, which translates into savings of over one hundred million dollars annually.

International Transactions in Operational Research, 2000

Neighborhood search algorithms are often the most effective approaches available for solving part... more Neighborhood search algorithms are often the most effective approaches available for solving partitioning problems, a difficult class of combinatorial optimization problems arising in many application domains including vehicle routing, telecommunications network design, parallel machine scheduling, location theory, and clustering. A critical issue in the design of a neighborhood search algorithm is the choice of the neighborhood structure, that is, the manner in which the neighborhood is defined. Currently, the two-exchange neighborhood is the most widely used neighborhood for solving partitioning problems. The paper describes the cyclic exchange uneighborhood, which is a generalization of the two-exchange neighborhood in which a neighbor is obtained by performing a cyclic exchange. The cyclic exchange neighborhood has substantially more neighbors compared to the two-exchange neighborhood. This paper outlines a network optimization based methodology to search the neighborhood efficiently and presents a proof of concept by applying it to the capacitated minimum spanning tree problem, an important problem in telecommunications network design.

The capacitated minimum spanning tree (CMST) problem is to ÿnd a minimum cost spanning tree in a ... more The capacitated minimum spanning tree (CMST) problem is to ÿnd a minimum cost spanning tree in a network where nodes have speciÿed demands, with an additional capacity constraints on the subtrees incident to a given source node s. The capacitated minimum spanning tree problem arises as an important subproblem in many telecommunication network design problems. In a recent paper, Ahuja et al. (Math. Program. 91 proposed two very large-scale neighborhood search algorithms for the capacitated minimum spanning tree problem. Their ÿrst node-based neighborhood structure is obtained by performing multi-exchanges involving several trees where each tree contributes a single node. Their second tree-based neighborhood structure is obtained by performing multi-exchanges where each tree contributes a subtree. The computational investigations found that node-based multi-exchange neighborhood gives the best performance for the homogenous demand case (when all nodes have the same demand), and the tree-based multi-exchange neighborhood gives the best performance for the heterogeneous demand case (when nodes may have di erent demands). In this paper, we propose a composite neighborhood structure that subsumes both the node-based and tree-based neighborhoods, and outperforms both the previous neighborhood search algorithms for solving the capacitated minimum spanning tree problem on standard benchmark instances. We also develop improved dynamic programming based exact algorithms for searching the composite neighborhood.

SSRN Electronic Journal, 2002

generalizing the swap-based neighborhood search approach of for FAM which proceeds by swapping th... more generalizing the swap-based neighborhood search approach of for FAM which proceeds by swapping the fleet assignment of two flight paths flown by two different plane types that originate and terminate at the same stations and the same times. An important feature of our approach is that the size of our neighborhood is very large; hence the suggested algorithm is in the category of very large-scale neighborhood (VLSN) search algorithms. Another important feature of our approach is that we use integer programming to identify improved neighbors. We provide computational results that indicate that the neighborhood search approach for ctFAM provides substantial savings over the sequential approach of solving FAM and TAM.

Operations Research, 2010

We present a Simplex-type algorithm, that is, an algorithm that moves from one extreme point of t... more We present a Simplex-type algorithm, that is, an algorithm that moves from one extreme point of the infinite-dimensional feasible region to another not necessarily adjacent extreme point, for solving a class of linear programs with countably infinite variables and constraints. Each iteration of this method can be implemented in finite time, while the solution values converge to the optimal value as the number of iterations increases. This Simplex-type algorithm moves to an adjacent extreme point and hence reduces to a true infinite-dimensional Simplex method for the important special cases of non-stationary infinite-horizon deterministic and stochastic dynamic programs. subject to ∞ j=1 a ij x j = b i , i = 1, 2, . . .

Optimal decision making in manufacturing requires integrated consideration of cap- ital investmen... more Optimal decision making in manufacturing requires integrated consideration of cap- ital investment, revenue management, production scheduling and sales planning. Tra- ditionally these decisions are made independently at difierent times and organizational levels, which may lead to sub-optimal results. In this paper, we develop a joint op- timization model that considers these interactions and present a simplifled example involving a single dedicated-production line. We employ a novel game-theoretic al- gorithm called Sampled Fictitious Play (SFP) to solve this example. Although this instance can be solved via traditional optimization approaches such as dynamic pro- gramming, we demonstrate that SFP is exceedingly faster than dynamic programming at flnding near-optimal solutions, flnding a solution within 1% of optimal value in less than one thousandth the computation time.

SSRN Electronic Journal, 2002

The locomotive scheduling problem (or the locomotive assignment problem) is to assign a consist (... more The locomotive scheduling problem (or the locomotive assignment problem) is to assign a consist (a set of locomotives) to each train in a pre-planned train schedule so as to provide them sufficient power to pull them from their origins to their destinations. Locomotive scheduling problems are among the most important problems in railroad scheduling. In this paper, we report the results of a study of the locomotive scheduling problem faced by CSX Transportation, a major US railroad company. We consider the planning version of the locomotive scheduling model (LSM), where there are multiple types of locomotives and we need to decide the set of locomotives to be assigned to each train. We present an integrated model that determines the set of active and deadheaded locomotives for each train, light traveling locomotives from power sources to power sinks, and train-to-train connections (specifying which inbound train and outbound trains can directly connect). An important feature of our model is that we explicitly consider consist-bustings and consistency. A consist is said to be busted when the set of locomotives coming on an inbound train is broken into subsets to be reassigned to two or more outbound trains. A solution is said to be consistent over a week with respect to a train, if the train gets the same locomotive assignment each day it runs. We give a mixed integer programming (MIP) formulation of the problem that contains about 197 thousand integer variables and 67 thousand constraints. An MIP of this size cannot be solved to optimality or near-optimality in acceptable running times using commercially available software. Using problem decomposition, integer programming, and very large-scale neighborhood search, we developed a solution technique to solve this problem within 30 minutes of computation time on a Pentium III computer. When we compared our solution with the solution obtained by the software in-house developed by CSX, we obtained a savings of over 400 locomotives, which translates into savings of over one hundred million dollars annually.

generalizing the swap-based neighborhood search approach of for FAM which proceeds by swapping th... more generalizing the swap-based neighborhood search approach of for FAM which proceeds by swapping the fleet assignment of two flight paths flown by two different plane types that originate and terminate at the same stations and the same times. An important feature of our approach is that the size of our neighborhood is very large; hence the suggested algorithm is in the category of very large-scale neighborhood (VLSN) search algorithms. Another important feature of our approach is that we use integer programming to identify improved neighbors. We provide computational results that indicate that the neighborhood search approach for ctFAM provides substantial savings over the sequential approach of solving FAM and TAM.

Mathematical Programming, 2001

The capacitated minimum spanning tree (CMST) problem is to find a minimum cost spanning tree with... more The capacitated minimum spanning tree (CMST) problem is to find a minimum cost spanning tree with an additional cardinality constraint on the sizes of the subtrees incident to a given root node. The CMST problem is an NP-complete problem, and existing exact algorithms can solve only small size problems. Currently, the best available heuristic procedures for the CMST problem are tabu search algorithms due to Amberg et al. and Sharaiha et al. These algorithms use two-exchange neighborhood structures that are based on exchanging a single node or a set of nodes between two subtrees. In this paper, we generalize their neighborhood structures to allow exchanges of nodes among multiple subtrees simultaneously; we refer to such neighborhood structures as multi-exchange neighborhood structures. Our first multi-exchange neighborhood structure allows exchanges of single nodes among several subtrees. Our second multi-exchange neighborhood structure allows exchanges that involve multiple subtrees. The size of each of these neighborhood structures grows exponentially with the problem size without any substantial increase in the computational times needed to find improved neighbors. Our approach, which is based on the cyclic transfer neighborhood structure due to Thompson and Psaraftis and Thompson and Orlin transforms a profitable exchange into a negative cost subset-disjoint cycle in a graph, called an improvement graph, and identifies these cycles using variants of shortest path label-correcting algorithms. Our computational results with GRASP and tabu search algorithms based on these neighborhood structures reveal that (i) for the unit demand case our algorithms obtained the best available solutions for all benchmark instances and improved some; and (ii) for the heterogeneous demand case our algorithms improved the best available solutions for most of the benchmark instances with improvements by as much as 18%. The running times our multi-exchange neighborhood search algorithms are comparable to those taken by two-exchange neighborhood search algorithms.

We consider neighborhood search defined on combinatorial optimization problems. Suppose that N is... more We consider neighborhood search defined on combinatorial optimization problems. Suppose that N is a neighborhood for combinatorial optimization problem X. We say that N′ is LO-equivalent (locally optimal) to N if for any instance of X, the set of locally optimal solutions with respect to N and N′ are the same. The union of two LO-equivalent neighborhoods is itself LO-equivalent to the neighborhoods. The largest neighborhood that is LO-equivalent to N is called the extended neighborhood of N, and denoted as N * . We analyze some basic properties of the extended neighborhood. We provide a geometric characterization of the extended neighborhood N * when the instances have linear costs defined over a cone. For the TSP, we consider 2-opt * , the extended neighborhood for the 2-opt (i.e., 2-exchange) neighborhood structure. We show that number of neighbors of each tour T in 2-opt * is at least (n/2 -2)!. We show that finding the best tour in the 2-opt * neighborhood is NP-hard. We also show that the extended neighborhood for the graph partition problem is the same as the original neighborhood, regardless of the neighborhood defined. This result extends to the quadratic assignment problem as well. This result on extended neighborhoods relies on a proof that the convex hull of solutions for the graph partition problem has a diameter of 1, that is, every two corner points of this polytope are adjacent.

Journal of Hepatology, 2000

Neighborhood search algorithms are often the most effective approaches available for solving part... more Neighborhood search algorithms are often the most effective approaches available for solving partitioning problems, a difficult class of combinatorial optimization problems arising in many application domains including vehicle routing, telecommunications network design, parallel machine scheduling, location theory, and clustering. A critical issue in the design of a neighborhood search algorithm is the choice of the neighborhood structure, that is, the manner in which the neighborhood is defined. Currently, the two-exchange neighborhood is the most widely used neighborhood for solving partitioning problems. The paper describes the cyclic exchange uneighborhood, which is a generalization of the two-exchange neighborhood in which a neighbor is obtained by performing a cyclic exchange. The cyclic exchange neighborhood has substantially more neighbors compared to the two-exchange neighborhood. This paper outlines a network optimization based methodology to search the neighborhood efficiently and presents a proof of concept by applying it to the capacitated minimum spanning tree problem, an important problem in telecommunications network design.

Networks, 2006

We study capacitated network flow problems with demands defined on a countably infinite collectio... more We study capacitated network flow problems with demands defined on a countably infinite collection of nodes having finite degree. This class of network flow models includes, for example, all infinite horizon deterministic dynamic programs with finite action sets, because these are equivalent to the problem of finding a shortest path in an infinite directed network. We derive necessary and sufficient conditions for flows to be extreme points of the set of feasible flows. Under an additional regularity condition met by all such problems with integer data, we show that a feasible solution is an extreme point if and only if it contains neither a cycle nor a doubly-infinite path consisting of free arcs (an arc is free if its flow is strictly between its upper and lower bounds). We employ this result to show that the extreme points can be characterized by specifying a basis. Moreover, we establish the integrality of extreme point flows whenever node demands and arc capacities are integer valued. We illustrate our results with an application to an infinite horizon economic lot-sizing problem. © 2006 Wiley Periodicals, Inc. NETWORKS, Vol. 48(4), 209–222 2006

SSRN Electronic Journal, 2002

The Quadratic Assignment Problem (QAP) consists of assigning n facilities to n locations so as to... more The Quadratic Assignment Problem (QAP) consists of assigning n facilities to n locations so as to minimize the total weighted cost of interactions between facilities. The QAP arises in many diverse settings, is known to be NP-hard, and can be solved to optimality only for fairly small size instances (typically, n ≤ 25). Neighborhood search algorithms are the most popular heuristic algorithms to solve larger size instances of the QAP. The most extensively used neighborhood structure for the QAP is the 2-exchange neighborhood. This neighborhood is obtained by swapping the locations of two facilities and thus has size O(n 2 ). Previous efforts to explore larger size neighborhoods (such as 3-exchange or 4-exchange neighborhoods) were not very successful, as it took too long to evaluate the larger set of neighbors. In this paper, we propose very largescale neighborhood (VLSN) search algorithms where the size of the neighborhood is very large and we propose a novel search procedure to heuristically enumerate good neighbors. Our search procedure relies on the concept of improvement graph which allows us to evaluate neighbors much faster than the existing methods. We present extensive computational results of our algorithms on standard benchmark instances. These investigations reveal that very large-scale neighborhood search algorithms give consistently better solutions compared the popular 2exchange neighborhood algorithms considering both the solution time and solution accuracy.

Operations Research Letters, 2003

The capacitated minimum spanning tree (CMST) problem is to ÿnd a minimum cost spanning tree in a ... more The capacitated minimum spanning tree (CMST) problem is to ÿnd a minimum cost spanning tree in a network where nodes have speciÿed demands, with an additional capacity constraints on the subtrees incident to a given source node s. The capacitated minimum spanning tree problem arises as an important subproblem in many telecommunication network design problems. In a recent paper, Ahuja et al. (Math. Program. 91 proposed two very large-scale neighborhood search algorithms for the capacitated minimum spanning tree problem. Their ÿrst node-based neighborhood structure is obtained by performing multi-exchanges involving several trees where each tree contributes a single node. Their second tree-based neighborhood structure is obtained by performing multi-exchanges where each tree contributes a subtree. The computational investigations found that node-based multi-exchange neighborhood gives the best performance for the homogenous demand case (when all nodes have the same demand), and the tree-based multi-exchange neighborhood gives the best performance for the heterogeneous demand case (when nodes may have di erent demands). In this paper, we propose a composite neighborhood structure that subsumes both the node-based and tree-based neighborhoods, and outperforms both the previous neighborhood search algorithms for solving the capacitated minimum spanning tree problem on standard benchmark instances. We also develop improved dynamic programming based exact algorithms for searching the composite neighborhood.

Mathematical Programming, 2004

We consider neighborhood search defined on combinatorial optimization problems. Suppose that N is... more We consider neighborhood search defined on combinatorial optimization problems. Suppose that N is a Neighborhood for combinatorial optimization problem X. We say that N ′ is LO-equivalent (locally optimal) to N if for any instance of X, the set of locally optimal solutions with respect to N and N ′ are the same. The union of two LO-equivalent neighborhoods is itself LO-equivalent to the neighborhoods. The largest neighborhood that is LO-equivalent to N is called the extended neighborhood of N, and denoted as N *. We analyze some basic properties of the extended neighborhood. We provide a geometric characterization of the extended neighborhood N * when the instances have linear costs defined over a cone. For the TSP, we consider 2-opt*, the extended neighborhood for the 2-opt (i.e., 2-exchange) neighborhood structure. We show that number of neighbors of each tour T in 2-opt* is at least (n /2 -2)!. We show that finding the best tour in the 2-opt* neighborhood is NP-hard. We also show that the extended neighborhood for the graph partition problem is the same as the original neighborhood, regardless of the neighborhood defined. This result extends to the quadratic assignment problem as well. This result on extended neighborhoods relies on a proof that the convex hull of solutions for the graph partition problem has a diameter of 1, that is, every two corner points of this polytope are adjacent.

The locomotive scheduling problem (or the locomotive assignment problem) is to assign a consist (... more The locomotive scheduling problem (or the locomotive assignment problem) is to assign a consist (a set of locomotives) to each train in a pre-planned train schedule so as to provide them sufficient power to pull them from their origins to their destinations. Locomotive scheduling problems are among the most important problems in railroad scheduling. In this paper, we report the results of a study of the locomotive scheduling problem faced by CSX Transportation, a major US railroad company. We consider the planning version of the locomotive scheduling model (LSM), where there are multiple types of locomotives and we need to decide the set of locomotives to be assigned to each train. We present an integrated model that determines the set of active and deadheaded locomotives for each train, light traveling locomotives from power sources to power sinks, and train-to-train connections (specifying which inbound train and outbound trains can directly connect). An important feature of our model is that we explicitly consider consist-bustings and consistency. A consist is said to be busted when the set of locomotives coming on an inbound train is broken into subsets to be reassigned to two or more outbound trains. A solution is said to be consistent over a week with respect to a train, if the train gets the same locomotive assignment each day it runs. We give a mixed integer programming (MIP) formulation of the problem that contains about 197 thousand integer variables and 67 thousand constraints. An MIP of this size cannot be solved to optimality or near-optimality in acceptable running times using commercially available software. Using problem decomposition, integer programming, and very large-scale neighborhood search, we developed a solution technique to solve this problem within 30 minutes of computation time on a Pentium III computer. When we compared our solution with the solution obtained by the software in-house developed by CSX, we obtained a savings of over 400 locomotives, which translates into savings of over one hundred million dollars annually.

Informs Journal on Computing, 2007

generalizing the swap-based neighborhood search approach of for FAM which proceeds by swapping th... more generalizing the swap-based neighborhood search approach of for FAM which proceeds by swapping the fleet assignment of two flight paths flown by two different plane types that originate and terminate at the same stations and the same times. An important feature of our approach is that the size of our neighborhood is very large; hence the suggested algorithm is in the category of very large-scale neighborhood (VLSN) search algorithms. Another important feature of our approach is that we use integer programming to identify improved neighbors. We provide computational results that indicate that the neighborhood search approach for ctFAM provides substantial savings over the sequential approach of solving FAM and TAM.

Informs Journal on Computing, 2007

The Quadratic Assignment Problem (QAP) consists of assigning n facilities to n locations so as to... more The Quadratic Assignment Problem (QAP) consists of assigning n facilities to n locations so as to minimize the total weighted cost of interactions between facilities. The QAP arises in many diverse settings, is known to be NP-hard, and can be solved to optimality only for fairly small size instances (typically, n ≤ 25). Neighborhood search algorithms are the most popular heuristic algorithms to solve larger size instances of the QAP. The most extensively used neighborhood structure for the QAP is the 2-exchange neighborhood. This neighborhood is obtained by swapping the locations of two facilities and thus has size O(n 2 ). Previous efforts to explore larger size neighborhoods (such as 3-exchange or 4-exchange neighborhoods) were not very successful, as it took too long to evaluate the larger set of neighbors. In this paper, we propose very largescale neighborhood (VLSN) search algorithms where the size of the neighborhood is very large and we propose a novel search procedure to heuristically enumerate good neighbors. Our search procedure relies on the concept of improvement graph which allows us to evaluate neighbors much faster than the existing methods. We present extensive computational results of our algorithms on standard benchmark instances. These investigations reveal that very large-scale neighborhood search algorithms give consistently better solutions compared the popular 2exchange neighborhood algorithms considering both the solution time and solution accuracy.

Transportation Science, 2005

The locomotive scheduling problem (or the locomotive assignment problem) is to assign a consist (... more The locomotive scheduling problem (or the locomotive assignment problem) is to assign a consist (a set of locomotives) to each train in a pre-planned train schedule so as to provide them sufficient power to pull them from their origins to their destinations. Locomotive scheduling problems are among the most important problems in railroad scheduling. In this paper, we report the results of a study of the locomotive scheduling problem faced by CSX Transportation, a major US railroad company. We consider the planning version of the locomotive scheduling model (LSM), where there are multiple types of locomotives and we need to decide the set of locomotives to be assigned to each train. We present an integrated model that determines the set of active and deadheaded locomotives for each train, light traveling locomotives from power sources to power sinks, and train-to-train connections (specifying which inbound train and outbound trains can directly connect). An important feature of our model is that we explicitly consider consist-bustings and consistency. A consist is said to be busted when the set of locomotives coming on an inbound train is broken into subsets to be reassigned to two or more outbound trains. A solution is said to be consistent over a week with respect to a train, if the train gets the same locomotive assignment each day it runs. We give a mixed integer programming (MIP) formulation of the problem that contains about 197 thousand integer variables and 67 thousand constraints. An MIP of this size cannot be solved to optimality or near-optimality in acceptable running times using commercially available software. Using problem decomposition, integer programming, and very large-scale neighborhood search, we developed a solution technique to solve this problem within 30 minutes of computation time on a Pentium III computer. When we compared our solution with the solution obtained by the software in-house developed by CSX, we obtained a savings of over 400 locomotives, which translates into savings of over one hundred million dollars annually.

International Transactions in Operational Research, 2000

Neighborhood search algorithms are often the most effective approaches available for solving part... more Neighborhood search algorithms are often the most effective approaches available for solving partitioning problems, a difficult class of combinatorial optimization problems arising in many application domains including vehicle routing, telecommunications network design, parallel machine scheduling, location theory, and clustering. A critical issue in the design of a neighborhood search algorithm is the choice of the neighborhood structure, that is, the manner in which the neighborhood is defined. Currently, the two-exchange neighborhood is the most widely used neighborhood for solving partitioning problems. The paper describes the cyclic exchange uneighborhood, which is a generalization of the two-exchange neighborhood in which a neighbor is obtained by performing a cyclic exchange. The cyclic exchange neighborhood has substantially more neighbors compared to the two-exchange neighborhood. This paper outlines a network optimization based methodology to search the neighborhood efficiently and presents a proof of concept by applying it to the capacitated minimum spanning tree problem, an important problem in telecommunications network design.